http://2013.igem.org/wiki/index.php?title=Special:Contributions/Oran&feed=atom&limit=50&target=Oran&year=&month=2013.igem.org - User contributions [en]2024-03-28T23:00:05ZFrom 2013.igem.orgMediaWiki 1.16.5http://2013.igem.org/Team:UCL/Practice/NeuroethicsTeam:UCL/Practice/Neuroethics2013-10-05T03:53:51Z<p>Oran: </p>
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<br />
<p class="major_title">THE NEUROETHICS REPORT</p><br />
<p class="minor_title">Why Look At Neuroethics?</p><br />
<p class="body_text"><br />
Our <a href="https://2013.igem.org/Team:UCL/Project" target="_blank"> project</a> deals with an idea which may seem, on the face of it, frightening to some; the insertion of modified brain cells, <a href="https://2013.igem.org/Team:UCL/Background/Microglia" target="_blank"> microglia</a>, to try and alleviate <a href="https://2013.igem.org/Team:UCL/Background/Alzheimers" target="_blank"> Alzheimer's disease (AD)</a>. Although more similar to a macrophage than a neuron, engineering microglial cells represents both a scientific and an ethical challenge, not least because it seems like the stuff of <a href="https://2013.igem.org/Team:UCL/Practice/Creative" target="_blank"> zombie B-movies</a>. After all, using microglia to halt the progression of AD, and therefore cognitive loss, by dissolving senile plaques is only one philosophical step (albeit very many scientific steps) from a genetic system for cognitive gain, so the implications of our project stretch past medical bioethics. In the interests of assessing the feasibility of the project in <a href="http://www.sciencedirect.com/science/article/pii/S1364661304002955" target="_blank"> social terms</a>, we are producing this report dealing with the attitudes and <a href=http://www.nature.com/neuro/journal/v5/n11/full/nn1102-1123.html" target="_blank">neuroethics</a> of the potential use of neuro-genetic engineering in medicine, therapy and enhancement technology, as well as expounding a little on some of the scientific concepts behind various approaches.<br />
</p><br />
<br />
<br />
<p class="minor_title">The Essay</p><br />
<p class="body_text"><br />
In a comprehensive report, team member <a href="https://2013.igem.org/Team:UCL/Team/Profile" target="_blank">Alexander Bates</a> takes a look at the medical ethics, the neuroethics and both the plausible and <a href="http://www.sciencedirect.com/science/article/pii/S0306987708002673" target="_blank"> fanciful</a> neuroscientific applications of synthetic biology: <p class="body_text"><b><a href="https://static.igem.org/mediawiki/2013/7/7c/Neuroethics_Report.pdf" target="_blank">Neuro-Genethics Report.PDF</a></p><br />
</p><br />
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<div class="gap"></div><br />
<br />
<p class="minor_title">Read On Our Site</p><br />
<br />
<p class="body_text"><b> <a href="https://2013.igem.org/Team:UCL/Practice/Essay1" target="_blank">Introduction: Medicine and Synthetic Biology</a></p><br />
<br />
<div class="gap"></div><br />
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<p class="body_text"><b> <a href="https://2013.igem.org/Team:UCL/Practice/Essay2" target="_blank">Medical Neuro-Genetic Engineering</a></p><br />
<br />
<div class="gap"></div><br />
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<p class="body_text"><b> <a href="https://2013.igem.org/Team:UCL/Practice/Essay3" target="_blank">Therapeutic Neuro-Genetic Engineering</a></p><br />
<br />
<div class="gap"></div><br />
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<p class="body_text"><b> <a href="https://2013.igem.org/Team:UCL/Practice/Essay4" target="_blank">Enhancement Neuro-Genetic Engineering</a></p><br />
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<div class="gap"></div><br />
<br />
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<p class="body_text"><b> <a href="https://2013.igem.org/Team:UCL/Practice/Essay5" target="_blank">The Core of the Neuroethical Debate</a></p><br />
<br />
<div class="gap"></div><br />
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<p class="body_text"><b><a href="https://2013.igem.org/Team:UCL/Practice/Essay6" target="_blank">Conclusion</a></p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text"><b><a href="https://2013.igem.org/Team:UCL/Practice/Essay7" target="_blank">Bibliography</a></p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="minor_title">Team member's opinions on Neuroethics</p><br />
<br />
<p class="body_text">Alex Bates </p><br />
<p class="body_text">Our project is, as yet, highly theoretical, but it's implications lead us to one of the most fundamental questions in life: what is it to be human? Only once in our history has the human existence been radically redefined - at the origin on mankind, the transition from animals to intelligent, self-conscious beings. We are, perhaps, moving towards the frontier of another transition - the ability to induce dramatic changes in our consciousness at will. The question, "Should we genetically engineer the brain?" essentially asks, do we want to, or even have the right to, fundamentally redefine our existence for only the second time in our history. </p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text">Ruxi Comisel </p><br />
<p class="body_text">I agree with the use of genetic engineering as part of a therapy provided that the only point of using it on the brain or in other parts of the human body is to alleviate the disastrous effect of disease on human integrity.<br />
I believe that the public should not reject this therapy as long as it is an available alternative and it can be used safely and under strict legal regulation, so that only the patients in advanced/terminal stages of suffering can benefit from it. <br />
On the other hand, I strongly oppose using genetic engineering in the context of patients who can benefit from other means of therapy known to be successful for the stages of disease they are at.</p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text">Tom Johnson</p><br />
<p class="body_text">Genetic Engineering has been around for a while, but it has typically been associated with crops rather than people. If GM crops are questioned by the public then surely we need to look long and hard at how we will influence sentient beings. Unfair advantages could be had for the rich - people could effectively buy intelligence etc. which could divide the rich - poor barrier even further. <br />
</p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text">Andy Cheng</p><br />
<p class="body_text">I personally believe genetic engineering is an amazing tool to program biological systems to perform tasks. However, the introduction of genetically engineered cells appear somewhat disturbing. We have to be able to prove these foreign cells would not interfere with integrity of the mind. <br />
</p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text"><b>Oran Maguire</b></p><br />
<p class="body_text">My feelings about Synthetic Biology as a whole are quite confused. There are a huge number of potential applicaions which are capable of impacting on every part of our lives. These could come off very well or very badly for us. I think that the objections which are grounded in the importance of unaltered life and identity do not convince me. What does make me cautious about this technlogy is the potential for environmental hazards, and its potential to be socioeconomically divisive. Who knows how that will pan out. Right now, I get the impression that the way these projects are frequently presented, largely by young and the technically gifted students, will seem rather hubristic to many people looking in from the outside. Anyone aged 50 or under has every reason to take these extraordinary developments rather gravely, so to call projects such as these "cool" will ultimately strike a bad chord, and it will set people's opinions about Synthetic Biology prematurely.<br />
</p><br />
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</html></div>Oranhttp://2013.igem.org/Team:UCL/Practice/NeuroethicsTeam:UCL/Practice/Neuroethics2013-10-05T03:52:33Z<p>Oran: </p>
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<br />
<p class="major_title">THE NEUROETHICS REPORT</p><br />
<p class="minor_title">Why Look At Neuroethics?</p><br />
<p class="body_text"><br />
Our <a href="https://2013.igem.org/Team:UCL/Project" target="_blank"> project</a> deals with an idea which may seem, on the face of it, frightening to some; the insertion of modified brain cells, <a href="https://2013.igem.org/Team:UCL/Background/Microglia" target="_blank"> microglia</a>, to try and alleviate <a href="https://2013.igem.org/Team:UCL/Background/Alzheimers" target="_blank"> Alzheimer's disease (AD)</a>. Although more similar to a macrophage than a neuron, engineering microglial cells represents both a scientific and an ethical challenge, not least because it seems like the stuff of <a href="https://2013.igem.org/Team:UCL/Practice/Creative" target="_blank"> zombie B-movies</a>. After all, using microglia to halt the progression of AD, and therefore cognitive loss, by dissolving senile plaques is only one philosophical step (albeit very many scientific steps) from a genetic system for cognitive gain, so the implications of our project stretch past medical bioethics. In the interests of assessing the feasibility of the project in <a href="http://www.sciencedirect.com/science/article/pii/S1364661304002955" target="_blank"> social terms</a>, we are producing this report dealing with the attitudes and <a href=http://www.nature.com/neuro/journal/v5/n11/full/nn1102-1123.html" target="_blank">neuroethics</a> of the potential use of neuro-genetic engineering in medicine, therapy and enhancement technology, as well as expounding a little on some of the scientific concepts behind various approaches.<br />
</p><br />
<br />
<br />
<p class="minor_title">The Essay</p><br />
<p class="body_text"><br />
In a comprehensive report, team member <a href="https://2013.igem.org/Team:UCL/Team/Profile" target="_blank">Alexander Bates</a> takes a look at the medical ethics, the neuroethics and both the plausible and <a href="http://www.sciencedirect.com/science/article/pii/S0306987708002673" target="_blank"> fanciful</a> neuroscientific applications of synthetic biology: <p class="body_text"><b><a href="https://static.igem.org/mediawiki/2013/7/7c/Neuroethics_Report.pdf" target="_blank">Neuro-Genethics Report.PDF</a></p><br />
</p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="minor_title">Read On Our Site</p><br />
<br />
<p class="body_text"><b> <a href="https://2013.igem.org/Team:UCL/Practice/Essay1" target="_blank">Introduction: Medicine and Synthetic Biology</a></p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text"><b> <a href="https://2013.igem.org/Team:UCL/Practice/Essay2" target="_blank">Medical Neuro-Genetic Engineering</a></p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text"><b> <a href="https://2013.igem.org/Team:UCL/Practice/Essay3" target="_blank">Therapeutic Neuro-Genetic Engineering</a></p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text"><b> <a href="https://2013.igem.org/Team:UCL/Practice/Essay4" target="_blank">Enhancement Neuro-Genetic Engineering</a></p><br />
<br />
<div class="gap"></div><br />
<br />
<br />
<p class="body_text"><b> <a href="https://2013.igem.org/Team:UCL/Practice/Essay5" target="_blank">The Core of the Neuroethical Debate</a></p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text"><b><a href="https://2013.igem.org/Team:UCL/Practice/Essay6" target="_blank">Conclusion</a></p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text"><b><a href="https://2013.igem.org/Team:UCL/Practice/Essay7" target="_blank">Bibliography</a></p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="minor_title">Team member's opinions on Neuroethics</p><br />
<br />
<p class="body_text">Alex Bates </p><br />
<p class="body_text">Our project is, as yet, highly theoretical, but it's implications lead us to one of the most fundamental questions in life: what is it to be human? Only once in our history has the human existence been radically redefined - at the origin on mankind, the transition from animals to intelligent, self-conscious beings. We are, perhaps, moving towards the frontier of another transition - the ability to induce dramatic changes in our consciousness at will. The question, "Should we genetically engineer the brain?" essentially asks, do we want to, or even have the right to, fundamentally redefine our existence for only the second time in our history. </p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text">Ruxi Comisel </p><br />
<p class="body_text">I agree with the use of genetic engineering as part of a therapy provided that the only point of using it on the brain or in other parts of the human body is to alleviate the disastrous effect of disease on human integrity.<br />
I believe that the public should not reject this therapy as long as it is an available alternative and it can be used safely and under strict legal regulation, so that only the patients in advanced/terminal stages of suffering can benefit from it. <br />
On the other hand, I strongly oppose using genetic engineering in the context of patients who can benefit from other means of therapy known to be successful for the stages of disease they are at.</p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text">Tom Johnson</p><br />
<p class="body_text">Genetic Engineering has been around for a while, but it has typically been associated with crops rather than people. If GM crops are questioned by the public then surely we need to look long and hard at how we will influence sentient beings. Unfair advantages could be had for the rich - people could effectively buy intelligence etc. which could divide the rich - poor barrier even further. <br />
</p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text">Andy Cheng</p><br />
<p class="body_text">I personally believe genetic engineering is an amazing tool to program biological systems to perform tasks. However, the introduction of genetically engineered cells appear somewhat disturbing. We have to be able to prove these foreign cells would not interfere with integrity of the mind. <br />
</p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text">Oran Maguire</p><br />
<p class="body_text">My feelings about Synthetic Biology as a whole are quite confused. There are a huge number of potential applicaions which are capable of impacting on every part of our lives. These could come off very well or very badly for us. I think that the objections which are grounded in the importance of unaltered life and identity do not convince me. What does make me cautious about this technlogy is the potential for environmental hazards, and its potential to be socioeconomically divisive. Who knows how that will pan out. Right now, I get the impression that the way these projects are frequently presented, largely by young and the technically gifted students, will seem rather hubristic to many people looking in from the outside. Anyone aged 50 or under has every reason to take these extraordinary developments rather gravely, so to call projects such as these "cool" will ultimately strike a bad chord, and it will set people's opinions about Synthetic Biology prematurely.<br />
</p><br />
<br />
<div class="gap"></div><br />
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</html></div>Oranhttp://2013.igem.org/Team:UCL/Practice/NeuroethicsTeam:UCL/Practice/Neuroethics2013-10-05T03:48:21Z<p>Oran: </p>
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<br />
<p class="major_title">THE NEUROETHICS REPORT</p><br />
<p class="minor_title">Why Look At Neuroethics?</p><br />
<p class="body_text"><br />
Our <a href="https://2013.igem.org/Team:UCL/Project" target="_blank"> project</a> deals with an idea which may seem, on the face of it, frightening to some; the insertion of modified brain cells, <a href="https://2013.igem.org/Team:UCL/Background/Microglia" target="_blank"> microglia</a>, to try and alleviate <a href="https://2013.igem.org/Team:UCL/Background/Alzheimers" target="_blank"> Alzheimer's disease (AD)</a>. Although more similar to a macrophage than a neuron, engineering microglial cells represents both a scientific and an ethical challenge, not least because it seems like the stuff of <a href="https://2013.igem.org/Team:UCL/Practice/Creative" target="_blank"> zombie B-movies</a>. After all, using microglia to halt the progression of AD, and therefore cognitive loss, by dissolving senile plaques is only one philosophical step (albeit very many scientific steps) from a genetic system for cognitive gain, so the implications of our project stretch past medical bioethics. In the interests of assessing the feasibility of the project in <a href="http://www.sciencedirect.com/science/article/pii/S1364661304002955" target="_blank"> social terms</a>, we are producing this report dealing with the attitudes and <a href=http://www.nature.com/neuro/journal/v5/n11/full/nn1102-1123.html" target="_blank">neuroethics</a> of the potential use of neuro-genetic engineering in medicine, therapy and enhancement technology, as well as expounding a little on some of the scientific concepts behind various approaches.<br />
</p><br />
<br />
<br />
<p class="minor_title">The Essay</p><br />
<p class="body_text"><br />
In a comprehensive report, team member <a href="https://2013.igem.org/Team:UCL/Team/Profile" target="_blank">Alexander Bates</a> takes a look at the medical ethics, the neuroethics and both the plausible and <a href="http://www.sciencedirect.com/science/article/pii/S0306987708002673" target="_blank"> fanciful</a> neuroscientific applications of synthetic biology: <p class="body_text"><b><a href="https://static.igem.org/mediawiki/2013/7/7c/Neuroethics_Report.pdf" target="_blank">Neuro-Genethics Report.PDF</a></p><br />
</p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="minor_title">Read On Our Site</p><br />
<br />
<p class="body_text"><b> <a href="https://2013.igem.org/Team:UCL/Practice/Essay1" target="_blank">Introduction: Medicine and Synthetic Biology</a></p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text"><b> <a href="https://2013.igem.org/Team:UCL/Practice/Essay2" target="_blank">Medical Neuro-Genetic Engineering</a></p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text"><b> <a href="https://2013.igem.org/Team:UCL/Practice/Essay3" target="_blank">Therapeutic Neuro-Genetic Engineering</a></p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text"><b> <a href="https://2013.igem.org/Team:UCL/Practice/Essay4" target="_blank">Enhancement Neuro-Genetic Engineering</a></p><br />
<br />
<div class="gap"></div><br />
<br />
<br />
<p class="body_text"><b> <a href="https://2013.igem.org/Team:UCL/Practice/Essay5" target="_blank">The Core of the Neuroethical Debate</a></p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text"><b><a href="https://2013.igem.org/Team:UCL/Practice/Essay6" target="_blank">Conclusion</a></p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text"><b><a href="https://2013.igem.org/Team:UCL/Practice/Essay7" target="_blank">Bibliography</a></p><br />
<br />
<div class="gap"></div><br />
<br />
<p class="minor_title">Team member's opinions on Neuroethics</p><br />
<br />
<p class="body_text">Alex Bates </p><br />
Our project is, as yet, highly theoretical, but it's implications lead us to one of the most fundamental questions in life: what is it to be human? Only once in our history has the human existence been radically redefined - at the origin on mankind, the transition from animals to intelligent, self-conscious beings. We are, perhaps, moving towards the frontier of another transition - the ability to induce dramatic changes in our consciousness at will. The question, "Should we genetically engineer the brain?" essentially asks, do we want to, or even have the right to, fundamentally redefine our existence for only the second time in our history.<br />
<br />
<br />
<br />
<br />
<br />
<!-- END CONTENT ------------------------------------------------------------------------------------------------------><br />
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</html></div>Oranhttp://2013.igem.org/Team:UCL/Modeling/BioinformaticsTeam:UCL/Modeling/Bioinformatics2013-10-05T03:30:27Z<p>Oran: </p>
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<p class="major_title">A BIOINFORMATICS APPROACH</p><br />
<p class="minor_title">Finding New Parts</p><br />
<p class="body_text"><br />
Bioinformatics creates and enhances methods for storing, retrieving, organising and analysing biological data. We decided to take a completely new approach in our dry lab work and look into bioinformatic approaches to studying <a href="https://2013.igem.org/Team:UCL/Background/Alzheimers" target="_blank">Alzheimer’s disease (AD)</a>. <br />
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The rationale behind this is simple. In order to make a genetic circuit in a synthetic biological construct as effective as possible in a medical application, we may need to target key dysfunctional genes within the problematic biological entity. There are many risk factors for AD and so predicting the key, ‘driver genes’, and the group of proteins with which they interact is invaluable in knowing what we want our construct to produce, in order to mitigate AD. The idea is that bioinformatics work can feed back into synthetic biology, and though we did not have the time to demonstrate this full circle, we feel bioinformatics can have a place in iGEM, helping teams to decide which dysfunctional genes to target in medical projects.</p><br />
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<p class="minor_title">Bioinformatics and Alzheimer’s Disease</p> <br />
<p class="body_text"><br />
Recent progress in characterising AD has lead to the identification of dozens of highly interconnected genetic risk factors, yet it is likely that many more remain undiscovered <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044851/" target="_blank">(Soler-Lopez et al. 2011)</a> and the elucidation of their roles in AD could prove pivotal in beating the condition. AD is genetically complex, linked with many defects both mutational or of susceptibility. These defects produce alterations in the molecular interactions of cellular pathways, the collective effect of which may be gauged through the structure of the protein network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>. In other words, there is a strong link between protein connectivity and the disease phenotype. AD arises from the downstream interplay between genetic and non-genetic alterations in the human protein interaction network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>. <br />
</p><p class="body_text"><br />
Recent progress in characterising AD has lead to the identification of dozens of highly interconnected genetic risk factors, yet it is likely that many more remain undiscovered <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044851/" target="_blank">(Soler-Lopez et al. 2011)</a> and the elucidation of their roles in AD could prove pivotal in beating the condition. AD is genetically complex, linked with many defects both mutational or of susceptibility. These defects produce alterations in the molecular interactions of cellular pathways, the collective effect of which may be gauged through the structure of the protein network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>. In other words, there is a strong link between protein connectivity and the disease phenotype. AD arises from the downstream interplay between genetic and non-genetic alterations in the human protein interaction network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>.<br />
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In all pathologies, the most common way to predict driver genes is to target commonly recurrent genes. However, this approach misses misses rare altered genes which comprise the majority of genetic defects leading to, for example, carcinogenesis and arguably AD. This is partly because alterations in a single protein module can lead to the same disease phenotype. Thus, identification may best be attempted on a modular level. Yet it is also important to note correlation events between modules. Simply put, many rare gene alterations that influence the module they belong to and co-altered modules can collectively generate the disease pathology (Gu et al. 2013).<br />
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<p class="minor_title">Our Programme</p> <br />
<p class="body_text"><br />
