Team:Manchester/popdynamictest

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             <p id="footer"><b>Figure 1. Two colour image of FabA homodimer with labelled N- and C-Termini (arrows)</b><br>
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             <p><b><a id="Q1">Introduction</a></b><br>
             <p><b><a id="Q1">Introduction</a></b><br>
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Our model of the fatty acid biosynthesis pathways highlighted several key enzymes that could be altered to produce palm oil, including Delta 9, Delta 12 and FabA, which agrees with previous publications on these enzymes. Delta 9 and Delta 12 are involved in linear pathways, meaning that there are obvious reactants and products, so the overexpression of them can be measured directly via LC-MS and other techniques. However, FabA is a β-Hydroxydecanoyl Thiol Ester Dehydrase involved in a cyclic pathway<sup>[1]</sup> specifically the conversion of β-hydroxy acyl-ACP to Enol acyl-ACP as part of the fatty acid biosynthetic pathway<sup>[2]</sup>. Therefore characterising any specific change due to FabA overexpression will be challenging, as any products will automatically be involved in the proceeding stage of the cycle and it would therefore be difficult to determine the effect of overexpression. An alternative to measure the overexpression of FabA, would be through the addition of His-tags to either the N-terminal and or the C-terminal of FabA. However, depending on the structure of FabA, the addition of His-tags could potentially interfere with expression, protein folding, enzymatic functions and interactions<sup>[3][4][5][6][7]</sup>. Several studies including work by <sup>[5]</sup> and <sup>[6]</sup>, respectively showed that the addition of C-Terminus His-tags to proteins could interfere with enzyme activity and alter di-sulphide and therefore protein structure. These problems are particularly applicable to FabA, as it forms a homodimer, as shown by Leesong et al., 1996<sup>[1]</sup>. Therefore, the addition of His-tag could potentially interfere with the interaction domain and thus the formation of a homodimer, which would be consistent with several reports<sup>[4][5][6]</sup>. To address this issue, we decided to perform a molecular dynamics simulation using the GROMACS software package<sup>[8]</sup> on a structure of FabA, determined by X-ray crystallography by Leesong et al., 1996<sup>[1]</sup>. This would allow for the trajectories of the N- and C-Termini of FabA over the course of the simulation to be studied, therefore allowing us to identify which terminal would be more suited for His-tag addition.
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As part of our research into the ethical aspects of our project, we have modelled the future population of Sumatran Orangutan in a variety of situations; including with and without our project being implemented, and reflecting the uncertainty found in the economical research we compiled as part of this project as to how much we can replace traditional methods of Palm Oil production.
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This involved the use of the program Vortex:  “a Monte Carlo simulation of the effects of deterministic forces as well as demographic, environmental, and genetic stochastic events on wild population”[1]. Using this, we were able to run 200 iterations of each scenario, giving us a series of results based on the probability of a number of chance events, including catastrophes (eg. fires, landslides), inbreeding, litter frequencies and sex. This lead to the production of a variety of outcomes based on the occurrences of these chance events, the majority of which adhere to a general trend around a mean.
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We decided to base our model on the Sumatran Orangutan, due to their smaller population size. Restricted to the island of Sumatra in Western Indonesia, this species is at threat due to mass deforestation, a large amount of which occurs as a direct result of the Palm Oil industry. As the majority of the remaining orangutans are concentrated in a relatively small area in the north of the island, we treated this as a single population. We also chose Sumatra as it is home to a number of other endangered animals, included the critically endangered Sumatran Tiger and Sumatran Rhino, which are facing the same threats, and potentially the same future, as the Sumatran Orangutan.
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The aim of this aspect of our modelling ventures is to validate the need for our project, and set an overall deadline as to when our project must be put into action in order to save the Sumatran Orangutan.
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            <p><b><a id="Q2">Installing GROMACS</a></b><br>
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        <p><b><a id="Q2">Method</a></b><br>
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The Groningen Machine for Chemical Simulation (GROMACS) Version 4.5.5 [8], was installed on a MacBook Pro 2011 model, operating OS X 10.8.3, with a 4 GB 1333 MHz DDR3 memory, 2.3 GHz Intel Core i5 processor and a 320 GB SATA disk drive. Prior to installing GROMACS, “command line tools” were installed within Xcode, Version 4.6.1. GROMACS was installed to single precision, with the source file downloaded from www.gromacs.org/Downloads. In addition to GROMACS, the FFTW library, Version 3.3.3<sup>[9]</sup> was installed, with the source file downloaded from www.fftw.org/download.html.
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The building of the baseline for our orangutan model used methods previously outlined in the paper Orangutan population biology, life history, and conservation[2], and we adapted this to represent current orangutans population levels and deforestation levels, as well as the effects of our project.
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Average age of first reproduction has been established to be 15 years for female Sumatran Orangutan and 25 years for male Sumatran Orangutans. No menopause has been recorded in the  Sumatran Orangutan, and therefore Sumatran Orangutans were presumed to continue to produce litters until towards the end of the Orangutans lifespan, at 50 years of age(Orangutans in the wild are thought to potentially live up to several decades, but the oldest recorded lifespan is 55 years)[3].
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Density-dependant effects on reproduction were modelled using the same growth curve found in Lacy (2009), where  , where P0 =18.2, Pk = 11.1, A = 1, B=2 and N = initial population size.
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We modelled the effect of deforestation using time variable carry capacity:  where K is carry capacity and P is the present population.
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             <p><b><a id="Q3">Result </b><br>
