Team:Edinburgh/Modelling

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<p class="h1">Modelling</p>
<p class="h1">Modelling</p>
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Since we come from different backgrounds (our [[Team:Edinburgh/Team | team]] consists of biologists, engineers, a designer, a social scientist and a computer scientist), our views on modelling differ. Each of us would prefer approaches and techniques close to their home disciplines.
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One way of assessing the feasibility of the <span class="hardword" id="synergy">synergistic</span> approach to biorefineries is to use <span class="hardword" id="insilico">in silico</span> modelling. In particular, we would like to confirm that synergistic use of enzymes can make the process of <span class="hardword" id="cellulose">cellulose</span> degradation more efficient.
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We thought that we could turn our interdisciplinarity into our strength in modelling. Therefore, while collaborating, we tried to approach modelling from different perspectives:
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Some other models, calculations, and tools were also developed by the team.
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# Deterministic modelling - MATLAB
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==Cellulase models==
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# stochastic rule-based modelling (Kappa and Spatial Kappa)
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# C
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Additionally, we have developed other models / tools:
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===Approaches===
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# modelling phage dynamics
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As it happens, our [[Team:Edinburgh/Team | team]] includes:
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# artificial selection
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# genetic stability
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== Comparison of different modelling tools ==
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# Two engineers experienced in using MATLAB.
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Within the reactor tasked with degrading cellulose into glucose in the biorefinery, temperature, enzyme concentration, substrate reactivity as well as xylose, cellobiose and glucose inhibition all govern the amount of glucose product. Deterministic modelling using a set of ordinary differential equations highlights the essential kinetic relationship among the enzymes, exo/endo-glucanase and &beta;-glucosidase. By solving these governing equations using the numerical tool MATLAB the level of degradation is qualitatively predicted.
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# An informatician who quickly learned the Kappa modelling language.
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# A biologist who, for no good reason, knows the C programming language.
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However, we found that the equations we used for the deterministic modelling only gave sensible answers when the model parameters remained within certain limits. Outside those limits, results could be physically impossible; e.g. producing negative amounts of cellobiose.
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This led to three different attempts to model <span class="hardword" id="cellulase">cellulase</span> action.
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As an alternative, stochastic models were created using the Kappa language tool. These incorporate indeterminacy in the evolution of the state of the system. Rules are defined which describe how the model moves from one state to the next.
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===Results===
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== What ''is'' modelling? ==
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* [[Team:Edinburgh/Synergy (C model)|C model]] &mdash; a simple model that showed a difference between synergistic and non-synergistic systems
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* [[Team:Edinburgh/Synergy (Kappa model)|Kappa model]] &mdash; a more complex model that also showed a difference
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* [[Team:Edinburgh/Synergy (MATLAB model)|MATLAB model]] &mdash; the most complex model, only worked well for the non-synergistic system
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Modelling is, in our understanding, vital to the future of Synthetic Biology. Its function is to '''allow you to design''' biological circuits, systems etc. '''without having to go into a lab'''. This lets the designer '''abstract away''' from biological representations and think at a more abstract level.
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===Comparison of different modelling tools===
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Right now how things work is:
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Our analysis of the advantages and disadvantages of the various modelling approaches, together with conclusions, are on our [[Team:Edinburgh/Model Comparison | model comparison]] page.
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# you get an idea
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# you go to a lab and see whether it works
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How it might work in the future:
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==Other models and calculations==
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# you get an idea
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# you model the behaviour you want to achieve and see if the system ''might'' work
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# you go to a lab and see how it works in reality
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# you improve your idea, back to step 2...
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Compare a biologist to an aircraft designer: the latter has far more tools to simulate the behaviour of their product, before having to build a costly prototype, the former has to perform many experiments, which might not cost much, but are time-consuming and tedious.
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===Energy efficiency===
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==Synergy vs. non-synergy==
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Consider this: for bacteria to produce <span class="hardword" id="phage">phage</span> or <span class="hardword" id="inp">INP</span> requires energy. This energy could have been spent producing extra copies of the cellulases. In order for the phage and cell display projects to make sense, the benefits of synergy must outweigh the cost of producing all these extra proteins.
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Does the whole theory behind our projects - synergy - even work? What if enzymes were just secreted into the media instead?
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We can create ordinary differential equations, to compare and contrast synergy with the enzymes attached onto the cell, the phage and without (non-synergy). This would involve creating a control. We could assume date/results however the whole point of mathematical modelling is to see if using synergy is better than the status quo.
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As engineers we have done matlab to 'death', so probably the best programme to use especially for the mathematical modelling i.e ODE's. Next step is to create equations, as well as function and script files. So when we have data its just a point of plugging it in. Important point to note, the possibility of it being stochastic i.e. the behaviour is non-deterministic...
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==Energy efficiency==
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Is display of enzymes any good, even in theory?
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Consider this: for a bacteria to produce phage or INP requires energy. This energy could have been spent producing extra copies of the cellulases. In order for the phage and cell display projects to make sense, the benefits of synergy must outweigh the cost of producing all these extra proteins.
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This question can probably be investigated using [[Team:Edinburgh/Efficiency|simple maths and back-of-envelope calculations]]...
This question can probably be investigated using [[Team:Edinburgh/Efficiency|simple maths and back-of-envelope calculations]]...
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== Spatial modelling ==
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===Evolutionary analysis of cell-display vs. secretion===
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Since synergy is all about putting the right enzymes together in close proximity, we should model how the enzymes will move around in the medium. We can model expression of enzymes on INP and on M13 phages.
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One potential benefit of attaching enzymes to the cell surface rather than secreting them into the media is that any mutations that increase enzyme efficiency will specifically benefit the cell with the mutation, as the increased sugar yield will be physically present at the cell. The mutation will thus confer a fitness advantage, potentially allowing it to take over the culture.
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==Evolutionary analysis of cell-display vs. secretion?==
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By contrast, if a cell produces a ''secreted'' protein that is of higher efficiency, it will disperse and benefit random cells in the culture. To investigate this difference, we created our [[Team:Edinburgh/Artificial Selection|artificial selection]] model.
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This idea is broadly suggested by [http://www.springerlink.com/content/4l4m28lp06120253/ Van Zyl ''et al'' (2007)].
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One potential benefit of attaching enzymes to the cell surface rather than secreting them into the media is that any mutations that increase enzyme efficiency will specifically benefit the cell with the mutation, as the increased sugar yield will be physically present at the cell. The mutation will thus confer a fitness advantage, potentially allowing it to take over the culture.
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===Phage replication===
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By contrast, if a cell produces a ''secreted'' protein that is of higher efficiency, it will disperse and benefit random cells in the culture.
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The phage display system requires infected ''E. coli'' to dominate the system and not be outcompeted by uninfected ''E. coli''. Our [[Team:Edinburgh/Phage Replication|phage replication model]] verifies that this is indeed the expected outcome.
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==Synonymous codon usage to avoid recombination==
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==Genetic Stability Tool==
Our projects involve having multiple fusion proteins expressed, each of which uses a genetically identical carrier protein (e.g. ice-nucleation protein or the M13 pVIII gene). The presence of repeated sequences in DNA (i.e. the same sequence in multiple locations) can lead to genetic instability.
Our projects involve having multiple fusion proteins expressed, each of which uses a genetically identical carrier protein (e.g. ice-nucleation protein or the M13 pVIII gene). The presence of repeated sequences in DNA (i.e. the same sequence in multiple locations) can lead to genetic instability.
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To combat this, it ought to be possible to design and synthesise different versions of the genes that code for the same amino acids but use different codons and so are as distinct as possible. This could be investigated by computer.
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However, it ought to be possible to design and synthesise different versions of the genes that code for the same amino acids but use different codons and so are as distinct as possible.
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Hooray a solution is [[Team:Edinburgh/Genetic instability | here]]!
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To prove this, we made our [[Team:Edinburgh/Genetic instability | Genetic Stability Tool]].
==References==
==References==