Under the guidance and tutelage of <a href="http://bmm.cancerresearchuk.org/~cheng03/" target="_blank">Dr Tammy Cheng</a> from the <a href="http://bmm.cancerresearchuk.org/" target="_blank">Biomolecular Modelling (BMM) lab</a> at Cancer Research UK, team member <a href="https://2013.igem.org/Team:UCL/Team/Profile" target="_blank">Alexander Bates</a> coded in python a network analysis programme based on a method devised by Gu et al. and originally applied to the study of glioblastoma (brain cancer). The programme tries to reveal driver genes and co-altered functional modules for AD. The analysis procedure involves mapping altered genes (mutations, amplifications, repressions, etc.) in patient microRNA data to the protein interaction network (PIT), which currently accounts for 48,480 interactions between 10,982 human genes. This is termed the ‘AD altered network’, and is searched with the algorithm suggested by Gu et al. (which has been re-coded from scratch).<br />
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The programme builds up gene sets, two at a time, starting from two seed genes. These sets are termed 'modules'. Pairs of modules (‘G1’ and ‘G2’ in equation) are assumed to be co-altered if any gene within each module is altered in a proportion of AD sufferers, and genes between the modules are often altered together. For two modules, G1 and G2, we must calculate the probability, P, of observing than the number of the samples in the patient gene expression data that by chance simultaneously carry alterations in both gene sets. The gene expression data originates from post-mortem brain samples.<br />
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‘n’ is the total number of patient samples, ‘a’ is the number of patients with alterations in both G1 and G2, ‘b’ is the number of patients with alteration in just G1, ‘c’ is the number of patients with alterations in only G2, and ‘d’ is the number of patients with alterations in neither set. The co-altered score’ S, is defined below. A high score indicates that the two modules tend to be altered together in AD.<br />
</p><p class="body_text"><br />
Fig.1 depicts the searching algorithm. It searches and builds co-altered module pairs for the gene combinations within them that have the greatest co-alteration scores. In step 1, it methodically choose two seed genes from the AD altered network. The ellipsoids in the diagram denote direct interaction partners for these genes. These are added to the seeds to make temporary module pairs. The dashed line represents co-alteration. In step 2, the co-alteration score for each temporary module pair is calculated. Only the pair with the maximal S score is retained for subsequent searching. This maximal group becomes the new seeds group in step 3. In step 4, temporary modules are again derived, this time from step 3, and the maximum score is kept. In step 5, it must determine whether or not this group of genes is going to continue to expand. Each new addition save for the original two starting seeds is removed and S is recalculated. If in one of these configurations S becomes smaller, we loop through steps 3 to 5 again. Otherwise, if all combinations equate to the S value of the gene groups chosen from step 4, the process stops, having assumed that we have reached maximal module size for the two starting seeds.<br />
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In other words, we try to build up gene sets within a module as large was we can, whilst with each new addition increasing the co-alteration score.<br />
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We should end up with modules that frequently exhibit significant co-alteration in AD patients, and their gene products are therefore likely to be biochemically significant in the disease state.<br />
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<p class="minor_title">Results</p> <br />
<p class="body_text"><br />
Originally we planned, as previously suggested, to use the entirety of the human interactome to create an AD interactome and then run our programme in such a way as to build modules from this interactome. However, the estimated run time of the programme over-shot the iGEM 'wiki freeze' deadline. Therefore, we used the expression data for 311 hub genes, whose proteins are points of high connectivity in the human interactome, across 62 modules defined by Zhang et al., and searched for the hub genes combinations that produced the greatest co-alteration scores. The 62 modules are named after colours. <br />
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<b>Module groups: </b> <a href="https://static.igem.org/mediawiki/2013/e/ec/AlzModules.txt" target="_blank">AlzModules.py</a><br />
<p class="body_text"><br />
<b>Hub expression data:</b> <a href="https://static.igem.org/mediawiki/2013/7/7a/ALzData2.txt" target="_blank">AlzData.py</a><br />
</p><br />
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<b>Module matrix:</b> <a href="https://static.igem.org/mediawiki/2013/5/5f/AlzList.txt" target="_blank">AlzMatrix.py</a><br />
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The code for our network analysis programme can be found <a href="https://static.igem.org/mediawiki/2013/4/40/Alex4.txt" target="_blank">here</a>. It needs to be converted to a .py file to be used. Please note that the output is given as a set of numbers that as assigned to genes. For example, the final output for the data we ran can be found <a href="https://static.igem.org/mediawiki/2013/0/0f/AlzFinal.txt" target="_blank">here</a>.<br />
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<th><p class="citation_text">Fig.1 Histogram showing the frequency of gene sets by co-alteration score.</p></th><br />
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We used the output of our programme to produce a histogram, which shows that the frequency of gene combinations falls exponentially with increasing co-alteration score This suggests that a significant few combinations are regularly co-altered in Alzheimer's disease, in modules that may help drive the disease state. Because we are only looking at which hub genes within modules, we are most interested in what modules are co-altered in the high score end of the histogram, and not the hub genes specifically.</p><br />
<p class="body_text"><br />
Below, Fig.2 shows the twenty gene set pairs between two modules, which yielded the greatest co-alteration score. The module pair with the highest score, and that recurs most frequently in the top twenty, are the 'Khaki' and 'Honey Dew' modules. The most enriched functional category of the khaki module is the biosynthesis of a neurotransmitter called GABA. GABA is responsible for neuronal excitability and muscle tone. The Honey Dew module is primarily involved in muscle contraction, though the hub genes AHCYL1 and C9orf61 are thought to be involved in inositol signaling and are possibly associated with another brain condition, bi-polar disorder. However, since the gene expression data is from generally older patients, given the profile of AD, these muscle associated modules may be altered together because of changing muscle usage with age (there is no muscle in the brain but this may represent brain cell structural integrity). Both of these modules have almost 100% of their total brain gene expression in the prefrontal cortex, and area known to be heavily impacted in AD, causing cognitive and intellectual damage. This suggests that our genetic circuit could be adapted to target signaling mechanisms in this area.</p><br />
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<th><p class="citation_text">Fig.2 Table of the top 20 gene combinations and their modules by co-alteration score.</p></th><br />
</table><br />
<table><br />
<tr><br />
<th>Module Name and Gene Set</th><br />
<th>Module Name and Gene Set</th><br />
<th>Co-alteration Score</th><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>20.39 </td><br />
<tr><br />
<td>SLC15A2, FXYD1</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>19.73 </td><br />
<tr><br />
<td>GJA1, FXYD1</td><br />
<td>RFX4, AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>19.37 </td><br />
<tr><br />
<td>GJA1, FXYD1, ATP13A4</td><br />
<td>C20orf141, RFX4, AHCYL1, DGCR6</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>18.99 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RBM4</td><br />
<td>Contig47252_RC, IFITM2, CDK2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>18.81 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RBM4</td><br />
<td>ENST00000289005, Contig47252_RC, IFITM2, CDK2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>17.69 </td><br />
<tr><br />
<td>GJA1, FXYD1, SLC15A2</td><br />
<td>RFX4, AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>17.57 </td><br />
<tr><br />
<td>RRM2, NM_022346, FAM64A</td><br />
<td>OR4F5, GRAP, XM_166973</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Wheat</b></td><br />
<td>17.49 </td><br />
<tr><br />
<td>DYNC2LI1, RBM4</td><br />
<td>AF087999</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>16.95 </td><br />
<tr><br />
<td>HMMR</td><br />
<td>OR4F5, GRAP</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>16.95 </td><br />
<tr><br />
<td>HMMR</td><br />
<td>OR4F5, GRAP, CRYBA2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Wheat</b></td><br />
<td>16.78 </td><br />
<tr><br />
<td>CIRBP, RBM4</td><br />
<td>AF087999</td><br />
</tr><br />
<tr><br />
<td></td><br />
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<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>16.64 </td><br />
<tr><br />
<td>RRM2, NMMR, FAM64A</td><br />
<td>KRTHB4, GRAP, XM_166973</td><br />
</tr><br />
<tr><br />
<td></td><br />
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<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.47 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td><br />
<td>Contig47252_RC, IFITM2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.46 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td><br />
<td>Contig47252_RC, IFITM2, CDK2</td><br />
</tr> <br />
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<td></td><br />
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<td><b>Forestgreen</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.43 </td><br />
<tr><br />
<td>IFITM3, CSDA</td><br />
<td>CSDA</td><br />
</tr><br />
<tr><br />
<td></td><br />
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<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.38 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td><br />
<td>ENST00000289005, Contig47252_RC, IFITM2</td><br />
</tr><br />
<tr><br />
<td></td><br />
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<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.27 </td><br />
<tr><br />
<td>FXYD1, ATP13A4, SLC15A2</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.25 </td><br />
<tr><br />
<td>FXYD1, ATP13A4</td><br />
<td>DGCR6, AHCYL1, C20orf141, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
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<td><b>Gold 2</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.21 </td><br />
<tr><br />
<td>TUBB2B, NM_178525</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.04 </td><br />
<tr><br />
<td>SPON1, FXYD1, SLC15A2</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
</table> <br />
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</p><br />
<p class="minor_title">Analysis and Feedback into Circuit</p><br />
<p class="body_text"><br />
The second highest scoring module pair, and the second most frequent in the top twenty, are 'Turquoise' and 'Cyan'. The former is primarily involved with NAD(P) homeostasis, and so is significant in cells' metabolism, while the genes in the later mainly play a role in vasculature development. This suggests that co-alteration in genes involved within these two modules could impact cell vitality and trophic support and help cause AD. This suggests that our circuit could be improved by being adapted to help maintain general cell health and energy supply in the brain. </p><br />
<p class="body_text"><br />
The third highest scoring module pair, and the third most frequent in the top twenty, are 'Green 4' and 'Yellow 3'. Green 4 is involved in cell cycle regulation, and area that has already been targeted by our circuit, which produces <b>BDNF</b> to help avoid chromosomal division in the neurons of AD patients. Yellow 3 is associated with the peripheral nervous system. Co-alteration here may again be indicative of gene expression changes with age, and its link with Green 4 may suggest that this is to do with a deficiency in cell division, regeneration and growth, but this is not directly related to AD, although hub genes like GRAP do play a role in cytoplasmic signaling in cells including neurons and glia, This suggests that our circuit could be improved by being adapted to help maintain general cell health and energy supply in the brain. </p><br />
<p class="body_text"><br />
Other module pairs that feature in the top twenty include 'Wheat' and 'Turqouise', 'Forestgreen' and 'Cyan' and 'Gold 2' and 'Honey Dew'. Wheat is involved in protein folding and responses to unfolded and mis-folded protein. This is significant because incorrectly formed and folded amyloid is strongly associated with the progression of AD. This is something out circuit already seeks to address, but by targeting elements of the 'Wheat' module and similar modules it could aim to avoid mis-creation in the first place, and the nucleation of other mis-folded proteins. Forestgreen is involved in immune functions, which implicates microglia and the cellular response to inflammation in neurons - factors our circuit already tries to help address by acting to prevent neuroinflammation. Its association with Cyan could imply that negative inflammatory effects may be inked with brain vasculature in AD. Gold 2 is associated with the cytoskeleton and axonal cytoskeletal control.In AD, the formation of plaques and protein tangles disrupts the cytoskeleton and perturb axonal connections, engendering cell death. Our circuit tries to target this already by removing the plaques, but perhaps a future improvement should to be to create an element capable to supporting a healthy cytoskeleton or able to remove cytoskeletal protein tangles. Its association with Honey Dew, however, could point to unusual gene expression in this module being due to the lessened use of muscle in old age.</p><br />
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<p class="major_title">A BIOINFORMATICS APPROACH</p><br />
<p class="minor_title">Finding New Parts</p><br />
<p class="body_text"><br />
Bioinformatics creates and enhances methods for storing, retrieving, organising and analysing biological data. We decided to take a completely new approach in our dry lab work and look into bioinformatic approaches to studying <a href="https://2013.igem.org/Team:UCL/Background/Alzheimers" target="_blank">Alzheimer’s disease (AD)</a>. <br />
</p> <br />
<p class="body_text"><br />
The rationale behind this is simple. In order to make a genetic circuit in a synthetic biological construct as effective as possible in a medical application, we may need to target key dysfunctional genes within the problematic biological entity. There are many risk factors for AD and so predicting the key, ‘driver genes’, and the group of proteins with which they interact is invaluable in knowing what we want our construct to produce, in order to mitigate AD. The idea is that bioinformatics work can feed back into synthetic biology, and though we did not have the time to demonstrate this full circle, we feel bioinformatics can have a place in iGEM, helping teams to decide which dysfunctional genes to target in medical projects.</p><br />
<br />
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<a href="https://static.igem.org/mediawiki/2013/0/03/Human_interactome.jpg" data-lightbox="image-1" title="The Human Interactome"><br />
<img src="https://static.igem.org/mediawiki/2013/0/03/Human_interactome.jpg"><br />
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<p class="body_text"><br />
<p class="minor_title">Bioinformatics and Alzheimer’s Disease</p> <br />
<p class="body_text"><br />
Recent progress in characterising AD has lead to the identification of dozens of highly interconnected genetic risk factors, yet it is likely that many more remain undiscovered <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044851/" target="_blank">(Soler-Lopez et al. 2011)</a> and the elucidation of their roles in AD could prove pivotal in beating the condition. AD is genetically complex, linked with many defects both mutational or of susceptibility. These defects produce alterations in the molecular interactions of cellular pathways, the collective effect of which may be gauged through the structure of the protein network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>. In other words, there is a strong link between protein connectivity and the disease phenotype. AD arises from the downstream interplay between genetic and non-genetic alterations in the human protein interaction network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>. <br />
</p><p class="body_text"><br />
Recent progress in characterising AD has lead to the identification of dozens of highly interconnected genetic risk factors, yet it is likely that many more remain undiscovered <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044851/" target="_blank">(Soler-Lopez et al. 2011)</a> and the elucidation of their roles in AD could prove pivotal in beating the condition. AD is genetically complex, linked with many defects both mutational or of susceptibility. These defects produce alterations in the molecular interactions of cellular pathways, the collective effect of which may be gauged through the structure of the protein network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>. In other words, there is a strong link between protein connectivity and the disease phenotype. AD arises from the downstream interplay between genetic and non-genetic alterations in the human protein interaction network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>.<br />
</p><p class="body_text"><br />
In all pathologies, the most common way to predict driver genes is to target commonly recurrent genes. However, this approach misses misses rare altered genes which comprise the majority of genetic defects leading to, for example, carcinogenesis and arguably AD. This is partly because alterations in a single protein module can lead to the same disease phenotype. Thus, identification may best be attempted on a modular level. Yet it is also important to note correlation events between modules. Simply put, many rare gene alterations that influence the module they belong to and co-altered modules can collectively generate the disease pathology (Gu et al. 2013).<br />
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<p class="minor_title">Our Programme</p> <br />
<p class="body_text"><br />
Under the guidance and tutelage of <a href="http://bmm.cancerresearchuk.org/~cheng03/" target="_blank">Dr Tammy Cheng</a> from the <a href="http://bmm.cancerresearchuk.org/" target="_blank">Biomolecular Modelling (BMM) lab</a> at Cancer Research UK, team member <a href="https://2013.igem.org/Team:UCL/Team/Profile" target="_blank">Alexander Bates</a> coded in python a network analysis programme based on a method devised by Gu et al. and originally applied to the study of glioblastoma (brain cancer). The programme tries to reveal driver genes and co-altered functional modules for AD. The analysis procedure involves mapping altered genes (mutations, amplifications, repressions, etc.) in patient microRNA data to the protein interaction network (PIT), which currently accounts for 48,480 interactions between 10,982 human genes. This is termed the ‘AD altered network’, and is searched with the algorithm suggested by Gu et al. (which has been re-coded from scratch).<br />
</p><br />
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<p class="body_text"><br />
The programme builds up gene sets, two at a time, starting from two seed genes. These sets are termed 'modules'. Pairs of modules (‘G1’ and ‘G2’ in equation) are assumed to be co-altered if any gene within each module is altered in a proportion of AD sufferers, and genes between the modules are often altered together. For two modules, G1 and G2, we must calculate the probability, P, of observing than the number of the samples in the patient gene expression data that by chance simultaneously carry alterations in both gene sets. The gene expression data originates from post-mortem brain samples.<br />
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‘n’ is the total number of patient samples, ‘a’ is the number of patients with alterations in both G1 and G2, ‘b’ is the number of patients with alteration in just G1, ‘c’ is the number of patients with alterations in only G2, and ‘d’ is the number of patients with alterations in neither set. The co-altered score’ S, is defined below. A high score indicates that the two modules tend to be altered together in AD.<br />
</p><p class="body_text"><br />
Fig.1 depicts the searching algorithm. It searches and builds co-altered module pairs for the gene combinations within them that have the greatest co-alteration scores. In step 1, it methodically choose two seed genes from the AD altered network. The ellipsoids in the diagram denote direct interaction partners for these genes. These are added to the seeds to make temporary module pairs. The dashed line represents co-alteration. In step 2, the co-alteration score for each temporary module pair is calculated. Only the pair with the maximal S score is retained for subsequent searching. This maximal group becomes the new seeds group in step 3. In step 4, temporary modules are again derived, this time from step 3, and the maximum score is kept. In step 5, it must determine whether or not this group of genes is going to continue to expand. Each new addition save for the original two starting seeds is removed and S is recalculated. If in one of these configurations S becomes smaller, we loop through steps 3 to 5 again. Otherwise, if all combinations equate to the S value of the gene groups chosen from step 4, the process stops, having assumed that we have reached maximal module size for the two starting seeds.<br />
</p><br />
<p class="body_text"><br />
In other words, we try to build up gene sets within a module as large was we can, whilst with each new addition increasing the co-alteration score.<br />
</p><br />
<p class="body_text"><br />
We should end up with modules that frequently exhibit significant co-alteration in AD patients, and their gene products are therefore likely to be biochemically significant in the disease state.<br />
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<p class="minor_title">Results</p> <br />
<p class="body_text"><br />
Originally we planned, as previously suggested, to use the entirety of the human interactome to create an AD interactome and then run our programme in such a way as to build modules from this interactome. However, the estimated run time of the programme over-shot the iGEM 'wiki freeze' deadline. Therefore, we used the expression data for 311 hub genes, whose proteins are points of high connectivity in the human interactome, across 62 modules defined by Zhang et al., and searched for the hub genes combinations that produced the greatest co-alteration scores. The 62 modules are named after colours. <br />
</p><br />
<p class="body_text"><br />
<b>Module groups: </b> <a href="https://static.igem.org/mediawiki/2013/e/ec/AlzModules.txt" target="_blank">AlzModules.py</a><br />
<p class="body_text"><br />
<b>Hub expression data:</b> <a href="https://static.igem.org/mediawiki/2013/7/7a/ALzData2.txt" target="_blank">AlzData.py</a><br />
</p><br />
<p class="body_text"><br />
<b>Module matrix:</b> <a href="https://static.igem.org/mediawiki/2013/5/5f/AlzList.txt" target="_blank">AlzMatrix.py</a><br />
</p><br />
<p class="body_text"><br />
The code for our network analysis programme can be found <a href="https://static.igem.org/mediawiki/2013/4/40/Alex4.txt" target="_blank">here</a>. It needs to be converted to a .py file to be used. Please note that the output is given as a set of numbers that as assigned to genes. For example, the final output for the data we ran can be found <a href="https://static.igem.org/mediawiki/2013/0/0f/AlzFinal.txt" target="_blank">here</a>.<br />
</p><br />
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<table><br />
<th><p class="citation_text">Fig.1 Histogram showing the frequency of gene sets by co-alteration score.</p></th><br />
</table><br />
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<p class="body_text"><br />
We used the output of our programme to produce a histogram, which shows that the frequency of gene combinations falls exponentially with increasing co-alteration score This suggests that a significant few combinations are regularly co-altered in Alzheimer's disease, in modules that may help drive the disease state. Because we are only looking at which hub genes within modules, we are most interested in what modules are co-altered in the high score end of the histogram, and not the hub genes specifically.</p><br />
<p class="body_text"><br />