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            <p id="footer"><b>Figure 2. GROMACS Molecular Dynamics Simulation Workflow. The commands used at each step are included, with a brief description of their function</b><br>
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Using the methods described previously, we were able to model a variety of scenario and monitor the population sizes of the Orangutan throughout them.
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             <p><b><a id="Q3">Behind the scenes of the Molecular Dynamics Simulations</a></b><br>
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For the simulations of FabA, a dimeric structure of FabA (PDB ID: 1MKB) derived by X-ray Diffraction, with a resolution of 2 Å from an E.Coli expression system by<sup>[1]</sup> was used.
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The molecular dynamics simulation was performed within the GROMACS package using version 4.5.5 [8], with the AMBER99SB force field<sup>[10]</sup>, the transferrable intermolecular potential 3P (TIP3P) water model<sup>[11]</sup> and the original crystal waters, from the X-Ray Crystallography with periodic boundary conditions. The methods for generating both topologies and parameters for G16bP are as described above. For all cases of the Protein in water, a protocol derived from the “Lysozyme in Water” GROMACS tutorial (by J. Lemkul, Department of Biochemistry, Virginia Tech) and optimized for FabA was used. Briefly, a cubic cell of 2 nm in diameter with the protein centered was used and filled with the generic single point charge 216 (SPC216) water configuration<sup>[12]</sup>. The system was neutralized to a salt concentration of 150 mM by adding Na+ and Cl-. Energy Minimization (EM) was performed using the Steepest Descent Algorithm<sup>[13]</sup> with a tolerance of 1000 KJ mol-1 nm-1, to remove any steric clashes or inappropriate geometries.
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In the simulation, the long-range electrostatic interactions were modeled using the Particle-Mesh- Eswald (PME) method <sup>[14][15]</sup> and the Linear Constraint Solver (LINCS) algorithm<sup>[16]</sup> to preserve chemical bond lengths. Temperature and pressure coupling were performed independently, using a modified Berendsen thermostat<sup>[17]</sup> at constant temperature of 300 K at a time constant of 0.1 ps and the Parinello-Rahman barostat algorithm<sup>[18]</sup> at a constant pressure of 1 bar, for a time constant of 2 ps and V-rescale, respectively.
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The Molecular Dynamics simulations trajectories were analysed with the GROMACS analysis tools<sup>[19]</sup>, to output structural molecular dynamics trajectories and PyMOL (The PyMOL Molecular Graphics System, Education-Use-Only, Version 1.3 Schrödinger, LLC) used to visualize and create both still structures and videos. All calculations of progression plots from the simulations were produced using the GROMACS analysis tools with output files viewed in the Microsoft Excel 2011.
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We began by establishing when we can expect to see an extinction of the orangutan based on deforestation data collected previously as part of our bioethics research. This data put the extinction timeline for the Sumatran Orangutan to be around 43 - 45 years. All models ran to extinction. This is a slightly earlier estimation than previous explorations into the potential extinction of the Orangutan, which is likely to be because of an increase in the rate of deforestation over the past few years, fueled by palm oil and biofuel targets, although most models so should dangerously low populations levels in 40 years time [2].</p>
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            <p><b><a id="Q4">Getting a working GROMACS simulation for FabA</a></b><br>
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To study the motions of the N- and C-Termini of FabA with molecular dynamics, we first had to get an optimized system preparation protocol, which we based on the “Lysozyme in Water” GROMACS tutorial by <sup>[20]</sup>. Firstly, we built a simulation box of 2nm around FabA, which was sufficient to satisfy the minimum image convention and the simulation cut-off schemes without adding excess solvent. FabA had to then be solvated within this box by a solvent configuration compatible with the solvent model applied to the protein, in this case the SPC216<sup>[12]</sup>, which is compatible with the TIP3 water model<sup>[11]</sup>. The system is then neutralized through the addition of ions to a molarity of 150 mM, therefore allowing the system to reach a neutral state. Our results indicate that FabA is well solvated in SPC216 water and neutralized to a molarity of 150 mM in a cubic 2 nm cell.
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<img src="https://static.igem.org/mediawiki/2013/0/07/Fabafig1.png" width="900" height="300"/>
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<p id="footer"><b>Figure 2. Figure 1: Predicted extinction of orangutans, based on deforestation data analysed on our Human Practices pages (LINK)  (2.36% annual loss of rainforest coverage). 200 simulations are shown</b></p><br>
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<p>Using this simulation, estimates of the Orangutan population at set time periods in the future were created. Then, a variety of scenarios made possible by the implementation of our project were created.
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To begin with, we modelled the effect that a complete end to further deforestation, which is an potentially achieve outcome of our project. This time frame for this occurring is difficult to predict, so we began by looking at what we could expect if the implementation of our project takes a further 40 years.
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Our best case scenarios from the previous model suggested that in 40 years time we will be expecting Sumatran Orangutan population to be around 650 individuals. Therefore, we modelled a population of orangutans at this number for another 100 years, presuming that deforestation had stopped, but that the previous land that had been used for the production of palm oil is not being reconverted to land viable for orangutan use (for example, the land being too drained of nutrients to support rainforest, being converted to the production of another crop, or even continuing to be used for palm oil production, in order to support global demand which may never be able to be abolished - more information on these likely scenarios is available on our economics and bioethics pages, found here LINK TO ETHICS PAGE).
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<img src="https://static.igem.org/mediawiki/2013/c/c9/Fabafig3.png" width="900" height="258"/>
 
