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Team:Newcastle/Modelling/Introduction
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<table id="toc" class="toc"><tr><td><div id="toctitle"><h2>Contents</h2></div> | <table id="toc" class="toc"><tr><td><div id="toctitle"><h2>Contents</h2></div> | ||
<ul> | <ul> | ||
| - | <li | + | <li><a href="https://2013.igem.org/Team:Newcastle/Modelling/BioNetGen" target="_body"><span class="toctext">BioNetGen</span></a> |
<ul> | <ul> | ||
| - | <li | + | <li><a href="https://2013.igem.org/Team:Newcastle/Modelling/CellShapeModel"><span class="toctext">Cell Shape</span></a> |
<ul> | <ul> | ||
| - | <li | + | <li><a href="https://2013.igem.org/Team:Newcastle/Modelling/Cell_Fusion"><span class="toctext">Cell Fusion</span></a> |
| - | <li | + | <li><a href="https://2013.igem.org/Team:Newcastle/Modelling/Hbsu_Fusion_Protein"><span class="toctext">HBsu-xFP</span></a> |
| - | <li | + | <li><a href="https://2013.igem.org/Team:Newcastle/Modelling/L-form_Switch"><span class="toctext">L-form Switch</span></a> |
</ul> | </ul> | ||
</li> | </li> | ||
</ul> | </ul> | ||
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=Introduction to Modelling= | =Introduction to Modelling= | ||
<p> | <p> | ||
| - | Synthetic Biology aims to design and create novel biological systems. However the genetic systems | + | Synthetic Biology aims to design and create novel biological systems. However the genetic systems involved are very complex. Before going into the lab one must first have a reasonable approximation of what they expect to happen. When building a car, you have every single piece mapped out, and know exactly what should happen. Indeed, Synthetic Biology attempts to incorporate and apply engineering principles to biology.</p> |
<p>To achieve this it is common practice to model the behaviour of the biological system of interest before building it. Whilst fairly rudimentary, a simplified model can inform the design process, providing results otherwise very difficult to predict without actually doing the experiments. The effects of slight variations can be measured and accounted for, and we can generate hypothesis about the behaviour of the system, and modify our protocols accordingly. | <p>To achieve this it is common practice to model the behaviour of the biological system of interest before building it. Whilst fairly rudimentary, a simplified model can inform the design process, providing results otherwise very difficult to predict without actually doing the experiments. The effects of slight variations can be measured and accounted for, and we can generate hypothesis about the behaviour of the system, and modify our protocols accordingly. | ||
| - | However modelling is not perfect. Simplifications and assumptions must regularly be made. Furthermore it can be difficult to find the parameters necessary to model a system, | + | However modelling is not perfect. Simplifications and assumptions must regularly be made. Furthermore it can be difficult to find the parameters necessary to model a system, e.g. initial species concentrations or reaction rates. Even when the data is there it can be very time consuming to find, or only be available for a few model organisms. Furthermore the interactions within cells are so complex that we cannot pretend to understand them, let alone model them. Therefore a model can only represent some of the molecular interactions that occur, especially as many species will be ignored for the sake of simplicity.</p> |
| - | <p>Despite this it is often possible to build a model that gives a rough approximation | + | <p>Despite this it is often possible to build a model that gives a rough approximation of the experimental results. As computers get more powerful and the body of research accumulates the outcomes become more reliable.</p> |
<p> Our modelling can be split into four sections: | <p> Our modelling can be split into four sections: | ||
*[https://2013.igem.org/Team:Newcastle/Modelling/CellShapeModel The prediction of how L-forms will grow in constrained shapes using Matlab]. | *[https://2013.igem.org/Team:Newcastle/Modelling/CellShapeModel The prediction of how L-forms will grow in constrained shapes using Matlab]. | ||
| - | * [https://2013.igem.org/Team:Newcastle/Modelling/Cell_Fusion A | + | * [https://2013.igem.org/Team:Newcastle/Modelling/Cell_Fusion A Java animation showing green/red fluorescent L-forms moving in a chamber and fusing, showing how we expected the colours to change]. |
* [https://2013.igem.org/Team:Newcastle/Modelling/Hbsu_Fusion_Protein The expression of HBsu-xFP at differing IPTG concentrations] | * [https://2013.igem.org/Team:Newcastle/Modelling/Hbsu_Fusion_Protein The expression of HBsu-xFP at differing IPTG concentrations] | ||
*[https://2013.igem.org/Team:Newcastle/Parts/l_form_switch#Modelling How our L-form switch BioBrick causes the loss of the cell wall]. | *[https://2013.igem.org/Team:Newcastle/Parts/l_form_switch#Modelling How our L-form switch BioBrick causes the loss of the cell wall]. | ||
Latest revision as of 18:49, 28 October 2013
Contents |
Introduction to Modelling
Synthetic Biology aims to design and create novel biological systems. However the genetic systems involved are very complex. Before going into the lab one must first have a reasonable approximation of what they expect to happen. When building a car, you have every single piece mapped out, and know exactly what should happen. Indeed, Synthetic Biology attempts to incorporate and apply engineering principles to biology.
To achieve this it is common practice to model the behaviour of the biological system of interest before building it. Whilst fairly rudimentary, a simplified model can inform the design process, providing results otherwise very difficult to predict without actually doing the experiments. The effects of slight variations can be measured and accounted for, and we can generate hypothesis about the behaviour of the system, and modify our protocols accordingly. However modelling is not perfect. Simplifications and assumptions must regularly be made. Furthermore it can be difficult to find the parameters necessary to model a system, e.g. initial species concentrations or reaction rates. Even when the data is there it can be very time consuming to find, or only be available for a few model organisms. Furthermore the interactions within cells are so complex that we cannot pretend to understand them, let alone model them. Therefore a model can only represent some of the molecular interactions that occur, especially as many species will be ignored for the sake of simplicity.
Despite this it is often possible to build a model that gives a rough approximation of the experimental results. As computers get more powerful and the body of research accumulates the outcomes become more reliable.
Our modelling can be split into four sections:
- The prediction of how L-forms will grow in constrained shapes using Matlab.
- A Java animation showing green/red fluorescent L-forms moving in a chamber and fusing, showing how we expected the colours to change.
- The expression of HBsu-xFP at differing IPTG concentrations
- How our L-form switch BioBrick causes the loss of the cell wall.
The HBsu-xFP and peptidoglycan synthesis were modelled using BioNetGen. Very few teams have used it, despite it being intuitive and fun. So we have made a tutorial video to encourage future iGEM teams to use the software.