Below, Fig.2 shows the twenty gene set pairs between two modules, which yielded the greatest co-alteration score. The module pair with the highest score, and that recurs most frequently in the top twenty, are the 'Khaki' and 'Honey Dew' modules. The most enriched functional category of the khaki module is the biosynthesis of a neurotransmitter called GABA. GABA is responsible for neuronal excitability and muscle tone. The Honey Dew module is primarily involved in muscle contraction, though the hub genes AHCYL1 and C9orf61 are thought to be involved in inositol signaling and are possibly associated with another brain condition, bi-polar disorder. However, since the gene expression data is from generally older patients, given the profile of AD, these muscle associated modules may be altered together because of changing muscle usage with age (there is no muscle in the brain but this may represent brain cell structural integrity). Both of these modules have almost 100% of their total brain gene expression in the prefrontal cortex, and area known to be heavily impacted in AD, causing cognitive and intellectual damage. This suggests that our genetic circuit could be adapted to target signaling mechanisms in this area.</p><br />
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<table><br />
<th><p class="citation_text">Fig.2 Table of the top 20 gene combinations and their modules by co-alteration score.</p></th><br />
</table><br />
<table><br />
<tr><br />
<th>Module Name and Gene Set</th><br />
<th>Module Name and Gene Set</th><br />
<th>Co-alteration Score</th><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>20.39 </td><br />
<tr><br />
<td>SLC15A2, FXYD1</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>19.73 </td><br />
<tr><br />
<td>GJA1, FXYD1</td><br />
<td>RFX4, AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>19.37 </td><br />
<tr><br />
<td>GJA1, FXYD1, ATP13A4</td><br />
<td>C20orf141, RFX4, AHCYL1, DGCR6</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>18.99 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RBM4</td><br />
<td>Contig47252_RC, IFITM2, CDK2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>18.81 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RBM4</td><br />
<td>ENST00000289005, Contig47252_RC, IFITM2, CDK2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>17.69 </td><br />
<tr><br />
<td>GJA1, FXYD1, SLC15A2</td><br />
<td>RFX4, AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>17.57 </td><br />
<tr><br />
<td>RRM2, NM_022346, FAM64A</td><br />
<td>OR4F5, GRAP, XM_166973</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Wheat</b></td><br />
<td>17.49 </td><br />
<tr><br />
<td>DYNC2LI1, RBM4</td><br />
<td>AF087999</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>16.95 </td><br />
<tr><br />
<td>HMMR</td><br />
<td>OR4F5, GRAP</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>16.95 </td><br />
<tr><br />
<td>HMMR</td><br />
<td>OR4F5, GRAP, CRYBA2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Wheat</b></td><br />
<td>16.78 </td><br />
<tr><br />
<td>CIRBP, RBM4</td><br />
<td>AF087999</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>16.64 </td><br />
<tr><br />
<td>RRM2, NMMR, FAM64A</td><br />
<td>KRTHB4, GRAP, XM_166973</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.47 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td><br />
<td>Contig47252_RC, IFITM2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.46 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td><br />
<td>Contig47252_RC, IFITM2, CDK2</td><br />
</tr> <br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Forestgreen</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.43 </td><br />
<tr><br />
<td>IFITM3, CSDA</td><br />
<td>CSDA</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.38 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td><br />
<td>ENST00000289005, Contig47252_RC, IFITM2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.27 </td><br />
<tr><br />
<td>FXYD1, ATP13A4, SLC15A2</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.25 </td><br />
<tr><br />
<td>FXYD1, ATP13A4</td><br />
<td>DGCR6, AHCYL1, C20orf141, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Gold 2</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.21 </td><br />
<tr><br />
<td>TUBB2B, NM_178525</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.04 </td><br />
<tr><br />
<td>SPON1, FXYD1, SLC15A2</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
</table> <br />
<br />
</p><br />
<p class="minor_title">Analysis and Feedback into Circuit</p><br />
<p class="body_text"><br />
The second highest scoring module pair, and the second most frequent in the top twenty, are 'Turquoise' and 'Cyan'. The former is primarily involved with NAD(P) homeostasis, and so is significant in cells' metabolism, while the genes in the later mainly play a role in vasculature development. This suggests that co-alteration in genes involved within these two modules could impact cell vitality and trophic support and help cause AD. This suggests that our circuit could be improved by being adapted to help maintain general cell health and energy supply in the brain. </p><br />
<p class="body_text"><br />
The third highest scoring module pair, and the third most frequent in the top twenty, are 'Green 4' and 'Yellow 3'. Green 4 is involved in cell cycle regulation, and area that has already been targeted by our circuit, which produces <b>BDNF</b> to help avoid chromosomal division in the neurons of AD patients. Yellow 3 is associated with the peripheral nervous system. Co-alteration here may again be indicative of gene expression changes with age, and its link with Green 4 may suggest that this is to do with a deficiency in cell division, regeneration and growth, but this is not directly related to AD, although hub genes like GRAP do play a role in cytoplasmic signaling in cells including neurons and glia, This suggests that our circuit could be improved by being adapted to help maintain general cell health and energy supply in the brain. </p><br />
<p class="body_text"><br />
Other module pairs that feature in the top twenty include 'Wheat' and 'Turqouise', 'Forestgreen' and 'Cyan' and 'Gold 2' and 'Honey Dew'. Wheat is involved in protein folding and responses to unfolded and mis-folded protein. This is significant because incorrectly formed and folded amyloid is strongly associated with the progression of AD. This is something out circuit already seeks to address, but by targeting elements of the 'Wheat' module and similar modules it could aim to avoid mis-creation in the first place, and the nucleation of other mis-folded proteins. Forestgreen is involved in immune functions, which implicates microglia and the cellular response to inflammation in neurons - factors our circuit already tries to help address by acting to prevent neuroinflammation. Its association with Cyan could imply that negative inflammatory effects may be inked with brain vasculature in AD. Gold 2 is associated with the cytoskeleton and axonal cytoskeletal control.In AD, the formation of plaques and protein tangles disrupts the cytoskeleton and perturb axonal connections, engendering cell death. Our circuit tries to target this already by removing the plaques, but perhaps a future improvement should to be to create an element capable to supporting a healthy cytoskeleton or able to remove cytoskeletal protein tangles. Its association with Honey Dew, however, could point to unusual gene expression in this module being due to the lessened use of muscle in old age.</p><br />
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<p class="major_title">A BIOINFORMATICS APPROACH</p><br />
<p class="minor_title">Finding New Parts</p><br />
<p class="body_text"><br />
Bioinformatics creates and enhances methods for storing, retrieving, organising and analysing biological data. We decided to take a completely new approach in our dry lab work and look into bioinformatic approaches to studying <a href="https://2013.igem.org/Team:UCL/Background/Alzheimers" target="_blank">Alzheimer’s disease (AD)</a>. <br />
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<p class="body_text"><br />
The rationale behind this is simple. In order to make a genetic circuit in a synthetic biological construct as effective as possible in a medical application, we may need to target key dysfunctional genes within the problematic biological entity. There are many risk factors for AD and so predicting the key, ‘driver genes’, and the group of proteins with which they interact is invaluable in knowing what we want our construct to produce, in order to mitigate AD. The idea is that bioinformatics work can feed back into synthetic biology, and though we did not have the time to demonstrate this full circle, we feel bioinformatics can have a place in iGEM, helping teams to decide which dysfunctional genes to target in medical projects.</p><br />
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<p class="minor_title">Bioinformatics and Alzheimer’s Disease</p> <br />
<p class="body_text"><br />
Recent progress in characterising AD has lead to the identification of dozens of highly interconnected genetic risk factors, yet it is likely that many more remain undiscovered <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044851/" target="_blank">(Soler-Lopez et al. 2011)</a> and the elucidation of their roles in AD could prove pivotal in beating the condition. AD is genetically complex, linked with many defects both mutational or of susceptibility. These defects produce alterations in the molecular interactions of cellular pathways, the collective effect of which may be gauged through the structure of the protein network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>. In other words, there is a strong link between protein connectivity and the disease phenotype. AD arises from the downstream interplay between genetic and non-genetic alterations in the human protein interaction network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>. <br />
</p><p class="body_text"><br />
Recent progress in characterising AD has lead to the identification of dozens of highly interconnected genetic risk factors, yet it is likely that many more remain undiscovered <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044851/" target="_blank">(Soler-Lopez et al. 2011)</a> and the elucidation of their roles in AD could prove pivotal in beating the condition. AD is genetically complex, linked with many defects both mutational or of susceptibility. These defects produce alterations in the molecular interactions of cellular pathways, the collective effect of which may be gauged through the structure of the protein network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>. In other words, there is a strong link between protein connectivity and the disease phenotype. AD arises from the downstream interplay between genetic and non-genetic alterations in the human protein interaction network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>.<br />
</p><p class="body_text"><br />
In all pathologies, the most common way to predict driver genes is to target commonly recurrent genes. However, this approach misses misses rare altered genes which comprise the majority of genetic defects leading to, for example, carcinogenesis and arguably AD. This is partly because alterations in a single protein module can lead to the same disease phenotype. Thus, identification may best be attempted on a modular level. Yet it is also important to note correlation events between modules. Simply put, many rare gene alterations that influence the module they belong to and co-altered modules can collectively generate the disease pathology (Gu et al. 2013).<br />
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<p class="minor_title">Our Programme</p> <br />
<p class="body_text"><br />
Under the guidance and tutelage of <a href="http://bmm.cancerresearchuk.org/~cheng03/" target="_blank">Dr Tammy Cheng</a> from the <a href="http://bmm.cancerresearchuk.org/" target="_blank">Biomolecular Modelling (BMM) lab</a> at Cancer Research UK, team member <a href="https://2013.igem.org/Team:UCL/Team/Profile" target="_blank">Alexander Bates</a> coded in python a network analysis programme based on a method devised by Gu et al. and originally applied to the study of glioblastoma (brain cancer). The programme tries to reveal driver genes and co-altered functional modules for AD. The analysis procedure involves mapping altered genes (mutations, amplifications, repressions, etc.) in patient microRNA data to the protein interaction network (PIT), which currently accounts for 48,480 interactions between 10,982 human genes. This is termed the ‘AD altered network’, and is searched with the algorithm suggested by Gu et al. (which has been re-coded from scratch).<br />
</p><br />
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<p class="body_text"><br />
The programme builds up gene sets, two at a time, starting from two seed genes. These sets are termed 'modules'. Pairs of modules (‘G1’ and ‘G2’ in equation) are assumed to be co-altered if any gene within each module is altered in a proportion of AD sufferers, and genes between the modules are often altered together. For two modules, G1 and G2, we must calculate the probability, P, of observing than the number of the samples in the patient gene expression data that by chance simultaneously carry alterations in both gene sets. The gene expression data originates from post-mortem brain samples.<br />
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‘n’ is the total number of patient samples, ‘a’ is the number of patients with alterations in both G1 and G2, ‘b’ is the number of patients with alteration in just G1, ‘c’ is the number of patients with alterations in only G2, and ‘d’ is the number of patients with alterations in neither set. The co-altered score’ S, is defined below. A high score indicates that the two modules tend to be altered together in AD.<br />
</p><p class="body_text"><br />
Fig.1 depicts the searching algorithm. It searches and builds co-altered module pairs for the gene combinations within them that have the greatest co-alteration scores. In step 1, it methodically choose two seed genes from the AD altered network. The ellipsoids in the diagram denote direct interaction partners for these genes. These are added to the seeds to make temporary module pairs. The dashed line represents co-alteration. In step 2, the co-alteration score for each temporary module pair is calculated. Only the pair with the maximal S score is retained for subsequent searching. This maximal group becomes the new seeds group in step 3. In step 4, temporary modules are again derived, this time from step 3, and the maximum score is kept. In step 5, it must determine whether or not this group of genes is going to continue to expand. Each new addition save for the original two starting seeds is removed and S is recalculated. If in one of these configurations S becomes smaller, we loop through steps 3 to 5 again. Otherwise, if all combinations equate to the S value of the gene groups chosen from step 4, the process stops, having assumed that we have reached maximal module size for the two starting seeds.<br />
</p><br />
<p class="body_text"><br />
In other words, we try to build up gene sets within a module as large was we can, whilst with each new addition increasing the co-alteration score.<br />
</p><br />
<p class="body_text"><br />
We should end up with modules that frequently exhibit significant co-alteration in AD patients, and their gene products are therefore likely to be biochemically significant in the disease state.<br />
</p><p class="body_text"><br />
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<p class="minor_title"></p> <br />
<p class="minor_title">Results</p> <br />
<p class="body_text"><br />
Originally we planned, as previously suggested, to use the entirety of the human interactome to create an AD interactome and then run our programme in such a way as to build modules from this interactome. However, the estimated run time of the programme over-shot the iGEM 'wiki freeze' deadline. Therefore, we used the expression data for 311 hub genes, whose proteins are points of high connectivity in the human interactome, across 62 modules defined by Zhang et al., and searched for the hub genes combinations that produced the greatest co-alteration scores. The 62 modules are named after colours. <br />
</p><br />
<p class="body_text"><br />
<b>Module groups: </b> <a href="https://static.igem.org/mediawiki/2013/e/ec/AlzModules.txt" target="_blank">AlzModules.py</a><br />
<p class="body_text"><br />
<b>Hub expression data:</b> <a href="https://static.igem.org/mediawiki/2013/7/7a/ALzData2.txt" target="_blank">AlzData.py</a><br />
</p><br />
<p class="body_text"><br />
<b>Module matrix:</b> <a href="https://static.igem.org/mediawiki/2013/5/5f/AlzList.txt" target="_blank">AlzMatrix.py</a><br />
</p><br />
<p class="body_text"><br />
The code for our network analysis programme can be found <a href="https://static.igem.org/mediawiki/2013/4/40/Alex4.txt" target="_blank">here</a>. It needs to be converted to a .py file to be used. Please note that the output is given as a set of numbers that as assigned to genes. For example, the final output for the data we ran can be found <a href="https://static.igem.org/mediawiki/2013/0/0f/AlzFinal.txt" target="_blank">here</a>.<br />
</p><br />
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<table><br />
<th><p class="citation_text">Fig.1 Histogram showing the frequency of gene sets by co-alteration score.</p></th><br />
</table><br />
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<p class="body_text"><br />
We used the output of our programme to produce a histogram, which shows that the frequency of gene combinations falls exponentially with increasing co-alteration score This suggests that a significant few combinations are regularly co-altered in Alzheimer's disease, in modules that may help drive the disease state. Because we are only looking at which hub genes within modules, we are most interested in what modules are co-altered in the high score end of the histogram, and not the hub genes specifically.</p><br />
<p class="body_text"><br />
Below, Fig.2 shows the twenty gene set pairs between two modules, which yielded the greatest co-alteration score. The module pair with the highest score, and that recurs most frequently in the top twenty, are the 'Khaki' and 'Honey Dew' modules. The most enriched functional category of the khaki module is the biosynthesis of a neurotransmitter called GABA. GABA is responsible for neuronal excitability and muscle tone. The Honey Dew module is primarily involved in muscle contraction, though the hub genes AHCYL1 and C9orf61 are thought to be involved in inositol signaling and are possibly associated with another brain condition, bi-polar disorder. However, since the gene expression data is from generally older patients, given the profile of AD, these muscle associated modules may be altered together because of changing muscle usage with age (there is no muscle in the brain but this may represent brain cell structural integrity). Both of these modules have almost 100% of their total brain gene expression in the prefrontal cortex, and area known to be heavily impacted in AD, causing cognitive and intellectual damage. This suggests that our genetic circuit could be adapted to target signaling mechanisms in this area.</p><br />
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<table><br />
<th><p class="citation_text">Fig.2 Table of the top 20 gene combinations and their modules by co-alteration score.</p></th><br />
</table><br />
<table><br />
<tr><br />
<th>Module Name and Gene Set</th><br />
<th>Module Name and Gene Set</th><br />
<th>Co-alteration Score</th><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>20.39 </td><br />
<tr><br />
<td>SLC15A2, FXYD1</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>19.73 </td><br />
<tr><br />
<td>GJA1, FXYD1</td><br />
<td>RFX4, AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>19.37 </td><br />
<tr><br />
<td>GJA1, FXYD1, ATP13A4</td><br />
<td>C20orf141, RFX4, AHCYL1, DGCR6</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>18.99 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RBM4</td><br />
<td>Contig47252_RC, IFITM2, CDK2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>18.81 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RBM4</td><br />
<td>ENST00000289005, Contig47252_RC, IFITM2, CDK2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>17.69 </td><br />
<tr><br />
<td>GJA1, FXYD1, SLC15A2</td><br />
<td>RFX4, AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>17.57 </td><br />
<tr><br />
<td>RRM2, NM_022346, FAM64A</td><br />
<td>OR4F5, GRAP, XM_166973</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Wheat</b></td><br />
<td>17.49 </td><br />
<tr><br />
<td>DYNC2LI1, RBM4</td><br />
<td>AF087999</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>16.95 </td><br />
<tr><br />
<td>HMMR</td><br />
<td>OR4F5, GRAP</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>16.95 </td><br />
<tr><br />
<td>HMMR</td><br />
<td>OR4F5, GRAP, CRYBA2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Wheat</b></td><br />
<td>16.78 </td><br />
<tr><br />
<td>CIRBP, RBM4</td><br />
<td>AF087999</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>16.64 </td><br />
<tr><br />
<td>RRM2, NMMR, FAM64A</td><br />
<td>KRTHB4, GRAP, XM_166973</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.47 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td><br />
<td>Contig47252_RC, IFITM2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.46 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td><br />
<td>Contig47252_RC, IFITM2, CDK2</td><br />
</tr> <br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Forestgreen</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.43 </td><br />
<tr><br />
<td>IFITM3, CSDA</td><br />
<td>CSDA</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.38 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td><br />
<td>ENST00000289005, Contig47252_RC, IFITM2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.27 </td><br />
<tr><br />
<td>FXYD1, ATP13A4, SLC15A2</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.25 </td><br />
<tr><br />
<td>FXYD1, ATP13A4</td><br />
<td>DGCR6, AHCYL1, C20orf141, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Gold 2</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.21 </td><br />
<tr><br />
<td>TUBB2B, NM_178525</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.04 </td><br />
<tr><br />
<td>SPON1, FXYD1, SLC15A2</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
</table> <br />
<br />
</p><br />
<p class="minor_title">Analysis and Feedback into Circuit</p><br />
<p class="body_text"><br />
The second highest scoring module pair, and the second most frequent in the top twenty, are 'Turquoise' and 'Cyan'. The former is primarily involved with NAD(P) homeostasis, and so is significant in cells' metabolism, while the genes in the later mainly play a role in vasculature development. This suggests that co-alteration in genes involved within these two modules could impact cell vitality and trophic support and help cause AD. This suggests that our circuit could be improved by being adapted to help maintain general cell health and energy supply in the brain. </p><br />
<p class="body_text"><br />
The third highest scoring module pair, and the third most frequent in the top twenty, are 'Green 4' and 'Yellow 3'. Green 4 is involved in cell cycle regulation, and area that has already been targeted by our circuit, which produces <b>BDNF</b> to help avoid chromosomal division in the neurons of AD patients. Yellow 3 is associated with the peripheral nervous system. Co-alteration here may again be indicative of gene expression changes with age, and its link with Green 4 may suggest that this is to do with a deficiency in cell division, regeneration and growth, but this is not directly related to AD, although hub genes like GRAP do play a role in cytoplasmic signaling in cells including neurons and glia, This suggests that our circuit could be improved by being adapted to help maintain general cell health and energy supply in the brain. </p><br />
<p class="body_text"><br />
Other module pairs that feature in the top twenty include 'Wheat' and 'Turqouise', 'Forestgreen' and 'Cyan' and 'Gold 2' and 'Honey Dew'. Wheat is involved in protein folding and responses to unfolded and mis-folded protein. This is significant because incorrectly formed and folded amyloid is strongly associated with the progression of AD. This is something out circuit already seeks to address, but by targeting elements of the 'Wheat' module and similar modules it could aim to avoid mis-creation in the first place, and the nucleation of other mis-folded proteins. Forestgreen is involved in immune functions, which implicates microglia and the cellular response to inflammation in neurons - factors our circuit already tries to help address by acting to prevent neuroinflammation. Its association with Cyan could imply that negative inflammatory effects may be inked with brain vasculature in AD. Gold 2 is associated with the cytoskeleton and axonal cytoskeletal control.In AD, the formation of plaques and protein tangles disrupts the cytoskeleton and perturb axonal connections, engendering cell death. Our circuit tries to target this already by removing the plaques, but perhaps a future improvement should to be to create an element capable to supporting a healthy cytoskeleton or able to remove cytoskeletal protein tangles. Its association with Honey Dew, however, could point to unusual gene expression in this module being due to the lessened use of muscle in old age.</p><br />