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<p id="footer"><b>Figure 3. Establishing, solvating and neutralising FabA, for simulation preparation.  A. The establishment of a 2nm simulation cube around the dimerised structure of FabA. B. The solvation of the FabA structure by the addition of SPC216 water molecule as a solvent. C. Neutralisation of the solvated system by the addition of Na+ and Cl- ions. Na+ and Cl- ions are represented by Blue and Green spheres, respectively and SPC216 water is represented by Cyan coloured molecules.</b><p>
 
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<p>Once we had prepared the system, it was relaxed by energy minimization. This ensures that there are no steric clashes or inappropriate geometries that exist, by applying the steepest descent algorithm<sup>[13]</sup>. With the minimized structure, the system of solvent and ions around the protein are equilibrated to become orientated about the protein solute at the same temperature, by an isothermal-isochoric ensemble<sup>[17]</sup>. Pressure is then applied using an isobaric-isochoric ensemble, thereby ensuring that the system reaches a proper density<sup>[18]</sup>.  Our energy minimized structure shows a decrease from -6.75E+05, to a maximum energy plateau for the system of -1.05 E+06 after 1238 ps of minimization time. The temperature of the system quickly reaches the target value of 300 K remaining stable, with an average temperature of 299.82 K, the equivalent of 27 °C over the 100 ps equilibration. Over the course of the 100 ps equilibration stage, both the pressure and density of the system averages 0 bar and 1015.19 Kg m-3.  This is close to the experimental value of 0 bar and 1000 Kg m-3 and the equivalent of Earth’s atmospheric pressure at sea level and the density of water. The pressure fluctuations are consistent with the applied isothermal-isobaric ensemble and is suggestive of compatible molecular dynamics conditions for simulations of FabA.</p>
 
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<img src="https://static.igem.org/mediawiki/2013/1/1a/Fabafig4.png" width="900" height="500"/>
 
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<p id="footer"><b>Figure 4. Graphs of the Energy Minimisation, Temperature, Pressure and Density equilibration of the FabA simulation system prior to simulation. </b><p>
 
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            <p><b><a id="Q5">To His-tag or not to His-tag</a></b><br>
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<p id="footer"><b>Figure 2: Predicted Sumatran Orangutan population levels, if deforestation in Indonesia was halted after 40 years.
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Now that FabA is ready to undergo simulations, we ran a simulation for 1 ns under the notion that we would be able to visualize motions around the N- and C-Terminals during the course of the simulation and therefore determine, which terminal would be more appropriate to add His-tags to. The conclusion for our simulation was that the N-Terminal is ideal for the addition of His-tags for several reasons. Firstly, the C-terminal is localized in close vicinity to the interaction domain of the FabA homodimer, therefore the addition of His-tags could possibly interfere with the dimer interaction<sup>[6]</sup>. Our model also shows that the C-terminal is more dynamic compared to the N-terminal and there are several times in the simulation that the C-terminal interacts with the dimerization domain and may interfere with the folding and function of the protein<sup>[4][5][6]</sup>. Therefore we concluded that the N-terminal would be ideal to add the His-tags to, as the N-terminal is less dynamic and will be less likely to interfere with folding and the protein function. With this in mind, our experimental team began to design methods to extract FabA and add His-tags to use for the characterization of FabA overexpression.
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<p>We can see from these simulations that Orangutans at this small a population are unable to recover from the effect that palm oil has had, or even reach carrying capacity in the land with has been left to them. This is likely to be due to a mixture of the sparse population density of the Orangutan leading to lower frequencies of mating, and inbreeding of the remaining population. In addition, there are possibilities that the population of the Orangutans could drop to dangerously low levels, below 200 individuals, where a chance event such as flooding and landslides could erase the remainder of the population, meaning that we could still see an extinction of the Sumatran Orangutan within the next 140 years.
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We also ran simulations for if certain events occurred in 30 years, rather than 40 years, time. As our simulations for this time period showed more variance, we modelled both a best case (2520 individuals remaining) and worst case (1664 individuals remaining) scenario.
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<p id="footer"><b>Figure 5. Overlay of structures from 1 ns FabA simulation. Images of overlaid from the following respective time points: 0 ps, 250 ps, 500 ps, 750 ps and 1000 ps with the following colours indicating each individual image: Green, Blue, Purple, Orange and Grey, respectively.  Both the N-Terminal and C-Terminal, are specified (Dotted Box), with a zoom in on each respective terminal at an angle appropriate to visualise the positions of the terminals.
 