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<p class="major_title">A BIOINFORMATICS APPROACH</p><br />
<p class="minor_title">Finding New Parts</p><br />
<p class="body_text"><br />
Bioinformatics creates and enhances methods for storing, retrieving, organising and analysing biological data. We decided to take a completely new approach in our dry lab work and look into bioinformatic approaches to studying <a href="https://2013.igem.org/Team:UCL/Background/Alzheimers" target="_blank">Alzheimer’s disease (AD)</a>. <br />
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The rationale behind this is simple. In order to make a genetic circuit in a synthetic biological construct as effective as possible in a medical application, we may need to target key dysfunctional genes within the problematic biological entity. There are many risk factors for AD and so predicting the key, ‘driver genes’, and the group of proteins with which they interact is invaluable in knowing what we want our construct to produce, in order to mitigate AD. The idea is that bioinformatics work can feed back into synthetic biology, and though we did not have the time to demonstrate this full circle, we feel bioinformatics can have a place in iGEM, helping teams to decide which dysfunctional genes to target in medical projects.</p><br />
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<a href="https://static.igem.org/mediawiki/2013/0/03/Human_interactome.jpg" data-lightbox="image-1" title="The Human Interactome"><br />
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<p class="minor_title">Bioinformatics and Alzheimer’s Disease</p> <br />
<p class="body_text"><br />
Recent progress in characterising AD has lead to the identification of dozens of highly interconnected genetic risk factors, yet it is likely that many more remain undiscovered <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044851/" target="_blank">(Soler-Lopez et al. 2011)</a> and the elucidation of their roles in AD could prove pivotal in beating the condition. AD is genetically complex, linked with many defects both mutational or of susceptibility. These defects produce alterations in the molecular interactions of cellular pathways, the collective effect of which may be gauged through the structure of the protein network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>. In other words, there is a strong link between protein connectivity and the disease phenotype. AD arises from the downstream interplay between genetic and non-genetic alterations in the human protein interaction network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>. <br />
</p><p class="body_text"><br />
Recent progress in characterising AD has lead to the identification of dozens of highly interconnected genetic risk factors, yet it is likely that many more remain undiscovered <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044851/" target="_blank">(Soler-Lopez et al. 2011)</a> and the elucidation of their roles in AD could prove pivotal in beating the condition. AD is genetically complex, linked with many defects both mutational or of susceptibility. These defects produce alterations in the molecular interactions of cellular pathways, the collective effect of which may be gauged through the structure of the protein network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>. In other words, there is a strong link between protein connectivity and the disease phenotype. AD arises from the downstream interplay between genetic and non-genetic alterations in the human protein interaction network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>.<br />
</p><p class="body_text"><br />
In all pathologies, the most common way to predict driver genes is to target commonly recurrent genes. However, this approach misses misses rare altered genes which comprise the majority of genetic defects leading to, for example, carcinogenesis and arguably AD. This is partly because alterations in a single protein module can lead to the same disease phenotype. Thus, identification may best be attempted on a modular level. Yet it is also important to note correlation events between modules. Simply put, many rare gene alterations that influence the module they belong to and co-altered modules can collectively generate the disease pathology (Gu et al. 2013).<br />
<div class="gap"><br />
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<p class="minor_title">Our Programme</p> <br />
<p class="body_text"><br />
Under the guidance and tutelage of <a href="http://bmm.cancerresearchuk.org/~cheng03/" target="_blank">Dr Tammy Cheng</a> from the <a href="http://bmm.cancerresearchuk.org/" target="_blank">Biomolecular Modelling (BMM) lab</a> at Cancer Research UK, team member <a href="https://2013.igem.org/Team:UCL/Team/Profile" target="_blank">Alexander Bates</a> coded in python a network analysis programme based on a method devised by Gu et al. and originally applied to the study of glioblastoma (brain cancer). The programme tries to reveal driver genes and co-altered functional modules for AD. The analysis procedure involves mapping altered genes (mutations, amplifications, repressions, etc.) in patient microRNA data to the protein interaction network (PIT), which currently accounts for 48,480 interactions between 10,982 human genes. This is termed the ‘AD altered network’, and is searched with the algorithm suggested by Gu et al. (which has been re-coded from scratch).<br />
</p><br />
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<p class="body_text"><br />
The programme builds up gene sets, two at a time, starting from two seed genes. These sets are termed 'modules'. Pairs of modules (‘G1’ and ‘G2’ in equation) are assumed to be co-altered if any gene within each module is altered in a proportion of AD sufferers, and genes between the modules are often altered together. For two modules, G1 and G2, we must calculate the probability, P, of observing than the number of the samples in the patient gene expression data that by chance simultaneously carry alterations in both gene sets. The gene expression data originates from post-mortem brain samples.<br />
</p><br />
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‘n’ is the total number of patient samples, ‘a’ is the number of patients with alterations in both G1 and G2, ‘b’ is the number of patients with alteration in just G1, ‘c’ is the number of patients with alterations in only G2, and ‘d’ is the number of patients with alterations in neither set. The co-altered score’ S, is defined below. A high score indicates that the two modules tend to be altered together in AD.<br />
</p><p class="body_text"><br />
Fig.1 depicts the searching algorithm. It searches and builds co-altered module pairs for the gene combinations within them that have the greatest co-alteration scores. In step 1, it methodically choose two seed genes from the AD altered network. The ellipsoids in the diagram denote direct interaction partners for these genes. These are added to the seeds to make temporary module pairs. The dashed line represents co-alteration. In step 2, the co-alteration score for each temporary module pair is calculated. Only the pair with the maximal S score is retained for subsequent searching. This maximal group becomes the new seeds group in step 3. In step 4, temporary modules are again derived, this time from step 3, and the maximum score is kept. In step 5, it must determine whether or not this group of genes is going to continue to expand. Each new addition save for the original two starting seeds is removed and S is recalculated. If in one of these configurations S becomes smaller, we loop through steps 3 to 5 again. Otherwise, if all combinations equate to the S value of the gene groups chosen from step 4, the process stops, having assumed that we have reached maximal module size for the two starting seeds.<br />
</p><br />
<p class="body_text"><br />
In other words, we try to build up gene sets within a module as large was we can, whilst with each new addition increasing the co-alteration score.<br />
</p><br />
<p class="body_text"><br />
We should end up with modules that frequently exhibit significant co-alteration in AD patients, and their gene products are therefore likely to be biochemically significant in the disease state.<br />
</p><p class="body_text"><br />
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<p class="minor_title">Results</p> <br />
<p class="body_text"><br />
Originally we planned, as previously suggested, to use the entirety of the human interactome to create an AD interactome and then run our programme in such a way as to build modules from this interactome. However, the estimated run time of the programme over-shot the iGEM 'wiki freeze' deadline. Therefore, we used the expression data for 311 hub genes, whose proteins are points of high connectivity in the human interactome, across 62 modules defined by Zhang et al., and searched for the hub genes combinations that produced the greatest co-alteration scores. The 62 modules are named after colours. <br />
</p><br />
<p class="body_text"><br />
<b>Module groups: </b> <a href="https://static.igem.org/mediawiki/2013/e/ec/AlzModules.txt" target="_blank">AlzModules.py</a><br />
<p class="body_text"><br />
<b>Hub expression data:</b> <a href="https://static.igem.org/mediawiki/2013/7/7a/ALzData2.txt" target="_blank">AlzData.py</a><br />
</p><br />
<p class="body_text"><br />
<b>Module matrix:</b> <a href="https://static.igem.org/mediawiki/2013/5/5f/AlzList.txt" target="_blank">AlzMatrix.py</a><br />
</p><br />
<p class="body_text"><br />
The code for our network analysis programme can be found <a href="https://static.igem.org/mediawiki/2013/4/40/Alex4.txt" target="_blank">here</a>. It needs to be converted to a .py file to be used. Please note that the output is given as a set of numbers that as assigned to genes. For example, the final output for the data we ran can be found <a href="https://static.igem.org/mediawiki/2013/0/0f/AlzFinal.txt" target="_blank">here</a>.<br />
</p><br />
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<table><br />
<th><p class="citation_text">Fig.1 Histogram showing the frequency of gene sets by co-alteration score.</p></th><br />
</table><br />
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<p class="body_text"><br />
We used the output of our programme to produce a histogram, which shows that the frequency of gene combinations falls exponentially with increasing co-alteration score This suggests that a significant few combinations are regularly co-altered in Alzheimer's disease, in modules that may help drive the disease state. Because we are only looking at which hub genes within modules, we are most interested in what modules are co-altered in the high score end of the histogram, and not the hub genes specifically.</p><br />
<p class="body_text"><br />
Below, Fig.2 shows the twenty gene set pairs between two modules, which yielded the greatest co-alteration score. The module pair with the highest score, and that recurs most frequently in the top twenty, are the 'Khaki' and 'Honey Dew' modules. The most enriched functional category of the khaki module is the biosynthesis of a neurotransmitter called GABA. GABA is responsible for neuronal excitability and muscle tone. The Honey Dew module is primarily involved in muscle contraction, though the hub genes AHCYL1 and C9orf61 are thought to be involved in inositol signaling and are possibly associated with another brain condition, bi-polar disorder. However, since the gene expression data is from generally older patients, given the profile of AD, these muscle associated modules may be altered together because of changing muscle usage with age (there is no muscle in the brain but this may represent brain cell structural integrity). Both of these modules have almost 100% of their total brain gene expression in the prefrontal cortex, and area known to be heavily impacted in AD, causing cognitive and intellectual damage. This suggests that our genetic circuit could be adapted to target signaling mechanisms in this area.</p><br />
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<table><br />
<th><p class="citation_text">Fig.2 Table of the top 20 gene combinations and their modules by co-alteration score.</p></th><br />
</table><br />
<table><br />
<tr><br />
<th>Module Name and Gene Set</th><br />
<th>Module Name and Gene Set</th><br />
<th>Co-alteration Score</th><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>20.39 </td><br />
<tr><br />
<td>SLC15A2, FXYD1</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>19.73 </td><br />
<tr><br />
<td>GJA1, FXYD1</td><br />
<td>RFX4, AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>19.37 </td><br />
<tr><br />
<td>GJA1, FXYD1, ATP13A4</td><br />
<td>C20orf141, RFX4, AHCYL1, DGCR6</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>18.99 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RBM4</td><br />
<td>Contig47252_RC, IFITM2, CDK2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>18.81 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RBM4</td><br />
<td>ENST00000289005, Contig47252_RC, IFITM2, CDK2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>17.69 </td><br />
<tr><br />
<td>GJA1, FXYD1, SLC15A2</td><br />
<td>RFX4, AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>17.57 </td><br />
<tr><br />
<td>RRM2, NM_022346, FAM64A</td><br />
<td>OR4F5, GRAP, XM_166973</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Wheat</b></td><br />
<td>17.49 </td><br />
<tr><br />
<td>DYNC2LI1, RBM4</td><br />
<td>AF087999</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>16.95 </td><br />
<tr><br />
<td>HMMR</td><br />
<td>OR4F5, GRAP</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>16.95 </td><br />
<tr><br />
<td>HMMR</td><br />
<td>OR4F5, GRAP, CRYBA2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Wheat</b></td><br />
<td>16.78 </td><br />
<tr><br />
<td>CIRBP, RBM4</td><br />
<td>AF087999</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>16.64 </td><br />
<tr><br />
<td>RRM2, NMMR, FAM64A</td><br />
<td>KRTHB4, GRAP, XM_166973</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.47 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td><br />
<td>Contig47252_RC, IFITM2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.46 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td><br />
<td>Contig47252_RC, IFITM2, CDK2</td><br />
</tr> <br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Forestgreen</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.43 </td><br />
<tr><br />
<td>IFITM3, CSDA</td><br />
<td>CSDA</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.38 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td><br />
<td>ENST00000289005, Contig47252_RC, IFITM2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.27 </td><br />
<tr><br />
<td>FXYD1, ATP13A4, SLC15A2</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.25 </td><br />
<tr><br />
<td>FXYD1, ATP13A4</td><br />
<td>DGCR6, AHCYL1, C20orf141, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Gold 2</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.21 </td><br />
<tr><br />
<td>TUBB2B, NM_178525</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.04 </td><br />
<tr><br />
<td>SPON1, FXYD1, SLC15A2</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
</table> <br />
<br />
</p><br />
<p class="minor_title">Analysis and Feedback into Circuit</p><br />
<p class="body_text"><br />
The second highest scoring module pair, and the second most frequent in the top twenty, are 'Turquoise' and 'Cyan'. The former is primarily involved with NAD(P) homeostasis, and so is significant in cells' metabolism, while the genes in the later mainly play a role in vasculature development. This suggests that co-alteration in genes involved within these two modules could impact cell vitality and trophic support and help cause AD. This suggests that our circuit could be improved by being adapted to help maintain general cell health and energy supply in the brain. </p><br />
<p class="body_text"><br />
The third highest scoring module pair, and the third most frequent in the top twenty, are 'Green 4' and 'Yellow 3'. Green 4 is involved in cell cycle regulation, and area that has already been targeted by our circuit, which produces <b>BDNF</b> to help avoid chromosomal division in the neurons of AD patients. Yellow 3 is associated with the peripheral nervous system. Co-alteration here may again be indicative of gene expression changes with age, and its link with Green 4 may suggest that this is to do with a deficiency in cell division, regeneration and growth, but this is not directly related to AD, although hub genes like GRAP do play a role in cytoplasmic signaling in cells including neurons and glia, This suggests that our circuit could be improved by being adapted to help maintain general cell health and energy supply in the brain. </p><br />
<p class="body_text"><br />
Other module pairs that feature in the top twenty include 'Wheat' and 'Turqouise', 'Forestgreen' and 'Cyan' and 'Gold 2' and 'Honey Dew'. Wheat is involved in protein folding and responses to unfolded and mis-folded protein. This is significant because incorrectly formed and folded amyloid is strongly associated with the progression of AD. This is something out circuit already seeks to address, but by targeting elements of the 'Wheat' module and similar modules it could aim to avoid mis-creation in the first place, and the nucleation of other mis-folded proteins. Forestgreen is involved in immune functions, which implicates microglia and the cellular response to inflammation in neurons - factors our circuit already tries to help address by acting to prevent neuroinflammation. Its association with Cyan could imply that negative inflammatory effects may be inked with brain vasculature in AD. Gold 2 is associated with the cytoskeleton and axonal cytoskeletal control.In AD, the formation of plaques and protein tangles disrupts the cytoskeleton and perturb axonal connections, engendering cell death. Our circuit tries to target this already by removing the plaques, but perhaps a future improvement should to be to create an element capable to supporting a healthy cytoskeleton or able to remove cytoskeletal protein tangles. Its association with Honey Dew, however, could point to unusual gene expression in this module being due to the lessened use of muscle in old age.</p><br />
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<p class="major_title">A BIOINFORMATICS APPROACH</p><br />
<p class="minor_title">Finding New Parts</p><br />
<p class="body_text"><br />
Bioinformatics creates and enhances methods for storing, retrieving, organising and analysing biological data. We decided to take a completely new approach in our dry lab work and look into bioinformatic approaches to studying <a href="https://2013.igem.org/Team:UCL/Background/Alzheimers" target="_blank">Alzheimer’s disease (AD)</a>. <br />
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<p class="body_text"><br />
The rationale behind this is simple. In order to make a genetic circuit in a synthetic biological construct as effective as possible in a medical application, we may need to target key dysfunctional genes within the problematic biological entity. There are many risk factors for AD and so predicting the key, ‘driver genes’, and the group of proteins with which they interact is invaluable in knowing what we want our construct to produce, in order to mitigate AD. The idea is that bioinformatics work can feed back into synthetic biology, and though we did not have the time to demonstrate this full circle, we feel bioinformatics can have a place in iGEM, helping teams to decide which dysfunctional genes to target in medical projects.</p><br />
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<a href="https://static.igem.org/mediawiki/2013/0/03/Human_interactome.jpg" data-lightbox="image-1" title="The Human Interactome"><br />
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<p class="minor_title">Bioinformatics and Alzheimer’s Disease</p> <br />
<p class="body_text"><br />
Recent progress in characterising AD has lead to the identification of dozens of highly interconnected genetic risk factors, yet it is likely that many more remain undiscovered <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044851/" target="_blank">(Soler-Lopez et al. 2011)</a> and the elucidation of their roles in AD could prove pivotal in beating the condition. AD is genetically complex, linked with many defects both mutational or of susceptibility. These defects produce alterations in the molecular interactions of cellular pathways, the collective effect of which may be gauged through the structure of the protein network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>. In other words, there is a strong link between protein connectivity and the disease phenotype. AD arises from the downstream interplay between genetic and non-genetic alterations in the human protein interaction network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>. <br />
</p><p class="body_text"><br />
Recent progress in characterising AD has lead to the identification of dozens of highly interconnected genetic risk factors, yet it is likely that many more remain undiscovered <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044851/" target="_blank">(Soler-Lopez et al. 2011)</a> and the elucidation of their roles in AD could prove pivotal in beating the condition. AD is genetically complex, linked with many defects both mutational or of susceptibility. These defects produce alterations in the molecular interactions of cellular pathways, the collective effect of which may be gauged through the structure of the protein network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>. In other words, there is a strong link between protein connectivity and the disease phenotype. AD arises from the downstream interplay between genetic and non-genetic alterations in the human protein interaction network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>.<br />
</p><p class="body_text"><br />
In all pathologies, the most common way to predict driver genes is to target commonly recurrent genes. However, this approach misses misses rare altered genes which comprise the majority of genetic defects leading to, for example, carcinogenesis and arguably AD. This is partly because alterations in a single protein module can lead to the same disease phenotype. Thus, identification may best be attempted on a modular level. Yet it is also important to note correlation events between modules. Simply put, many rare gene alterations that influence the module they belong to and co-altered modules can collectively generate the disease pathology (Gu et al. 2013).<br />
<div class="gap"><br />
</div><br />
<p class="minor_title">Our Programme</p> <br />
<p class="body_text"><br />
Under the guidance and tutelage of <a href="http://bmm.cancerresearchuk.org/~cheng03/" target="_blank">Dr Tammy Cheng</a> from the <a href="http://bmm.cancerresearchuk.org/" target="_blank">Biomolecular Modelling (BMM) lab</a> at Cancer Research UK, team member <a href="https://2013.igem.org/Team:UCL/Team/Profile" target="_blank">Alexander Bates</a> coded in python a network analysis programme based on a method devised by Gu et al. and originally applied to the study of glioblastoma (brain cancer). The programme tries to reveal driver genes and co-altered functional modules for AD. The analysis procedure involves mapping altered genes (mutations, amplifications, repressions, etc.) in patient microRNA data to the protein interaction network (PIT), which currently accounts for 48,480 interactions between 10,982 human genes. This is termed the ‘AD altered network’, and is searched with the algorithm suggested by Gu et al. (which has been re-coded from scratch).<br />
</p><br />
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<p class="body_text"><br />
The programme builds up gene sets, two at a time, starting from two seed genes. These sets are termed 'modules'. Pairs of modules (‘G1’ and ‘G2’ in equation) are assumed to be co-altered if any gene within each module is altered in a proportion of AD sufferers, and genes between the modules are often altered together. For two modules, G1 and G2, we must calculate the probability, P, of observing than the number of the samples in the patient gene expression data that by chance simultaneously carry alterations in both gene sets. The gene expression data originates from post-mortem brain samples.<br />
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‘n’ is the total number of patient samples, ‘a’ is the number of patients with alterations in both G1 and G2, ‘b’ is the number of patients with alteration in just G1, ‘c’ is the number of patients with alterations in only G2, and ‘d’ is the number of patients with alterations in neither set. The co-altered score’ S, is defined below. A high score indicates that the two modules tend to be altered together in AD.<br />
</p><p class="body_text"><br />
Fig.1 depicts the searching algorithm. It searches and builds co-altered module pairs for the gene combinations within them that have the greatest co-alteration scores. In step 1, it methodically choose two seed genes from the AD altered network. The ellipsoids in the diagram denote direct interaction partners for these genes. These are added to the seeds to make temporary module pairs. The dashed line represents co-alteration. In step 2, the co-alteration score for each temporary module pair is calculated. Only the pair with the maximal S score is retained for subsequent searching. This maximal group becomes the new seeds group in step 3. In step 4, temporary modules are again derived, this time from step 3, and the maximum score is kept. In step 5, it must determine whether or not this group of genes is going to continue to expand. Each new addition save for the original two starting seeds is removed and S is recalculated. If in one of these configurations S becomes smaller, we loop through steps 3 to 5 again. Otherwise, if all combinations equate to the S value of the gene groups chosen from step 4, the process stops, having assumed that we have reached maximal module size for the two starting seeds.<br />
</p><br />
<p class="body_text"><br />
In other words, we try to build up gene sets within a module as large was we can, whilst with each new addition increasing the co-alteration score.<br />
</p><br />
<p class="body_text"><br />
We should end up with modules that frequently exhibit significant co-alteration in AD patients, and their gene products are therefore likely to be biochemically significant in the disease state.<br />
</p><p class="body_text"><br />
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<p class="minor_title">Results</p> <br />
<p class="body_text"><br />
Originally we planned, as previously suggested, to use the entirety of the human interactome to create an AD interactome and then run our programme in such a way as to build modules from this interactome. However, the estimated run time of the programme over-shot the iGEM 'wiki freeze' deadline. Therefore, we used the expression data for 311 hub genes, whose proteins are points of high connectivity in the human interactome, across 62 modules defined by Zhang et al., and searched for the hub genes combinations that produced the greatest co-alteration scores. The 62 modules are named after colours. <br />
</p><br />
<p class="body_text"><br />
<b>Module groups: </b> <a href="https://static.igem.org/mediawiki/2013/e/ec/AlzModules.txt" target="_blank">AlzModules.py</a><br />
<p class="body_text"><br />
<b>Hub expression data:</b> <a href="https://static.igem.org/mediawiki/2013/7/7a/ALzData2.txt" target="_blank">AlzData.py</a><br />
</p><br />
<p class="body_text"><br />
<b>Module matrix:</b> <a href="https://static.igem.org/mediawiki/2013/5/5f/AlzList.txt" target="_blank">AlzMatrix.py</a><br />
</p><br />
<p class="body_text"><br />
The code for our network analysis programme can be found <a href="https://static.igem.org/mediawiki/2013/4/40/Alex4.txt" target="_blank">here</a>. It needs to be converted to a .py file to be used. Please note that the output is given as a set of numbers that as assigned to genes. For example, the final output for the data we ran can be found <a href="https://static.igem.org/mediawiki/2013/0/0f/AlzFinal.txt" target="_blank">here</a>.<br />
</p><br />
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<table><br />
<th><p class="citation_text">Fig.1 Histogram showing the frequency of gene sets by co-alteration score.</p></th><br />
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We used the output of our programme to produce a histogram, which shows that the frequency of gene combinations falls exponentially with increasing co-alteration score This suggests that a significant few combinations are regularly co-altered in Alzheimer's disease, in modules that may help drive the disease state. Because we are only looking at which hub genes within modules, we are most interested in what modules are co-altered in the high score end of the histogram, and not the hub genes specifically.</p><br />
<p class="body_text"><br />
Below, Fig.2 shows the twenty gene set pairs between two modules, which yielded the greatest co-alteration score. The module pair with the highest score, and that recurs most frequently in the top twenty, are the 'Khaki' and 'Honey Dew' modules. The most enriched functional category of the khaki module is the biosynthesis of a neurotransmitter called GABA. GABA is responsible for neuronal excitability and muscle tone. The Honey Dew module is primarily involved in muscle contraction, though the hub genes AHCYL1 and C9orf61 are thought to be involved in inositol signaling and are possibly associated with another brain condition, bi-polar disorder. However, since the gene expression data is from generally older patients, given the profile of AD, these muscle associated modules may be altered together because of changing muscle usage with age (there is no muscle in the brain but this may represent brain cell structural integrity). Both of these modules have almost 100% of their total brain gene expression in the prefrontal cortex, and area known to be heavily impacted in AD, causing cognitive and intellectual damage. This suggests that our genetic circuit could be adapted to target signaling mechanisms in this area.</p><br />
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<table><br />
<th><p class="citation_text">Fig.2 Table of the top 20 gene combinations and their modules by co-alteration score.</p></th><br />
</table><br />
<table><br />
<tr><br />
<th>Module Name and Gene Set</th><br />
<th>Module Name and Gene Set</th><br />
<th>Co-alteration Score</th><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>20.39 </td><br />
<tr><br />
<td>SLC15A2, FXYD1</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>19.73 </td><br />
<tr><br />
<td>GJA1, FXYD1</td><br />
<td>RFX4, AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>19.37 </td><br />
<tr><br />
<td>GJA1, FXYD1, ATP13A4</td><br />
<td>C20orf141, RFX4, AHCYL1, DGCR6</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>18.99 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RBM4</td><br />
<td>Contig47252_RC, IFITM2, CDK2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>18.81 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RBM4</td><br />
<td>ENST00000289005, Contig47252_RC, IFITM2, CDK2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>17.69 </td><br />
<tr><br />
<td>GJA1, FXYD1, SLC15A2</td><br />
<td>RFX4, AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>17.57 </td><br />
<tr><br />
<td>RRM2, NM_022346, FAM64A</td><br />
<td>OR4F5, GRAP, XM_166973</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Wheat</b></td><br />
<td>17.49 </td><br />
<tr><br />
<td>DYNC2LI1, RBM4</td><br />
<td>AF087999</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>16.95 </td><br />
<tr><br />
<td>HMMR</td><br />
<td>OR4F5, GRAP</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>16.95 </td><br />
<tr><br />
<td>HMMR</td><br />
<td>OR4F5, GRAP, CRYBA2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Wheat</b></td><br />
<td>16.78 </td><br />
<tr><br />
<td>CIRBP, RBM4</td><br />
<td>AF087999</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>16.64 </td><br />
<tr><br />
<td>RRM2, NMMR, FAM64A</td><br />
<td>KRTHB4, GRAP, XM_166973</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.47 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td><br />
<td>Contig47252_RC, IFITM2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.46 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td><br />
<td>Contig47252_RC, IFITM2, CDK2</td><br />
</tr> <br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Forestgreen</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.43 </td><br />
<tr><br />
<td>IFITM3, CSDA</td><br />
<td>CSDA</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.38 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td><br />
<td>ENST00000289005, Contig47252_RC, IFITM2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.27 </td><br />
<tr><br />
<td>FXYD1, ATP13A4, SLC15A2</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.25 </td><br />
<tr><br />
<td>FXYD1, ATP13A4</td><br />
<td>DGCR6, AHCYL1, C20orf141, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Gold 2</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.21 </td><br />
<tr><br />
<td>TUBB2B, NM_178525</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.04 </td><br />
<tr><br />
<td>SPON1, FXYD1, SLC15A2</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
</table> <br />
<br />
</p><br />
<p class="minor_title">Analysis and Feedback into Circuit</p><br />
<p class="body_text"><br />
The second highest scoring module pair, and the second most frequent in the top twenty, are 'Turquoise' and 'Cyan'. The former is primarily involved with NAD(P) homeostasis, and so is significant in cells' metabolism, while the genes in the later mainly play a role in vasculature development. This suggests that co-alteration in genes involved within these two modules could impact cell vitality and trophic support and help cause AD. This suggests that our circuit could be improved by being adapted to help maintain general cell health and energy supply in the brain. </p><br />
<p class="body_text"><br />
The third highest scoring module pair, and the third most frequent in the top twenty, are 'Green 4' and 'Yellow 3'. Green 4 is involved in cell cycle regulation, and area that has already been targeted by our circuit, which produces <b>BDNF</b> to help avoid chromosomal division in the neurons of AD patients. Yellow 3 is associated with the peripheral nervous system. Co-alteration here may again be indicative of gene expression changes with age, and its link with Green 4 may suggest that this is to do with a deficiency in cell division, regeneration and growth, but this is not directly related to AD, although hub genes like GRAP do play a role in cytoplasmic signaling in cells including neurons and glia, This suggests that our circuit could be improved by being adapted to help maintain general cell health and energy supply in the brain. </p><br />
<p class="body_text"><br />
Other module pairs that feature in the top twenty include 'Wheat' and 'Turqouise', 'Forestgreen' and 'Cyan' and 'Gold 2' and 'Honey Dew'. Wheat is involved in protein folding and responses to unfolded and mis-folded protein. This is significant because incorrectly formed and folded amyloid is strongly associated with the progression of AD. This is something out circuit already seeks to address, but by targeting elements of the 'Wheat' module and similar modules it could aim to avoid mis-creation in the first place, and the nucleation of other mis-folded proteins. Forestgreen is involved in immune functions, which implicates microglia and the cellular response to inflammation in neurons - factors our circuit already tries to help address by acting to prevent neuroinflammation. Its association with Cyan could imply that negative inflammatory effects may be inked with brain vasculature in AD. Gold 2 is associated with the cytoskeleton and axonal cytoskeletal control.In AD, the formation of plaques and protein tangles disrupts the cytoskeleton and perturb axonal connections, engendering cell death. Our circuit tries to target this already by removing the plaques, but perhaps a future improvement should to be to create an element capable to supporting a healthy cytoskeleton or able to remove cytoskeletal protein tangles. Its association with Honey Dew, however, could point to unusual gene expression in this module being due to the lessened use of muscle in old age.</p><br />
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<p class="major_title">A BIOINFORMATICS APPROACH</p><br />
<p class="minor_title">Finding New Parts</p><br />
<p class="body_text"><br />
Bioinformatics creates and enhances methods for storing, retrieving, organising and analysing biological data. We decided to take a completely new approach in our dry lab work and look into bioinformatic approaches to studying <a href="https://2013.igem.org/Team:UCL/Background/Alzheimers" target="_blank">Alzheimer’s disease (AD)</a>. <br />
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The rationale behind this is simple. In order to make a genetic circuit in a synthetic biological construct as effective as possible in a medical application, we may need to target key dysfunctional genes within the problematic biological entity. There are many risk factors for AD and so predicting the key, ‘driver genes’, and the group of proteins with which they interact is invaluable in knowing what we want our construct to produce, in order to mitigate AD. The idea is that bioinformatics work can feed back into synthetic biology, and though we did not have the time to demonstrate this full circle, we feel bioinformatics can have a place in iGEM, helping teams to decide which dysfunctional genes to target in medical projects.</p><br />
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<a href="https://static.igem.org/mediawiki/2013/0/03/Human_interactome.jpg" data-lightbox="image-1" title="The Human Interactome"><br />
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<p class="minor_title">Bioinformatics and Alzheimer’s Disease</p> <br />
<p class="body_text"><br />
Recent progress in characterising AD has lead to the identification of dozens of highly interconnected genetic risk factors, yet it is likely that many more remain undiscovered <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044851/" target="_blank">(Soler-Lopez et al. 2011)</a> and the elucidation of their roles in AD could prove pivotal in beating the condition. AD is genetically complex, linked with many defects both mutational or of susceptibility. These defects produce alterations in the molecular interactions of cellular pathways, the collective effect of which may be gauged through the structure of the protein network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>. In other words, there is a strong link between protein connectivity and the disease phenotype. AD arises from the downstream interplay between genetic and non-genetic alterations in the human protein interaction network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>. <br />
</p><p class="body_text"><br />
Recent progress in characterising AD has lead to the identification of dozens of highly interconnected genetic risk factors, yet it is likely that many more remain undiscovered <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3044851/" target="_blank">(Soler-Lopez et al. 2011)</a> and the elucidation of their roles in AD could prove pivotal in beating the condition. AD is genetically complex, linked with many defects both mutational or of susceptibility. These defects produce alterations in the molecular interactions of cellular pathways, the collective effect of which may be gauged through the structure of the protein network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>. In other words, there is a strong link between protein connectivity and the disease phenotype. AD arises from the downstream interplay between genetic and non-genetic alterations in the human protein interaction network <a href="http://www.sciencedirect.com/science/article/pii/S0092867413003875" target="_blank">(Zhang et al. 2013)</a>.<br />
</p><p class="body_text"><br />
In all pathologies, the most common way to predict driver genes is to target commonly recurrent genes. However, this approach misses misses rare altered genes which comprise the majority of genetic defects leading to, for example, carcinogenesis and arguably AD. This is partly because alterations in a single protein module can lead to the same disease phenotype. Thus, identification may best be attempted on a modular level. Yet it is also important to note correlation events between modules. Simply put, many rare gene alterations that influence the module they belong to and co-altered modules can collectively generate the disease pathology (Gu et al. 2013).<br />
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<p class="minor_title">Our Programme</p> <br />
<p class="body_text"><br />
Under the guidance and tutelage of <a href="http://bmm.cancerresearchuk.org/~cheng03/" target="_blank">Dr Tammy Cheng</a> from the <a href="http://bmm.cancerresearchuk.org/" target="_blank">Biomolecular Modelling (BMM) lab</a> at Cancer Research UK, team member <a href="https://2013.igem.org/Team:UCL/Team/Profile" target="_blank">Alexander Bates</a> coded in python a network analysis programme based on a method devised by Gu et al. and originally applied to the study of glioblastoma (brain cancer). The programme tries to reveal driver genes and co-altered functional modules for AD. The analysis procedure involves mapping altered genes (mutations, amplifications, repressions, etc.) in patient microRNA data to the protein interaction network (PIT), which currently accounts for 48,480 interactions between 10,982 human genes. This is termed the ‘AD altered network’, and is searched with the algorithm suggested by Gu et al. (which has been re-coded from scratch).<br />
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The programme builds up gene sets, two at a time, starting from two seed genes. These sets are termed 'modules'. Pairs of modules (‘G1’ and ‘G2’ in equation) are assumed to be co-altered if any gene within each module is altered in a proportion of AD sufferers, and genes between the modules are often altered together. For two modules, G1 and G2, we must calculate the probability, P, of observing than the number of the samples in the patient gene expression data that by chance simultaneously carry alterations in both gene sets. The gene expression data originates from post-mortem brain samples.<br />
</p><br />
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‘n’ is the total number of patient samples, ‘a’ is the number of patients with alterations in both G1 and G2, ‘b’ is the number of patients with alteration in just G1, ‘c’ is the number of patients with alterations in only G2, and ‘d’ is the number of patients with alterations in neither set. The co-altered score’ S, is defined below. A high score indicates that the two modules tend to be altered together in AD.<br />
</p><p class="body_text"><br />
Fig.1 depicts the searching algorithm. It searches and builds co-altered module pairs for the gene combinations within them that have the greatest co-alteration scores. In step 1, it methodically choose two seed genes from the AD altered network. The ellipsoids in the diagram denote direct interaction partners for these genes. These are added to the seeds to make temporary module pairs. The dashed line represents co-alteration. In step 2, the co-alteration score for each temporary module pair is calculated. Only the pair with the maximal S score is retained for subsequent searching. This maximal group becomes the new seeds group in step 3. In step 4, temporary modules are again derived, this time from step 3, and the maximum score is kept. In step 5, it must determine whether or not this group of genes is going to continue to expand. Each new addition save for the original two starting seeds is removed and S is recalculated. If in one of these configurations S becomes smaller, we loop through steps 3 to 5 again. Otherwise, if all combinations equate to the S value of the gene groups chosen from step 4, the process stops, having assumed that we have reached maximal module size for the two starting seeds.<br />
</p><br />
<p class="body_text"><br />
In other words, we try to build up gene sets within a module as large was we can, whilst with each new addition increasing the co-alteration score.<br />
</p><br />
<p class="body_text"><br />
We should end up with modules that frequently exhibit significant co-alteration in AD patients, and their gene products are therefore likely to be biochemically significant in the disease state.<br />
</p><p class="body_text"><br />
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<p class="minor_title">Results</p> <br />
<p class="body_text"><br />
Originally we planned, as previously suggested, to use the entirety of the human interactome to create an AD interactome and then run our programme in such a way as to build modules from this interactome. However, the estimated run time of the programme over-shot the iGEM 'wiki freeze' deadline. Therefore, we used the expression data for 311 hub genes, whose proteins are points of high connectivity in the human interactome, across 62 modules defined by Zhang et al., and searched for the hub genes combinations that produced the greatest co-alteration scores. The 62 modules are named after colours. <br />
</p><br />
<p class="body_text"><br />
<b>Module groups: </b> <a href="https://static.igem.org/mediawiki/2013/e/ec/AlzModules.txt" target="_blank">AlzModules.py</a><br />
<p class="body_text"><br />
<b>Hub expression data:</b> <a href="https://static.igem.org/mediawiki/2013/7/7a/ALzData2.txt" target="_blank">AlzData.py</a><br />
</p><br />
<p class="body_text"><br />
<b>Module matrix:</b> <a href="https://static.igem.org/mediawiki/2013/5/5f/AlzList.txt" target="_blank">AlzMatrix.py</a><br />
</p><br />
<p class="body_text"><br />
The code for our network analysis programme can be found <a href="https://static.igem.org/mediawiki/2013/4/40/Alex4.txt" target="_blank">here</a>. It needs to be converted to a .py file to be used. Please note that the output is given as a set of numbers that as assigned to genes. For example, the final output for the data we ran can be found <a href="https://static.igem.org/mediawiki/2013/0/0f/AlzFinal.txt" target="_blank">here</a>.<br />
</p><br />
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<table><br />
<th><p class="citation_text">Fig.1 Histogram showing the frequency of gene sets by co-alteration score.</p></th><br />
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We used the output of our programme to produce a histogram, which shows that the frequency of gene combinations falls exponentially with increasing co-alteration score This suggests that a significant few combinations are regularly co-altered in Alzheimer's disease, in modules that may help drive the disease state. Because we are only looking at which hub genes within modules, we are most interested in what modules are co-altered in the high score end of the histogram, and not the hub genes specifically.</p><br />
<p class="body_text"><br />
Below, Fig.2 shows the twenty gene set pairs between two modules, which yielded the greatest co-alteration score. The module pair with the highest score, and that recurs most frequently in the top twenty, are the 'Khaki' and 'Honey Dew' modules. The most enriched functional category of the khaki module is the biosynthesis of a neurotransmitter called GABA. GABA is responsible for neuronal excitability and muscle tone. The Honey Dew module is primarily involved in muscle contraction, though the hub genes AHCYL1 and C9orf61 are thought to be involved in inositol signaling and are possibly associated with another brain condition, bi-polar disorder. However, since the gene expression data is from generally older patients, given the profile of AD, these muscle associated modules may be altered together because of changing muscle usage with age (there is no muscle in the brain but this may represent brain cell structural integrity). Both of these modules have almost 100% of their total brain gene expression in the prefrontal cortex, and area known to be heavily impacted in AD, causing cognitive and intellectual damage. This suggests that our genetic circuit could be adapted to target signaling mechanisms in this area.</p><br />
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<table><br />
<th><p class="citation_text">Fig.2 Table of the top 20 gene combinations and their modules by co-alteration score.</p></th><br />
</table><br />
<table><br />
<tr><br />
<th>Module Name and Gene Set</th><br />
<th>Module Name and Gene Set</th><br />
<th>Co-alteration Score</th><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>20.39 </td><br />
<tr><br />
<td>SLC15A2, FXYD1</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>19.73 </td><br />