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<iframe src="//player.vimeo.com/video/75638296" width="900" height="300" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>
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<img src="https://static.igem.org/mediawiki/2013/0/07/Fabafig1.png" width="900" height="300"/>
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<p id="footer"><b>Figure 3: Predicted Sumatran Orangutan population levels, if deforestation in Indonesia was halted after 30 years and populations at this points were at the worst case scenario level indicated by previous simulations (1664 individuals)</b></p><br>
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<div class="note">
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<img src="https://static.igem.org/mediawiki/2013/0/07/Fabafig1.png" width="900" height="300"/>
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<b>Note</b><br>
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<p id="footer"><b>Figure 4: Predicted Sumatran Orangutan population levels, if deforestation in Indonesia was halted after 30 years and populations at this points were at the best case scenario level indicated by previous simulations (2520 individuals)</b></p><br>
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All figures of FabA were produced by us, using the FabA structure (PDB ID: 1MKB found at http://www.rcsb.org/pdb/explore/explore.do?structureId=1MKB) as obtained by Leesong et al 1996
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<p>From Figure 3 and 4, we can see that in most cases, orangutan population levels remain at a viable level, although in certain instances, struggling to maintain a population at carry capacity. However, it would seem that if we were able to implement our project with a 30 year time frame, the Orangutan may be able to maintain a healthy population size.
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However, due to the giant demand for Palm Oil demonstrated during our economic investigations, it is unlikely that we will ever be able to entirely replace the palm oil industry. Therefore, we also modelled the Orangutan populations at this critical 30 year point if we were able to lower annual rainforest loss by a half.</p>
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          <p><b>References</b> <br>
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[1]. Leesong, M., Henderson, B.S., Gillig, J.R., Schwad, J.M. and Smith, J.L. 1996. Structure of a dehydratase-isomerase from the bacterial bathways for biosynthesis of unsaturated fatty acids> two catalytic activities in one active site. Structure 4:253-264.<br>
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[2]. Xu, P., Gu, G., Wang, L., Bower, A.G., Collins, C.H. and Koffas, M.A. 2013. Modular optimization of multi-gene pathways for fatty acids production in E.coli. Nature Communications. 4: (1409): 1-8. DOI: 10.1038/ncomms2425<br>
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<img src="https://static.igem.org/mediawiki/2013/0/07/Fabafig1.png" width="900" height="300"/>
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<p id="footer"><b>Figure 5: Predicted extinction of Sumatran Orangutan, if annual loss of rainforest coverage could be halved (1.18%) in 30 years, with an initial population size of 2520 individuals </b></p><br>
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[3]. Carson, M., Johnson, D.H., McDonald, H., Brouillette C. and Delucas, L.J. (2007) His-tag impact on structure. Acta Crystallogr D Biol Crystallogr. 63(3):295-301. <br>
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<p>We can see that lowering the rate of deforestation has a massive impact on the population of the Orangutan, with an estimated extinction date of 105 to 118 years as opposed to 45.</p>
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[4]. Chant, A., Kraemer-Pecore, C.M., Watkin, R. and Kneale G.G. 2005. Attachment of a histidine tag to the minimal zinc finger protein of the Aspergillus nidulans gene regulatory protein AreA causes a conformational change at the DNA-binding site. Protein Expr Purif. 39(2):152-9.<br>
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            <p><b><a id="Q4">Getting a working GROMACS simulation for FabA</a></b><br>
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<p>In conclusion, we can see that a decline in the rate of traditional methods of palm oil production would have a massive impact on the survival on the Sumatran Orangutan. Our estimates suggest that the tipping point is around 30 years from now - if we have not implemented a method to reduce traditional methods of palm oil by this time, we can expect to see an extinction of the Sumatran Orangutan. It is important to note that the actual date could be earlier than this, as detrimental effects to the population such as hunting has not be modelled.
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[5]. Freydank, A.C., Brandt, W. and Dräger, B. 2008. Protein structure modeling indicates hexahistidine-tag interference with enzyme activity. Proteins. 72(1):173-83. doi: 10.1002/prot.21905.<br>
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At these levels, programs such as rehabilitation of captive orangutans could have a dramatic impact on the survival rate of the Sumatran Orangutan.
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[6].  Klose, J., Wendt, N., Kubald, S., Krause, E., Fechner, K., Beyermann, M., Bienert, M., Rudolph, R. and Rothemund, S. 2004. Hexa-histidin tag position influences disulfide structure but not binding behavior of in vitro folded N-terminal domain of rat corticotropin-releasing factor receptor type 2a. Protein Sci. 13(9):2470-5. <br>
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For future work, we would like to investigate the effect that reformation of the land previously used for the palm oil industry would have in the event that our project would be able to stop deforestation completely.</p>
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            <p><b><a id="Q5">To His-tag or not to His-tag</a></b><br>
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All orangutans are going to die one day but we could definitely delay this happening.
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[7]. Woestenenk, E.A., Hammarström M., Van Den Berg, S., Härd, T. and Berglund, H. 2004 His tag effect on solubility of human proteins produced in Escherichia coli: a comparison between four expression vectors. J Struct Funct Genomics. <br>5(3):217-29.
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[1] Lacy, R.C. 2000. Structure of the VORTEX simulation model for population viability analysis. Ecological Bulletins 48:191-203
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[8]. Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M.R., Smith, J.C., Kasson, P.M., Van der Spoel, D., Hess, B., Lindahl, E., 2013. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics: 1–10.<br>
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[2] Marshall, A. J., Lacy, R., Ancrenaz, M., Byers, O., Husson, S. J., Leighton, M., Meijaard, E., Rosen, N., Singleton, I., Stephens, S., Traylor-Holzer, K., Utami Atmoko, S. S., van Schaik, C. P. and Wich, S. A. (2009) Orangutan population biology, life history and conservation. In: Wich, S. A., Utami Atmoko, S. S., Mitra Setia, T., and van Schaik, C. P. (Eds.) 2009. Orangutans: Geographic variation in behavioral ecology and conservation. Oxford University Press. Pp. 311-326.
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[9]. Frigo, M., Johnson, S.G., 2005. The Design and Implementation of FFTW3. Proceedings of the IEEE 93: 216–231.<br>
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[10]. Hornak, V., Abel, R., Okur, A., 2006. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins: Structure, Function and Bioinformatics 65: 712–725.<br>
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[11]. Jorgensen, W.L., Chandrasekhar, J., Madura, J.D., Impey, R.W., Klein, M.L., 1983. Comparison of simple potential functions for simulating liquid water. The Journal of Chemical Physics 79: 926.<br>
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[12]. Berendsen, H., 1981. Interaction models for water in relation to protein hydration. Intermolecular Forces: 331–338<br>
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[13]. Kiwiel, K., Murty, K., 1996. Convergence of the steepest descent method for minimizing quasiconvex functions. Journal of Optimization Theory and Applications 89: 221–226.<br>
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[3] Wich; S., Utami-Atmoko. S., Setia, T., Rijksen, H., Schürmann, C., van Hooff, J. &  van Schaik, C. (2004). Life history of wild Sumatran orangutans (Pongo abelii) Journal of Human Evolution 47 (6): 385–398
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[14]. Darden, T., York, D., Pedersen, L., 1993. Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems. The Journal of Chemical Physics 98: 10089.<br>
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[15]. Karttunen, M., Rottler, J., Vattulainen, I., Sagui, C., 2008. CHAPTER 2 Electrostatics in Biomolecular Simulations  : Where Are We Now and Where Are We Heading  ? Current Topics in Membranes 60: 49–89.<br>
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[16]. Hess, B., Bekker, H., Berendsen, H.J.C., Fraaije, J.G.E.M., 1997. LINCS: A linear constraint solver for molecular simulations. Journal of Computational Chemistry 18: 1463–1472.<br>
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[17]. Bussi, G., Donadio, D., Parrinello, M., 2007. Canonical sampling through velocity rescaling. The Journal of Chemical Physics 126: 014101.<br>
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[18]. Parrinello, M., 1981. Polymorphic transitions in single crystals: A new molecular dynamics method. Journal of Applied Physics 52: 7182.<br>
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[19]. Lindahl, E., Hess, B., Spoel, D. Van Der, 2001. GROMACS 3.0: a package for molecular simulation and trajectory analysis. Molecular Modeling Annual: 306–317.<br>
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[20] J. Lemkul, Department of Biochemistry, Virginia Tech. http://www.bevanlab.biochem.vt.edu/Pages/Personal/justin/gmx-tutorials/lysozyme/. [Last Accessed: 27/09/2013]<br> </p>
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Safety