<tr><br />
<td>GJA1, FXYD1</td><br />
<td>RFX4, AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>19.37 </td><br />
<tr><br />
<td>GJA1, FXYD1, ATP13A4</td><br />
<td>C20orf141, RFX4, AHCYL1, DGCR6</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>18.99 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RBM4</td><br />
<td>Contig47252_RC, IFITM2, CDK2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>18.81 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RBM4</td><br />
<td>ENST00000289005, Contig47252_RC, IFITM2, CDK2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>17.69 </td><br />
<tr><br />
<td>GJA1, FXYD1, SLC15A2</td><br />
<td>RFX4, AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>17.57 </td><br />
<tr><br />
<td>RRM2, NM_022346, FAM64A</td><br />
<td>OR4F5, GRAP, XM_166973</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Wheat</b></td><br />
<td>17.49 </td><br />
<tr><br />
<td>DYNC2LI1, RBM4</td><br />
<td>AF087999</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>16.95 </td><br />
<tr><br />
<td>HMMR</td><br />
<td>OR4F5, GRAP</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>16.95 </td><br />
<tr><br />
<td>HMMR</td><br />
<td>OR4F5, GRAP, CRYBA2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Wheat</b></td><br />
<td>16.78 </td><br />
<tr><br />
<td>CIRBP, RBM4</td><br />
<td>AF087999</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Green 4</b></td><br />
<td><b>Yellow 3</b></td><br />
<td>16.64 </td><br />
<tr><br />
<td>RRM2, NMMR, FAM64A</td><br />
<td>KRTHB4, GRAP, XM_166973</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.47 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td><br />
<td>Contig47252_RC, IFITM2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.46 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td><br />
<td>Contig47252_RC, IFITM2, CDK2</td><br />
</tr> <br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Forestgreen</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.43 </td><br />
<tr><br />
<td>IFITM3, CSDA</td><br />
<td>CSDA</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Turquoise</b></td><br />
<td><b>Cyan</b></td><br />
<td>16.38 </td><br />
<tr><br />
<td>DYNC2LI1, CIRBP, ACRC, RCC1, RBM4</td><br />
<td>ENST00000289005, Contig47252_RC, IFITM2</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.27 </td><br />
<tr><br />
<td>FXYD1, ATP13A4, SLC15A2</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.25 </td><br />
<tr><br />
<td>FXYD1, ATP13A4</td><br />
<td>DGCR6, AHCYL1, C20orf141, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Gold 2</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.21 </td><br />
<tr><br />
<td>TUBB2B, NM_178525</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
<tr><br />
<td></td><br />
</tr><br />
<td><b>Khaki</b></td><br />
<td><b>Honey Dew</b></td><br />
<td>16.04 </td><br />
<tr><br />
<td>SPON1, FXYD1, SLC15A2</td><br />
<td>AHCYL1, C9orf61</td><br />
</tr><br />
</table> <br />
<br />
</p><br />
<p class="minor_title">Analysis and Feedback into Circuit</p><br />
<p class="body_text"><br />
The second highest scoring module pair, and the second most frequent in the top twenty, are 'Turquoise' and 'Cyan'. The former is primarily involved with NAD(P) homeostasis, and so is significant in cells' metabolism, while the genes in the later mainly play a role in vasculature development. This suggests that co-alteration in genes involved within these two modules could impact cell vitality and trophic support and help cause AD. This suggests that our circuit could be improved by being adapted to help maintain general cell health and energy supply in the brain. </p><br />
<p class="body_text"><br />
The third highest scoring module pair, and the third most frequent in the top twenty, are 'Green 4' and 'Yellow 3'. Green 4 is involved in cell cycle regulation, and area that has already been targeted by our circuit, which produces <b>BDNF</b> to help avoid chromosomal division in the neurons of AD patients. Yellow 3 is associated with the peripheral nervous system. Co-alteration here may again be indicative of gene expression changes with age, and its link with Green 4 may suggest that this is to do with a deficiency in cell division, regeneration and growth, but this is not directly related to AD, although hub genes like GRAP do play a role in cytoplasmic signaling in cells including neurons and glia, This suggests that our circuit could be improved by being adapted to help maintain general cell health and energy supply in the brain. </p><br />
<p class="body_text"><br />
Other module pairs that feature in the top twenty include 'Wheat' and 'Turqouise', 'Forestgreen' and 'Cyan' and 'Gold 2' and 'Honey Dew'. Wheat is involved in protein folding and responses to unfolded and mis-folded protein. This is significant because incorrectly formed and folded amyloid is strongly associated with the progression of AD. This is something out circuit already seeks to address, but by targeting elements of the 'Wheat' module and similar modules it could aim to avoid mis-creation in the first place, and the nucleation of other mis-folded proteins. Forestgreen is involved in immune functions, which implicates microglia and the cellular response to inflammation in neurons - factors our circuit already tries to help address by acting to prevent neuroinflammation. Its association with Cyan could imply that negative inflammatory effects may be inked with brain vasculature in AD. Gold 2 is associated with the cytoskeleton and axonal cytoskeletal control.In AD, the formation of plaques and protein tangles disrupts the cytoskeleton and perturb axonal connections, engendering cell death. Our circuit tries to target this already by removing the plaques, but perhaps a future improvement should to be to create an element capable to supporting a healthy cytoskeleton or able to remove cytoskeletal protein tangles. Its association with Honey Dew, however, could point to unusual gene expression in this module being due to the lessened use of muscle in old age.</p><br />
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<p class="major_title">Creative Writing Competition: 'Changing the Human Brain'</p><br />
</p><br />
<p class="minor_title">Opening Ethical Windows Into The Human Mind</p><br />
<br />
<p class="body_text"><br />
Science fiction is, perhaps, the greatest liar the universe has ever known. Where are our <a href="http://www.imdb.com/title/tt1219289/" target="_blank">'limitless'</a> pills that super excite our brains into genius? Where is the <a href="http://www.imdb.com/title/tt0338013/" target="_blank"> eternal sunshine</a> of the memory eraser machine and where is the fiery ‘nervous system upgrade’ technology featured in Iron Man 3? Ever since <a href="http://www.wired.com/wiredscience/2012/03/what-can-novelists-learn-from-neuroscience" target="_blank">science met fiction</a> writers have envisioned possible, if not always plausible, technologies to come and this has fed right back into science, inspiring generations of new researchers and moulding the public’s perception of what goes on under the fume hood and in the petri dish. Sometimes it gets its predictions right, as with the touchscreen of Star Trek or the point-of-view guns of Douglas Adam’s, but right or wrong it plays a key role in demonstrating public opinion and controlling it.<br />
</p><br />
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Fiction gives us an <a href="http://pieceslight.blogspot.co.uk/2013/05/neuroscience-in-fiction.html" target="_blank">unparalleled medium</a> through which to comprehend the value of neuroscientific accounts of behaviour and experience, because it allows for a very human non-scientific study of the effects of neuroscience, from the point of view of the very minds encountering new fandangled technologies. If genetic engineering of the brain really does perturb our sense of selfhood, help us fight mental diseases or endow us with new abilities, writers will swarm to produce work that can act as an ethical window into these nascent technologies. Their fiction can tell us something about how we consume, as a society, scientific ideas and blend them with social philosophy.<br />
</p><br />
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<br />
<p class="body_text"><br />
This is the thinking, along with the animated discussions at our <a href="https://2013.igem.org/Team:UCL/Debate" target="_blank">speed debate</a>, that inspired us to run a creative writing competition on the topic ‘changing the human brain/mind’. The competition ran from the 14th of August to September the 15th, and we received over fifty entries. Writers were allowed to submit short stories of 500-1,500 words, poems of up to 40 lines and (screen)plays of up to a 30 minute run time. The winning entries, along with a commentary by a UCL scientist, can be found below, in alphabetical order.<br />
</p><br />
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<b>The UCL iGEM 2013 Spotless Mind team would like to thank everyone who has sent their entries to us. It's a wonderful thing to receive submissions from many countries all around the world, knowing that our project has reached different corners of the globe! We are humbled by your interest in our project, and we hope these winning entries will serve as inspiration to people passionate in both literature and science.</b><br />
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<p class="major_title">Winning Entries</p><br />
</p><br />
<p class="minor_title">Congratulations!</p><br />
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<p class="body_text"><br />
<br />
<p class="body_text"><b> <a href="https://static.igem.org/mediawiki/2013/e/e3/Ivy_Alvarez.pdf" target="_blank">Cipher by Ivy Alzvarez</a></p><br />
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</p><br />
<p class="body_text"><br />
<I>They looked trivial. He knew the crowd was made up of individuals, each one with a story, each life holding value, but what of it? Together they made up an apathic crowd. One which, from his perspective, looked trivial..</I><br />
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<p class="body_text"><b> <a href="https://static.igem.org/mediawiki/2013/5/5b/Natasha_Ali.pdf" target="_blank">Anamnesis by Natasha Ali</a><br />
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<p class="body_text"><br />
<I>Swallow, says the hawk. She gestures at the plate in front of me. It is grey, but then the plates are always grey. Our clothes are always white and starched and uncomfortable. Our stools are always cold when we sit on them, our feet grazing the colder floor...</I><br />
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<br />
</p><br />
<p class="body_text"><br />
<b>Writer's biography: Natasha Ali was born in Karachi, Pakistan, and raised both there and in Brussels, Belgium, but currently lives in Riyadh, KSA. She is about to start her last year at a sixth-form college. The trials and tribulations of scientific research have always fascinated her.</b><br />
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<p class="body_text"><b> <a href="https://static.igem.org/mediawiki/2013/a/a5/Paul_Aroniyo.pdf" target="_blank">Moving Too Fast by Paul Aroniyoi</a></p><br />
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<I>Doctor says the effects of the drugs will soon fade, as my brain reconfigures itself and adjusts to accommodating; I quote “Higher levels of cognitive thinking.” But I’m sure weeks have passed and yet these headaches and the nausea still persist; I’m not getting any better and I think the Doc knows it...</I><br />
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<b>Writer's biography: I’m currently a Creative Writing student and I’m from London. I’m an avid sci-fi and comic book enthusiast (Yes, which means I love that characters but don’t read the actual comics!).</b><br />
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<p class="body_text"><b> <a href="https://static.igem.org/mediawiki/2013/a/af/Dot_Cobely.pdf" target="_blank">Four Poems by Dot Cobley</a></p><br />
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<I>There’s an intriguing illustration<br />
</p><br />
<p class="body_text"><br />
on this leaflet that the neuro team gave us.<br />
</p><br />
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Looks like somebody had fun<br />
</p><br />
<p class="body_text"><br />
playing around with those little plastic wheels...</I><br />
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</p><br />
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<b>Writer's biography: Dot Cobley’s poems appear in numerous anthologies and magazines, including The Rialto, Smiths Knoll and The SHOp. She underwent neurosurgery for trigeminal neuralgia, and has recently been diagnosed with Parkinson’s.</b><br />
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<p class="body_text"><b> <a href="https://static.igem.org/mediawiki/2013/4/4d/Siân_Davies.pdf" target="_blank">Affordable Beauty by Siân Davies</a></p><br />
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<I>There was electric classical music playing in the elevator when Alan Winterm alighted, his eyes fixed on the pre-released PDA in his hand. The silver ring he wore glinted in the cold, artificial light, dancing in his peripheral vision as he sent emails, replied to messages, booked appointments...</I><br />
<br />
</p><br />
<p class="body_text"><br />
<b>Writer's biography: My name is Siân, it's Welsh but I'm not. I live in a tiny rural town in Shropshire, selling people kettles and trying to learn how to write.</b><br />
<br />
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<br />
<br />
<br />
<p class="body_text"><b> <a href="https://static.igem.org/mediawiki/2013/d/da/Carol_Fraser.pdf" target="_blank">A Change of Mind by Carol Fraser</a></p><br />
<br />
</p><br />
<p class="body_text"><br />
<I>Just suppose. Now let's imagine. What if?<br />
</p><br />
<p class="body_text"><br />
What would it be like to have a memory<br />
</p><br />
<p class="body_text"><br />
That functioned sometimes but at others failed the test, <br />
</p><br />
<p class="body_text"><br />
Like some old creaky household gadget on the blink, <br />
</p><br />
<p class="body_text"><br />
As trusty as a teacup made of lace?<br />
</p><br />
<p class="body_text"><br />
...</I><br />
<br />
</p><br />
<p class="body_text"><br />
<b>Writer's biography: I am a retired musician, now a (very) mature philosophy student. I am interested in every aspect of human condition - and that of non-human.</b><br />
<br />
<br />
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<br />
<p class="body_text"><b> <a href="https://static.igem.org/mediawiki/2013/c/c4/Hilary_Greenleaf.pdf" target="_blank">284 steps by Hilary Greenleaf</a></p><br />
<br />
</p><br />
<p class="body_text"><br />
<I>The past has become an area of conflict, a dangerous area of uncertainty that lies extinct yet threatening, waiting to draw us all into fresh conflict and pain. As a family we are learning to sidestep it, and something that should be so natural for people with a shared history is now taboo...</I><br />
<br />
</p><br />
<p class="body_text"><br />
<b>Writer's biography: Hilary Greenleaf (46) is an HCPC registered podiatrist and mother of two. She lives in the Essex countryside and writes short stories in her spare time.</b><br />
<br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text"><b> <a href="https://static.igem.org/mediawiki/2013/8/86/Fatima_Muhammad.pdf" target="_blank"> The Demolishing Change by Fatima Muhammad</a></p><br />
<br />
</p><br />
<p class="body_text"><br />
<I>They looked trivial. He knew the crowd was made up of individuals, each one with a story, each life holding value, but what of it? Together they made up an apathic crowd. One which, from his perspective, looked trivial...</I><br />
<br />
</p><br />
<p class="body_text"><br />
<b>Writer's biography: Fatima Muhammad is a doctor, currently doing a postgraduate degree in Medical Education from Cardiff University. She’s had a few short stories published. Nothing large-scale yet, but here’s hoping.</b><br />
<br />
<div class="gap"></div><br />
<br />
<p class="body_text"><b> <a href="https://static.igem.org/mediawiki/2013/2/2f/Martha_Patterson.pdf" target="_blank"> A Constant Man by Martha Patterson</a></p><br />
<br />
</p><br />
<p class="body_text"><br />
<I>As he ages, Johnny comes to grips with his mother's dementia and her anxieties about her marriage...</I><br />
<br />
</p><br />
<p class="body_text"><br />
<b>Writer's biography: Martha Patterson has written more than 100 plays and has had work published in four anthologies by the International Centre for Women Playwrights and several collections by JAC Publishing and Original Works Publishing. Her work has been produced Off-Off-Broadway and in the UK, Korea, and Australia, as well as in twelve states around the USA. She has also had a half-hour mystery produced by Shoestring Radio Theatre in San Francisco. She earned her B.A. from Mount Holyoke College and an M.A. from Emerson College, both degrees in Theatre. She is a member of the Dramatists Guild of America, the International Centre for Women Playwrights, Screen Actors Guild, and Actors’ Equity Association. She lives in Boston, Massachusetts.</b><br />
<br />
<div class="gap"></div><br />
<br />
<br />
<p class="body_text"><b> <a href="https://static.igem.org/mediawiki/2013/4/4e/Ng_Chin_San.pdf" target="_blank">Changing the Human Brain by Ng Chin San</a></p><br />
<br />
</p><br />
<p class="body_text"><br />
<I>The road below was scattered with litter, broken beer bottles and picket signs the only evidence left of the chaos that was the previous evening. The Scientist stood motionless at the window surveying the dark scene, taking a sip from a glass of whiskey in his hand from time to time as Beethoven’s piano sonata in G minor played softly in the background...</I><br />
<br />
</p><br />
<p class="body_text"><br />
<b>Writer's biography: Ng Chin San is a third year law student at UCL. He enjoys playing sports. He also enjoys sleeping.</b><br />
<br />
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</html></div>Oranhttp://2013.igem.org/Team:UCL/Project/SafetyTeam:UCL/Project/Safety2013-10-05T02:51:41Z<p>Oran: </p>
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<p class="major_title">KEEPING SAFE</p><br />
<p class="minor_title">It's Just Good Lab Practice</p><br />
<p class="body_text"><br />
The health and safety considerations of the project are vital for the well-being of the project members and the laboratory environment. Some of the key considerations to be made involve handling bio-hazardous materials, process chemicals and mechanical operations; requiring planning and awareness in order prevent hazards from occurring, as well as appropriate plans of action in order to deal with any situation that may occur.<br />
</p><br />
<p class="body_text"><br />
The Escherichia coli strain used is not considered pathogenic, and thus not of considerable risk to the environment or team members. Despite this, there must still be a level of Good Laboratory Practice (GLP) to reduce risk associated with performing experiments on a daily basis. As a minimum requirement, all members will wear some form of protective clothing, which generally consists of a lab coat and goggles, as well as single use gloves.<br />
</p><br />
<p class="body_text"><br />
For disposal of spent materials, any item should be isolated and sealed in a container which prevents physical contact with any of the facility. The material should then be removed from the room through the waste corridor and disposed of accordingly (chemical or steam treatment for example).<br />
</p><br />
<p class="body_text"><br />
Continual monitoring and safety reviews of the facility is required to maintain GLP and the condition of the plant. By undertaking continual assessments of laboratory safety, the risk of a hazard occurring in the plant can be reduced. Training of team members in safe operation in general safety protocols is essential to reduce risk; so safety training and education in good laboratory practise was undertaken by all team members before experiments could begin.<br />
</p><br />
<p class="body_text"><br />
The main principle behind performing experiments in a safe environment is to minimise risk. Risk is considered as the amalgamation of the potential damage that a hazard could cause, combined with the likelihood of said hazard occurring. The damage a hazard could cause is normally set by the equipment present in the laboratory, whilst laboratory team members can often decrease risk by decreasing the likelihood of a hazard occurring. Below the three main categories (Microbiological, Chemical and Mechanical) are defined with tables showing which hazard fall into said category, and how the risk is minimised in each case.<br />
</p><br />
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<p class="minor_title">Microbiological Hazards</p><br />
<p class="body_text"><br />
The key focus of microbiological hazard control concerns the Escherichia coli expression system being used. The majority of Escherichia coli strains are categorised into Bio-safety levels 1 or 2, with non-pathogenic strains placed into level 1 under World Health Organisation classifications. Therefore the Escherichia coli strain used in the facility is considered to be Bio-safety level one, which often requires less stringent safety standards. The genetically engineered strains also may require more safety regulation requirements than wild type micro-organisms; however advances in synthetic biology are allowing more control over the expression system in terms of pathogenicity and also the ability to express suicide genes, which can prevent any live Escherichia coli from surviving outside of the facility environment. Regardless, the facility must ensure and validate that all of the Escherichia coli is killed and effectively disposed of. Pressure systems and directed ventilation are in place to prevent escape of any strains, although this may lead to possible contaminants entering the halls, which dictates that the live Escherichia coli must be sealed in containers or vessels at all times where possible in order to prevent infection. The use of mammalian systems in the project also present a degree of hazard, although the associated risk is lower when compared to using bacteria.<br />
</p><br />
<br />
<table><br />
<tr><br />
<th><b>Hazard Type</b></th><br />
<th>Hazard Explanation</th><br />
<th>How Hazard Is Addressed</th><br />
</tr><br />
<tr><br />
<td>Escape of Escherichia coli from lab</td><br />
<td>The strain(s) exit the laboratory environment, thus allowing the possibility of contamination of exterior objects and/or persons.</td><br />
<td>Pressure of lab is lower than of corridor, thus causing airflow into the lab as opposed of out, which minimises the chance of airborne or aerosol escape of E. coli. Laboratory coats are worn over clothes which do not leave the lab, thus reducing possibility of a team member inadvertently acting as a carrier out of the lab.</td><br />
</tr><br />
<tr><br />
<td>Contamination of E coli/ HeLa/ Microglia</td><br />
<td>Contamination of the desired strain leads to competition between several strains of bacteria, resulting in inaccurate stocks and therefore unusable data. Note that bacterial and mammalian experiments are undertaken in separate parts of the facility.</td><br />
<td>Exposure of strain to open air is minimised, which is standard practise for any container that may be involved with the desired E. coli strain. Single use gloves are used to minimise possible contamination when experiments are performed. Ethanol is applied on work surfaces before and after experimental procedures in order to minimise contamination on work surfaces etc.</td><br />
</tr><br />
<tr><br />
<td>Contamination of team members</td><br />
<td>Illness could ensue from working in the laboratory environment.</td><br />
<td>Team members are not advised to work when feeling unwell. Gloves, lab coat and goggles are also worn to minimise contact with organisms. Personal hygiene standards are advised to be upheld in particular for laboratory workers.</td><br />
</tr><br />
<tr><br />
<td>HeLa Cells</td><br />
<td>Level 2 biosafety class cells</td><br />
<td>Cell experiments always performed in a sterile environment and a fume hood, with a supervisor providing assistance to the experiment when necessary</td><br />
</tr><br />
<tr><br />
<td>Microglia cells</td><br />
<td>Level 2 biosafety class cells</td><br />
<td>Dr. Darren Nesbeth will perform the neccesary assays in place of iGEm members due to the safety regulations</td><br />
</tr><br />
</table><br />
<br />
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<p class="minor_title">Chemical Hazards</p><br />
<p class="body_text"><br />