Introduction
As part of our research into the ethical aspects of our project, we have modelled the future population of Sumatran Orangutan in a variety of situations; including with and without our project being implemented, and reflecting the uncertainty found in the economical research we compiled as part of this project as to how much we can replace traditional methods of Palm Oil production. This involved the use of the program Vortex: “a Monte Carlo simulation of the effects of deterministic forces as well as demographic, environmental, and genetic stochastic events on wild population”[1]. Using this, we were able to run 200 iterations of each scenario, giving us a series of results based on the probability of a number of chance events, including catastrophes (eg. fires, landslides), inbreeding, litter frequencies and sex. This lead to the production of a variety of outcomes based on the occurrences of these chance events, the majority of which adhere to a general trend around a mean. We decided to base our model on the Sumatran Orangutan, due to their smaller population size. Restricted to the island of Sumatra in Western Indonesia, this species is at threat due to mass deforestation, a large amount of which occurs as a direct result of the Palm Oil industry. As the majority of the remaining orangutans are concentrated in a relatively small area in the north of the island, we treated this as a single population. We also chose Sumatra as it is home to a number of other endangered animals, included the critically endangered Sumatran Tiger and Sumatran Rhino, which are facing the same threats, and potentially the same future, as the Sumatran Orangutan. The aim of this aspect of our modelling ventures is to validate the need for our project, and set an overall deadline as to when our project must be put into action in order to save the Sumatran Orangutan.

Method
The building of the baseline for our orangutan model used methods previously outlined in the paper Orangutan population biology, life history, and conservation[2], and we adapted this to represent current orangutans population levels and deforestation levels, as well as the effects of our project. Average age of first reproduction has been established to be 15 years for female Sumatran Orangutan and 25 years for male Sumatran Orangutans. No menopause has been recorded in the Sumatran Orangutan, and therefore Sumatran Orangutans were presumed to continue to produce litters until towards the end of the Orangutans lifespan, at 50 years of age(Orangutans in the wild are thought to potentially live up to several decades, but the oldest recorded lifespan is 55 years)[3]. Density-dependant effects on reproduction were modelled using the same growth curve found in Lacy (2009), where , where P0 =18.2, Pk = 11.1, A = 1, B=2 and N = initial population size. We modelled the effect of deforestation using time variable carry capacity: where K is carry capacity and P is the present population.

Result
Using the methods described previously, we were able to model a variety of scenario and monitor the population sizes of the Orangutan throughout them. We began by establishing when we can expect to see an extinction of the orangutan based on deforestation data collected previously as part of our bioethics research. This data put the extinction timeline for the Sumatran Orangutan to be around 43 - 45 years. All models ran to extinction. This is a slightly earlier estimation than previous explorations into the potential extinction of the Orangutan, which is likely to be because of an increase in the rate of deforestation over the past few years, fueled by palm oil and biofuel targets, although most models so should dangerously low populations levels in 40 years time [2].