When performing experiments, it may be necessary to use dangerous chemicals during certain processes. Whilst using hazardous chemicals is avoided where necessary, in some cases it is required, so in each case where this occurs the procedure must be executed in a fashion which is as safe as reasonably possible. Therefore standard protocols such as wearing protective clothing may be added to by performing experiments in fume hoods or other such devices to minimise any chance of contact via evaporation etc.<br />
</p><br />
<br />
<table><br />
<tr><br />
<th><b>Hazard Type</b></th><br />
<th>Hazard Explanation</th><br />
<th>How Hazard Is Addressed</th><br />
</tr><br />
<tr><br />
<td>Ethidium Bromide (EtBr)</td><br />
<td>Mutagenic.<br />
Minor toxicity issue</td><br />
<td>All operations using EtBr are carried out in a fume cupboard - separate equipment (pipettes etc.) are used only in the fume cupboard specifically for EtBr.<br />
Gloves and long sleeved protective gowns MUST be worn, disposal of items in contact with EtBr are disposed of separately to other wastes.<br />
Wash hands after gloves are removed.</td><br />
</tr><br />
<tr><br />
<td>High concentration Ethanol (EtOH) and other alcohols</td><br />
<td>Flammable.</td><br />
<td>Securely stored (glass container) in a separate cupboard in volumes less than 2 Litres.</td><br />
</tr><br />
<tr><br />
<td>Dimethyl sulfoxide & Zeocin</td><br />
<td>Irritant to operator if contact with skin is made</td><br />
<td>Chemical handled with extreme caution, with particular attention to no skin exposed when performing an experiment, particularly goggles must be worn</td><br />
</tr><br />
</table><br />
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<p class="minor_title">Mechanical Hazards</p><br />
<p class="body_text"><br />
For this project, mechanical hazards pose the lowest probability of occurring out of the three and thus may be considered as the lowest risk group. However, there are hazards present which must be minimised. The predominant issue here is the use of the desktop centrifuge system, which poses considerable danger if a blowout occurs.<br />
</p><br />
<br />
<table><br />
<tr><br />
<th><b>Hazard Type</b></th><br />
<th>Hazard Explanation</th><br />
<th>How Hazard Is Addressed</th><br />
</tr><br />
<tr><br />
<td>Centrifuge</td><br />
<td>Centrifuge mechanical failure (eg. of rotor) can cause severe damage to the machine, and possibly personnel in the nearby vicinity.</td><br />
<td><br />
Regular inspections and maintenance of the machine is required (performed by external engineers). <br />
Machine not to be used if in non optimal condition.<br />
Centrifuge must be balanced to minimise chance of rotor failure.<br />
Centrifuge must be properly closed before use (required for the machine to work.)<br />
Attention must be paid to ensure that nothing is spilled in the centrifuge bowl, or a build up of dust or aerosol should also be avoided.</td><br />
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<p class="minor_title">Concluding Comments</p><br />
<p class="body_text"><br />
The safety of the team members, produced material and environment are key aspects to the smooth running of the project, and it must be ensured that adequate measures are in place to provide protection and ensuring that guidelines can be followed proceeding mandatory safety training. Current Good Laboratory Practice must also be adhered to at all times. By following guidelines and acting in an appropriate manner whilst in the laboratory, the general risk in terms of micro-biological, chemical and mechanical aspects will be reduced to a level which is as low as reasonably possible for all project members involved.<br />
</p><br />
<p class="body_text"><br />
Due to the relatively small scale of the experiments performed, the quantities used of material is not large, but still must be handled and respected properly at all times. Proper containment and labelling of materials in sealed containers is necessary, especially when of particular hazard such as live material or Ethidium Bromide. Flammability concerns are also present with alcohol and other flammable materials stored in the lab, although the volumes (generally less than 2L) are not considerably large enough, although should still be used and handled properly when used, with fire extinguishers available and also emergency exits present if required. Protective clothing also should be worn where appropriate to minimise the possibility of skin contact, with wash and first aid station available in key areas where allowable due to the area classification.<br />
</p><br />
<p class="body_text"><br />
The mechanical safety predominantly concerns the proper maintenance of the centrifuge system, which can cause a considerable risk if a failure occurs. Regular inspections should be made, with the centrifuge often restricted access when it was considered unfit for use. This room is also placed away from mammalian and bacterial processing centres. Prevention methods should also be in place such as emergency shut-off procedures.<br />
</p><br />
<p class="body_text"><br />
By following all guidelines set out in safety training and following supervisor instructions, the project may be performed in a safe and effective manner. Continual training and inspection of the facility will aid in ensuring safety levels are maintained, with review protocols in place to investigate any faults. Team members have been trained in general safety, with supervisors having first aid experience and training. These considerations combined will ensure the safety risk to the personnel; product and environment is as low as reasonably possible. For further reading, the Institute of Chemical Engineering (IChemE) has relevant information, as well as other safety guidance documents relating to the relevant country where the university is located.<br />
</p><br />
<br />
<p class="minor_title">Safety Forms</p><br />
<br />
<p class="body_text"><br />
<a href="https://static.igem.org/mediawiki/2013/2/20/IGEM_2013_Basic_Safety_Form.pdf" target="_blank">Basic safety form</a> & <a href="https://static.igem.org/mediawiki/2013/7/71/IGEM_Biosafety_Form_Part_2.pdf" target="_blank">Biosafety form</a><br />
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</html></div>Oranhttp://2013.igem.org/Team:UCL/Project/ChassisTeam:UCL/Project/Chassis2013-10-05T02:48:30Z<p>Oran: </p>
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<p class="major_title">CHASSIS</p><br />
<p class="minor_title">Hosting A Genetic Circuit</p><br />
<p class="body_text"><br />
Synthetic biologists refer to the host cells for their ‘genetic circuits’, inserted genes sequences, as a ‘chassis’. You can think of the genetic circuit as computer code, and the chassis as the machine that will run it. The chassis manages all the material a genetic circuit requires to function, providing building blocks for protein synthesis, energy and an environment in which the inserted genes can operate. Cellular machinery is essential for reading a circuit's information. Synthetic biologists generally use a small suite of well understood chassis, primarily <i> Escherichia coli</i> (E.coli), in order to better standardise their creations and allow for the easy use of parts in labs worldwide. Other cell types must often be used for different types of circuit. The properties of a chassis often need to complement the properties of its genetic circuit. Highly specialist chassis may have to be used to perform specific tasks.<br />
</p><br />
<p class="body_text"><br />
If a chassis is to be a cell from a multicellular organism, then they can either be taken from that organism directly and used, these are primary cells and are generally harder to transfect, or immortalised cell lines are used. Immortalised cell lines can survive for long periods of time in vitro because, while they cannot divide indefinitely, they have been genetically manipulated to sidestep cellular senescence. Their behaviour is generally a good approximation to cells of the same type working in an organism, but the mutations and their accumulated genetic alterations can change their functioning slightly. <br />
</p><br />
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<p class="minor_title">E.Coli</p><br />
<p class="body_text"><br />
In our project, we used three different chassis; E.coli, HeLa cells and microglial cells. E.Coli are used to create our BioBricks, since they are easy to work with and have a high proliferation rate. They can produce relatively large amounts of recombinant plasmid in a short time frame.<br />
</p><br />
<p class="minor_title">HeLa</p><br />
<p class="body_text"><br />
Owing to difficulties obtaining microglia and the fact that, as immune cells, they are harder to transfect, we began work in HeLa cells to characterise our BioBricks and show that they work in a human cell line. HeLa cells are an immortalised human cell line of cervical cells derived from a cancer patient, Henrietta Lacks, who died of her illness in 1951. Given the appropriate growth medium and space, HeLa cells are capable of dividing rapidly and for a mammalian cell line are incredibly persistent - so much so that we have to be careful that we do not give them the opportunity to infect other cell lines in our mammalian lab. Because HeLa cells are cancerous, they produce more telomerase to overcome the Hayflick limit. Ethical debate surrounds the wide use of these cells because the Lacks family never gave full consent for their use in science and the identity of their source become widely known. They are, however, a human cell standard, and easily assimilated into iGEM's ethic of standardisation. They are relatively hardy and easy to transfect.<br />
</p><br />
<p class="minor_title">Microglia</p><br />
<p class="body_text"><br />
We intend to the immortalised human microglia SV-40 cell line. However, at the time of wiki freeze, despite ordering these cells in August, we have not received them. Arriving late from Applied Biological Materials and subsequently stuck in UCL's bureaucratic machinery that surrounds the lab (since they are human tissue cells) we nevertheless expect to be able to work with them after the jamboree and fully intend to continue to create our circuit in them whatever our results in the iGEM competition. <br />
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</html></div>Oranhttp://2013.igem.org/Team:UCL/Project/ChassisTeam:UCL/Project/Chassis2013-10-05T02:47:51Z<p>Oran: </p>
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<p class="major_title">CHASSIS</p><br />
<p class="minor_title">Hosting A Genetic Circuit</p><br />
<p class="body_text"><br />
Synthetic biologists refer to the host cells for their ‘genetic circuits’, inserted genes sequences, as a ‘chassis’. You can think of the genetic circuit as computer code, and the chassis as the machine that will run it. The chassis manages all the material a genetic circuit requires to function, providing building blocks for protein synthesis, energy and an environment in which the inserted genes can operate. Cellular machinery is essential for reading a circuit's information. Synthetic biologists generally use a small suite of well understood chassis, primarily <i> Escherichia coli</i> (E.coli), in order to better standardise their creations and allow for the easy use of parts in labs worldwide. Other cell types must often be used for different types of circuit. The properties of a chassis often need to complement the properties of its genetic circuit. Highly specialist chassis may have to be used to perform specific tasks.<br />
</p><br />
<p class="body_text"><br />
If a chassis is to be a cell from a multicellular organism, then they can either be taken from that organism directly and used, these are primary cells and are generally harder to transfect, or immortalised cell lines are used. Immortalised cell lines can survive for long periods of time in vitro because, while they cannot divide indefinitely, they have been genetically manipulated to sidestep cellular senescence. Their behaviour is generally a good approximation to cells of the same type working in an organism, but the mutations and their accumulated genetic alterations can change their functioning slightly. <br />
</p><br />
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<p class="minor_title">E.Coli</p><br />
<p class="body_text"><br />
In our project, we used three different chassis; E.coli, HeLa cells and microglial cells. E.Coli are used to create our BioBricks, since they are easy to work with and have a high proliferation rate. They can produce relatively large amounts of recombinant plasmid in a short time frame.<br />
</p><br />
<p class="minor_title">HeLa</p><br />
<p class="body_text"><br />
Owing to difficulties obtaining microglia and the fact that, as immune cells, they are harder to transfect, we began work in HeLa cells to characterise our BioBricks and show that they work in a human cell line. HeLa cells are an immortalised human cell line of cervical cells derived from a cancer patient, Henrietta Lacks, who died of her illness in 1951. Given the appropriate growth medium and space, HeLa cells are capable of dividing rapidly and for a mammalian cell line are incredibly persistent - so much so that we have to be careful that we do not give them the opportunity to infect other cell lines in our mammalian lab. Because HeLa cells are cancerous, they produce more telomerase to overcome the Hayflick limit. Ethical debate surrounds the wide use of these cells because the Lacks family never gave full consent for their use in science and the identity of their source become widely known. They are, however, a human cell standard, and easily assimilated into iGEM's ethic of standardisation. They are relatively hardy and easy to transfect.<br />
</p><br />
<p class="minor_title">Microglia</p><br />
<p class="body_text"><br />
We intend to the immortalised human microglia SV-40 cell line. However, at the time of wiki freeze, despite ordering these cells in August, we have not received them. Arriving late from Applied Biological Materials and subsequently stuck in UCL's bureaucratic machinery that surrounds the lab (since they are human tissue cells) we nevertheless expect to be able to work with them after the jamboree and fully intend to continue to create our circuit in them whatever our results in the iGEM competition. <br />
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</html></div>Oranhttp://2013.igem.org/Team:UCL/Project/ChassisTeam:UCL/Project/Chassis2013-10-05T02:47:10Z<p>Oran: </p>
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<p class="major_title">CHASSIS</p><br />
<p class="minor_title">Hosting A Genetic Circuit</p><br />
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Synthetic biologists refer to the host cells for their ‘genetic circuits’, inserted genes sequences, as a ‘chassis’. You can think of the genetic circuit as computer code, and the chassis as the machine that will run it. The chassis manages all the material a genetic circuit requires to function, providing building blocks for protein synthesis, energy and an environment in which the inserted genes can operate. Cellular machinery is essential for reading a circuit's information. Synthetic biologists generally use a small suite of well understood chassis, primarily <i> Escherichia coli</i> (E.coli), in order to better standardise their creations and allow for the easy use of parts in labs worldwide. Other cell types must often be used for different types of circuit. The properties of a chassis often need to complement the properties of its genetic circuit. Highly specialist chassis may have to be used to perform specific tasks.<br />
</p><br />
<p class="body_text"><br />
If a chassis is to be a cell from a multicellular organism, then they can either be taken from that organism directly and used, these are primary cells and are generally harder to transfect, or immortalised cell lines are used. Immortalised cell lines can survive for long periods of time in vitro because, while they cannot divide indefinitely, they have been genetically manipulated to sidestep cellular senescence. Their behaviour is generally a good approximation to cells of the same type working in an organism, but the mutations and their accumulated genetic alterations can change their functioning slightly. <br />
</p><br />
<div class="gap"></div><br />
<p class="minor_title">E.Coli</p><br />
<p class="body_text"><br />
In our project, we used three different chassis; E.coli, HeLa cells and microglial cells. E.Coli are used to create our BioBricks, since they are easy to work with and have a high proliferation rate. They can produce relatively large amounts of recombinant plasmid in a short time frame.<br />
</p><br />
<p class="minor_title">HeLa</p><br />
<p class="body_text"><br />
Owing to difficulties obtaining microglia and the fact that, as immune cells, they are harder to transfect, we began work in HeLa cells to characterise our BioBricks and show that they work in a human cell line. HeLa cells are an immortalised human cell line of cervical cells derived from a cancer patient, Henrietta Lacks, who died of her illness in 1951. Given the appropriate growth medium and space, HeLa cells are capable of dividing rapidly and for a mammalian cell line are incredibly persistent - so much so that we have to be careful that we do not give them the opportunity to infect other cell lines in our mammalian lab. Because HeLa cells are cancerous, they produce more telomerase to overcome the Hayflick limit. Ethical debate surrounds the wide use of these cells because the Lacks family never gave full consent for their use in science and the identity of their source become widely known. They are, however, a human cell standard, and easily assimilated into iGEM's ethic of standardisation. They are relatively hardy and easy to transfect.<br />
</p><br />
<p class="minor_title">Microglia</p><br />
<p class="body_text"><br />
We intend to the immortalised human microglia SV-40 cell line. However, at the time of wiki freeze, despite ordering these cells in August, we have not received them. Arriving late from Applied Biological Materials and subsequently stuck in UCL's bureaucratic machinery that surrounds the lab (since they are human tissue cells) we nevertheless expect to be able to work with them after the jamboree and fully intend to continue to create our circuit in them whatever our results in the iGEM competition. <br />
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<p class="major_title">The Creative Angle</p><br />
<p class="minor_title">Visual Art</p><br />
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How does the public interpret visually the invasion of synthetic biology into neuroscience? <br />
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We have heavily integrated visual art into our project, especially the website, to help engage the public and stage our ethical ideas. Our team includes two artists-in-residence who have helped design thought-provoking images. <br />
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This Gallery page exhibits primarily poster art pieces that have been created by team member Fong Yi Khoo. We have incorporated these art pieces into our project by using them as promotional posters, speed debate event posters, presentation and so on. You can find other art pieces created by our artists on every corner of our wiki page.<br />
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<p class="body_text">We used this main poster on our T-shirts and on our promotional material. It was imortant to show graphically the damage wrought on a brain by the onset of Alzheimer's Disease, and at the same time, display the vibrancy of the solution that we propose. The watercoulour blots are the figures of gial cells and their processes spreading through the brain. The dark mug from the brain mirrors its stem. It is round, like the figure of a closed circuit of our treatment (we do not propose that the transfected microglia should perpetuate themselves into the second generation). It symbolises the plasmid, and the marks of coffee mugs through the late nights we spent on the project.<br />
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<p class="body_text">The Brain - A Memory Bank?</p><br />
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<p class="body_text">How much do you know about how memories are stored in the brain? No-one really knows for sure, and the theories shared among Psychologists and Neuroscientists are far from common knowledge. At present, there are just under 500,000 people in the UK. We can't show with sores, or with injured limbs: something subtle, yet devastating is happening inside the skull. All that's clear is that the things which make us who we are steadily come apart. <br />
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<p class="body_text">Against the backdrop of damage shifting like heavy cloud through the tissues, what about the brain loss outside?</p><br />
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The Biology that we engineer forces us to think of the machinery in very clear cut terms, but what of the consequences, how will it react?<br />
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<p class="body_text">Neuroethics is confusing isn't it?</p><br />
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<p class="body_text">Coronal section of a brain afflicted with Alzheimers (in pencil)</p><br />
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<p class="body_text">Coronal section of a healthy brain (in pencil)</p><br />
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<p class="body_text">Aspect of the left Cortex (in charcoal)</p><br />
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<p class="body_text">Saggital section of a healthy brain (in charcoal)</p><br />
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</html></div>Oranhttp://2013.igem.org/File:Fongyi_last_minute_posters2.jpgFile:Fongyi last minute posters2.jpg2013-10-05T02:36:53Z<p>Oran: </p>
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<div></div>Oranhttp://2013.igem.org/File:Fongyi_last_minute_posters1.jpgFile:Fongyi last minute posters1.jpg2013-10-05T02:35:27Z<p>Oran: </p>
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What were the skies like when you were young?<br />
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They were beautiful, the most beautiful skies as a <br />
matter of fact. The sunsets were purple and red and <br />
yellow and on fire, and the clouds would catch the <br />
colours everywhere.<br />
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<br>Click to learn more.<br />
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One of the main symptoms of Alzheimer's Disease is the loss of memory.<br />
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Eternal Sunshine is an online vault where a special memory can be stored. Upload an image along with a short message to add your memory to the vault, or scroll down to see uploaded memories.<br />
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Click to upload your memory to Eternal Sunshine.<br />
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It's been a good journey thus far with StJohn's expertise in primer design. I will always remember the special bond in the form of Facebook messages consisting of DNA codes and scientific papers. Wishing StJohn a wonderful year in Montreal!<br />
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The memories of family holidays when you are young are such a mix of emotive events. Stress, arguments, adventure, taking in the wondrous sights of a new place... For me, they are the best of memories, because they distil the essence of what family is and inspire a love for travel.<br />
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I am not religious, but there is something awe inspiring about cathedrals. I suppose that's the point. I've visited many, and treasure these memories because they are a safe place to retreat to when things in the present get hectic.<br />
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This is me and some incredible young people I had the pleasure of teaching for a week at a summer camp in Romania. For me, the whole experience is a reminder of how many good people there are in the world, and how knowing people for such a short amount of time can still yield the richest memories and life-long friendships.<br />
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Going to Namibia and watching the elephants around the waterhole at sunset. They were the most beautiful animals I had ever seen. I hope I never forget this image.<br />
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This is a picture that evokes one of the best days of my life. To me its pictorial form represents a day that will never be replicated and perhaps even an era that has come to pass.<br />