Using this simulation, estimates of the Orangutan population at set time periods in the future were created. Then, a variety of scenarios made possible by the implementation of our project were created. To begin with, we modelled the effect that a complete end to further deforestation, which is an potentially achieve outcome of our project. This time frame for this occurring is difficult to predict, so we began by looking at what we could expect if the implementation of our project takes a further 40 years. Our best case scenarios from the previous model suggested that in 40 years time we will be expecting Sumatran Orangutan population to be around 650 individuals. Therefore, we modelled a population of orangutans at this number for another 100 years, presuming that deforestation had stopped, but that the previous land that had been used for the production of palm oil is not being reconverted to land viable for orangutan use (for example, the land being too drained of nutrients to support rainforest, being converted to the production of another crop, or even continuing to be used for palm oil production, in order to support global demand which may never be able to be abolished - more information on these likely scenarios is available on our economics and bioethics pages, found here LINK TO ETHICS PAGE).


We can see from these simulations that Orangutans at this small a population are unable to recover from the effect that palm oil has had, or even reach carrying capacity in the land with has been left to them. This is likely to be due to a mixture of the sparse population density of the Orangutan leading to lower frequencies of mating, and inbreeding of the remaining population. In addition, there are possibilities that the population of the Orangutans could drop to dangerously low levels, below 200 individuals, where a chance event such as flooding and landslides could erase the remainder of the population, meaning that we could still see an extinction of the Sumatran Orangutan within the next 140 years. We also ran simulations for if certain events occurred in 30 years, rather than 40 years, time. As our simulations for this time period showed more variance, we modelled both a best case (2520 individuals remaining) and worst case (1664 individuals remaining) scenario.



From Figure 3 and 4, we can see that in most cases, orangutan population levels remain at a viable level, although in certain instances, struggling to maintain a population at carry capacity. However, it would seem that if we were able to implement our project with a 30 year time frame, the Orangutan may be able to maintain a healthy population size. However, due to the giant demand for Palm Oil demonstrated during our economic investigations, it is unlikely that we will ever be able to entirely replace the palm oil industry. Therefore, we also modelled the Orangutan populations at this critical 30 year point if we were able to lower annual rainforest loss by a half.


We can see that lowering the rate of deforestation has a massive impact on the population of the Orangutan, with an estimated extinction date of 105 to 118 years as opposed to 45.

Getting a working GROMACS simulation for FabA

In conclusion, we can see that a decline in the rate of traditional methods of palm oil production would have a massive impact on the survival on the Sumatran Orangutan. Our estimates suggest that the tipping point is around 30 years from now - if we have not implemented a method to reduce traditional methods of palm oil by this time, we can expect to see an extinction of the Sumatran Orangutan. It is important to note that the actual date could be earlier than this, as detrimental effects to the population such as hunting has not be modelled. At these levels, programs such as rehabilitation of captive orangutans could have a dramatic impact on the survival rate of the Sumatran Orangutan. For future work, we would like to investigate the effect that reformation of the land previously used for the palm oil industry would have in the event that our project would be able to stop deforestation completely.

To His-tag or not to His-tag
All orangutans are going to die one day but we could definitely delay this happening. [1] Lacy, R.C. 2000. Structure of the VORTEX simulation model for population viability analysis. Ecological Bulletins 48:191-203 [2] Marshall, A. J., Lacy, R., Ancrenaz, M., Byers, O., Husson, S. J., Leighton, M., Meijaard, E., Rosen, N., Singleton, I., Stephens, S., Traylor-Holzer, K., Utami Atmoko, S. S., van Schaik, C. P. and Wich, S. A. (2009) Orangutan population biology, life history and conservation. In: Wich, S. A., Utami Atmoko, S. S., Mitra Setia, T., and van Schaik, C. P. (Eds.) 2009. Orangutans: Geographic variation in behavioral ecology and conservation. Oxford University Press. Pp. 311-326. [3] Wich; S., Utami-Atmoko. S., Setia, T., Rijksen, H., Schürmann, C., van Hooff, J. & van Schaik, C. (2004). Life history of wild Sumatran orangutans (Pongo abelii) Journal of Human Evolution 47 (6): 385–398