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Visiting America and seeing amazing places like NYC with my best friend who I met online. Amazing in every way, from exploring, to knowing that there are no barriers to meeting fantastic people who can bring happiness into our life.<br />
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This is a photo of me and my grandparents from when I was little. As a child,I used to spend my summer holiday at my grandparents and those moments were the best moments of my life, the single time when I was truly happy day after day. I've never been as happy as then. Certainly I don't want to forget these memories! I want to keep those memories alive as well as my grandfather's image,who passed away a long time ago. Hope I will be able to cherish my childhood memories till my dying day.<br />
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Memories are whey are because of the people you share them with<br />
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This is my favourite object. Its special because it reminds me of personal discovery and surprise, and how surprises occur constantly, even in old environments.<br />
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This was the beginning of my time at uni. It was my first week with people who would become great friends. I've loved my time at uni and this photo reminds me of some the amazing times I have had there.<br />
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There is something mesmerizing about this place; I felt free and honored to be able to witness it.<br />
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Going home from hospital with my two lovely babies and my wonderful husband.<br />
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A time when life was fun, frivolous and uncomplicated!<br />
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Perfect day celebrating birthday and public holiday with friends and family in a gorgeous setting and the knowledge that I was newly pregnant with my son!<br />
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There's a cliff thats particularly soft and green. Underneath there's dark slate shattered everywhere and its nosey and dangerous, but when you lie down its silent, and the softness of the moss muffles everything so all you can see is the sky- even the wind just misses your face. Its the perfect place to just be.<br />
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It represents ambition, the past and the future.<br />
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The perpetual cocktail party.<br />
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The annual bonfire night gathering. The air was always so cold and crisp and nipped at our rosy little cheeks<br />
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My sister and I when we were toddlers.<br />
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2011, wild winter which I missed, but below the upsetting snow there are the stairs of my childhood which were climbed up and down for so many times by me and my father.<br />
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My most treasured memory. Having fun with a group of awesome people in a whole, as one family. <3<br />
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My one and only<br />
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My prom with my beloved one.<br />
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We went to Miss Kiew's house on her 18th birthday cause we are irreplaceable friends. ;P we da bombs. Went there and bully her. As usual. 😂<br />
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Leo Forum 2013 <br />
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Every moment spent with my family is worth remembering.<br />
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Good friends are hard to find, harder to leave, and impossible to forget.<br />
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Why is this memory so special to you?<br />
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Life-changing experience on a student exchange programme all the way to Melbourne, Australia.<br />
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Missing the fun and adventurous times with the best mates!<br />
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Dubrovnik 2012 - this was the view from our bedroom window!<br />
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The beginning of the first story I ever wrote: early 1970s Bermondsey shines through. The place has changed.<br />
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When young, I was often taken to see the high cliffs at Beachy Head. I would throw the stones hard. One time the tide had rushed in to the foot of the cliff and I thought the water looked like flames.<br />
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I used to sell had-drawn T-shirts around the markets in London. As time passed, hare-brained messes like this became rarer. Good fun though!<br />
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Aged 7, around the street there was a dirty greenhouse packed with Cacti in the small concrete front garden. One time, some vandals smashed it, so when my parents weren't looking, I leant over the fence at the maw of the cactus. It looked quite like this.<br />
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The island of La Graciosa, by Lanzarote. This is the first time I had ever seen such a deserted place. The wind was horribly strong, so the waves and foam were brightly lit by the sun. It looked like a scene from Walking with Dinosaurs.<br />
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His name is Domo and he has a friend called Axalotyl. He travels with me everywhere. Like me, he needs glasses because his eyes are minute and way too far apart.<br />
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Recently, I walked out of London, due Southeast on a hot summer's day. When the Voyager probe left the solar system, it took a final look at the planets which were splayed across the sky like London's new glass towers are over this cornfield.<br />
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One of my first gigs. I played to dinosaurs and whalebones.<br />
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When I slept here was terrified of the sound of rushing water. There were lots of strange weeds growing out of the cracks in in the foundations.<br />
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My first year in University, I would run out of the halls and up primrose hill. I had a choice at this stage whether I took the hill steeply or gently, but I was getting a weird mix of runner's high and sleep deprivation. This would be the place I'd aim for.<br />
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I get to this bit of the road on the way home, and I'd always be astonished by how grey it was. Look at it! They must have tried so hard!<br />
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This is the worst flat I ever lived in. I think I was almost electrocuted here.<br />
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On a bike by the lake just outside outside Beijing. It was completely still and the heat was oppressive. Shortly after I took this, an enormous fish (no idea what kind) leapt clean out of the water.<br />
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I had a day off work, so I spent a day reading a book watching people various people sleep on this bench.<br />
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This was from an extraordinary museum. It made me think of the circle of life. It lined a cast bronze bottle and it was lit by cream coloured lighting.<br />
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I camped in Cornwall and for the first time I saw vegetable grown on an industrial scale. That night, I dreamt of bins full of cauliflowers.<br />
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The one and only time I went to a large commercial music festival. It was in Ireland, so it was a mud bath. People fought in the mud. I wore a black coat, black trousers and black boots, and I realised to my surprise that people found me attractive...<br />
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Shanghai is the most stupidly large city I have ever seen. Until I saw it, Paris and London seemed like the centre of the world. I sat half awake in that jam and felt that shock for at least an hour.<br />
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One morning, just after the london riots, I was furious. Really furious and helpless, and I was essentially under curfew, so I went to Brighton. I knew the dawn was beautiful, but right then, those contrails looked like fish guts.<br />
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I saw a Van Gough like crow field. Silly but true.<br />
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I joined the Trinis in the Notting Hill Carnival. We threw chocolate milk about and sang until our throats ached as we went around the corner down Ladbroke Grove.<br />
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In Glasgow, the towers look like gravestones. I was recommended this place by the tourist office in midwinter. It was miserable, and this is about the only time I ever felt homesick.<br />
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From the depths of Dungeness. "Blue lines" by Massive Attack was blaring out of the hut just by me. I found the skeleton of this crab claw in the crunchy gravel and put it on a rusted iron box in front of the Power station. That felt perfect.<br />
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This is a stash of heavy iron balls I found in my garden in Plymouth. That's history.<br />
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Comments: I was dressed funny out on the rocks of Dun Laoghaire in Dublin bay. A wolfhound and a golden retriever walked calmly and gingerly over the rocks. I was after finding someone as serene walking the coast path. She looked a good bet, but she gave me a hard stare.<br />
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There was a sofa outside in the graveyard, knew it was going to go mouldy, but the summer was still young. There was even a small firepit. I wanted to show my girlfriend. One night some sausagehead let the whole thing burn. Nothing left but melted plastic and small resealable bags.<br />
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My brother found a pig mask, so we set out to do Halloween properly. I wore a hollowed pumpkin over my head, speared with marshmallows, candles and wet twigs. My friend wore a morph suit. He fed me a Favorite Chicken burger through my pumpkin-flap. We took the bus to Lewisham and paced the top of Blackheath. My candles hardly lit.<br />
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Camping. We made a nasty lunch out of the remaining food and we sat among the bracken and some of us got sunburnt. We had a speaker, so we listened to Satie's Gnossiennes and Brand New Second Hand. The trees looked more than just alive: they were out to get us. Some guys from the village fired spud guns at us.<br />
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The Council planted sunflowers in the park. - A special year.<br />
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If you have never had fresh noodles before, they are the most extraordinary thing. Try it.<br />
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It was when I saw it in a snowstorm that I realised what an effort of will Peckham is.<br />
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</html></div>Oranhttp://2013.igem.org/File:PB181834.jpgFile:PB181834.jpg2013-10-05T02:22:18Z<p>Oran: </p>
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<div></div>Oranhttp://2013.igem.org/File:P7171373.jpgFile:P7171373.jpg2013-10-05T02:18:32Z<p>Oran: </p>
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<div></div>Oranhttp://2013.igem.org/File:P7131305.jpgFile:P7131305.jpg2013-10-05T02:17:42Z<p>Oran: </p>
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<div></div>Oranhttp://2013.igem.org/File:P7091193.jpgFile:P7091193.jpg2013-10-05T02:16:44Z<p>Oran: </p>
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<div></div>Oranhttp://2013.igem.org/File:P6120780.jpgFile:P6120780.jpg2013-10-05T02:14:49Z<p>Oran: </p>
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<div></div>Oranhttp://2013.igem.org/Team:UCL/MemoriesTeam:UCL/Memories2013-10-05T02:10:10Z<p>Oran: </p>
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What were the skies like when you were young?<br />
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They were beautiful, the most beautiful skies as a <br />
matter of fact. The sunsets were purple and red and <br />
yellow and on fire, and the clouds would catch the <br />
colours everywhere.<br />
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<br>Click to learn more.<br />
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One of the main symptoms of Alzheimer's Disease is the loss of memory.<br />
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Eternal Sunshine is an online vault where a special memory can be stored. Upload an image along with a short message to add your memory to the vault, or scroll down to see uploaded memories.<br />
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Click to upload your memory to Eternal Sunshine.<br />
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It's been a good journey thus far with StJohn's expertise in primer design. I will always remember the special bond in the form of Facebook messages consisting of DNA codes and scientific papers. Wishing StJohn a wonderful year in Montreal!<br />
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The memories of family holidays when you are young are such a mix of emotive events. Stress, arguments, adventure, taking in the wondrous sights of a new place... For me, they are the best of memories, because they distil the essence of what family is and inspire a love for travel.<br />
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I am not religious, but there is something awe inspiring about cathedrals. I suppose that's the point. I've visited many, and treasure these memories because they are a safe place to retreat to when things in the present get hectic.<br />
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This is me and some incredible young people I had the pleasure of teaching for a week at a summer camp in Romania. For me, the whole experience is a reminder of how many good people there are in the world, and how knowing people for such a short amount of time can still yield the richest memories and life-long friendships.<br />
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Going to Namibia and watching the elephants around the waterhole at sunset. They were the most beautiful animals I had ever seen. I hope I never forget this image.<br />
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This is a picture that evokes one of the best days of my life. To me its pictorial form represents a day that will never be replicated and perhaps even an era that has come to pass.<br />
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Visiting America and seeing amazing places like NYC with my best friend who I met online. Amazing in every way, from exploring, to knowing that there are no barriers to meeting fantastic people who can bring happiness into our life.<br />
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This is a photo of me and my grandparents from when I was little. As a child,I used to spend my summer holiday at my grandparents and those moments were the best moments of my life, the single time when I was truly happy day after day. I've never been as happy as then. Certainly I don't want to forget these memories! I want to keep those memories alive as well as my grandfather's image,who passed away a long time ago. Hope I will be able to cherish my childhood memories till my dying day.<br />
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Memories are whey are because of the people you share them with<br />
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This is my favourite object. Its special because it reminds me of personal discovery and surprise, and how surprises occur constantly, even in old environments.<br />
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This was the beginning of my time at uni. It was my first week with people who would become great friends. I've loved my time at uni and this photo reminds me of some the amazing times I have had there.<br />
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There is something mesmerizing about this place; I felt free and honored to be able to witness it.<br />
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Going home from hospital with my two lovely babies and my wonderful husband.<br />
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A time when life was fun, frivolous and uncomplicated!<br />
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Perfect day celebrating birthday and public holiday with friends and family in a gorgeous setting and the knowledge that I was newly pregnant with my son!<br />
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There's a cliff thats particularly soft and green. Underneath there's dark slate shattered everywhere and its nosey and dangerous, but when you lie down its silent, and the softness of the moss muffles everything so all you can see is the sky- even the wind just misses your face. Its the perfect place to just be.<br />
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It represents ambition, the past and the future.<br />
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The perpetual cocktail party.<br />
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The annual bonfire night gathering. The air was always so cold and crisp and nipped at our rosy little cheeks<br />
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My sister and I when we were toddlers.<br />
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2011, wild winter which I missed, but below the upsetting snow there are the stairs of my childhood which were climbed up and down for so many times by me and my father.<br />
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My most treasured memory. Having fun with a group of awesome people in a whole, as one family. <3<br />
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My one and only<br />
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My prom with my beloved one.<br />
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We went to Miss Kiew's house on her 18th birthday cause we are irreplaceable friends. ;P we da bombs. Went there and bully her. As usual. 😂<br />
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Leo Forum 2013 <br />
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Every moment spent with my family is worth remembering.<br />
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Good friends are hard to find, harder to leave, and impossible to forget.<br />
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Why is this memory so special to you?<br />
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Life-changing experience on a student exchange programme all the way to Melbourne, Australia.<br />
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Missing the fun and adventurous times with the best mates!<br />
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Dubrovnik 2012 - this was the view from our bedroom window!<br />
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The beginning of the first story I ever wrote: early 1970s Bermondsey shines through. The place has changed.<br />
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When young, I was often taken to see the high cliffs at Beachy Head. I would throw the stones hard. One time the tide had rushed in to the foot of the cliff and I thought the water looked like flames.<br />
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I used to sell had-drawn T-shirts around the markets in London. As time passed, hare-brained messes like this became rarer. Good fun though!<br />
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Aged 7, around the street there was a dirty greenhouse packed with Cacti in the small concrete front garden. One time, some vandals smashed it, so when my parents weren't looking, I leant over the fence at the maw of the cactus. It looked quite like this.<br />
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The island of La Graciosa, by Lanzarote. This is the first time I had ever seen such a deserted place. The wind was horribly strong, so the waves and foam were brightly lit by the sun. It looked like a scene from Walking with Dinosaurs.<br />
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His name is Domo and he has a friend called Axalotyl. He travels with me everywhere. Like me, he needs glasses because his eyes are minute and way too far apart.<br />
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Recently, I walked out of London, due Southeast on a hot summer's day. When the Voyager probe left the solar system, it took a final look at the planets which were splayed across the sky like London's new glass towers are over this cornfield.<br />
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One of my first gigs. I played to dinosaurs and whalebones.<br />
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When I slept here was terrified of the sound of rushing water. There were lots of strange weeds growing out of the cracks in in the foundations.<br />
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My first year in University, I would run out of the halls and up primrose hill. I had a choice at this stage whether I took the hill steeply or gently, but I was getting a weird mix of runner's high and sleep deprivation. This would be the place I'd aim for.<br />
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I get to this bit of the road on the way home, and I'd always be astonished by how grey it was. Look at it! They must have tried so hard!<br />
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This is the worst flat I ever lived in. I think I was almost electrocuted here.<br />
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On a bike by the lake just outside outside Beijing. It was completely still and the heat was oppressive. Shortly after I took this, an enormous fish (no idea what kind) leapt clean out of the water.<br />
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I had a day off work, so I spent a day reading a book watching people various people sleep on this bench.<br />
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This was from an extraordinary museum. It made me think of the circle of life. It lined a cast bronze bottle and it was lit by cream coloured lighting.<br />
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I camped in Cornwall and for the first time I saw vegetable grown on an industrial scale. That night, I dreamt of bins full of cauliflowers.<br />
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The one and only time I went to a large commercial music festival. It was in Ireland, so it was a mud bath. People fought in the mud. I wore a black coat, black trousers and black boots, and I realised to my surprise that people found me attractive...<br />
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Shanghai is the most stupidly large city I have ever seen. Until I saw it, Paris and London seemed like the centre of the world. I sat half awake in that jam and felt that shock for at least an hour.<br />
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One morning, just after the london riots, I was furious. Really furious and helpless, and I was essentially under curfew, so I went to Brighton. I knew the dawn was beautiful, but right then, those contrails looked like fish guts.<br />
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I saw a Van Gough like crow field. Silly but true.<br />
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I joined the Trinis in the Notting Hill Carnival. We threw chocolate milk about and sang until our throats ached as we went around the corner down Ladbroke Grove.<br />
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In Glasgow, the towers look like gravestones. I was recommended this place by the tourist office in midwinter. It was miserable, and this is about the only time I ever felt homesick.<br />
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From the depths of Dungeness. "Blue lines" by Massive Attack was blaring out of the hut just by me. I found the skeleton of this crab claw in the crunchy gravel and put it on a rusted iron box in front of the Power station. That felt perfect.<br />
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This is a stash of heavy iron balls I found in my garden in Plymouth. That's history.<br />
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