Latest revision as of 08:20, 12 October 2011

Modelling

One way of assessing the feasibility of the synergistic approach to biorefineries is to use in silico modelling. In particular, we would like to confirm that synergistic use of enzymes can make the process of cellulose degradation more efficient.

Some other models, calculations, and tools were also developed by the team.

Contents

Cellulase models

Approaches

As it happens, our team includes:

  1. Two engineers experienced in using MATLAB.
  2. An informatician who quickly learned the Kappa modelling language.
  3. A biologist who, for no good reason, knows the C programming language.

This led to three different attempts to model cellulase action.

Results

  • C model — a simple model that showed a difference between synergistic and non-synergistic systems
  • Kappa model — a more complex model that also showed a difference
  • MATLAB model — the most complex model, only worked well for the non-synergistic system

Comparison of different modelling tools

Our analysis of the advantages and disadvantages of the various modelling approaches, together with conclusions, are on our model comparison page.

Other models and calculations

Energy efficiency

Consider this: for bacteria to produce phage or INP requires energy. This energy could have been spent producing extra copies of the cellulases. In order for the phage and cell display projects to make sense, the benefits of synergy must outweigh the cost of producing all these extra proteins.

This question can probably be investigated using simple maths and back-of-envelope calculations...

Evolutionary analysis of cell-display vs. secretion

One potential benefit of attaching enzymes to the cell surface rather than secreting them into the media is that any mutations that increase enzyme efficiency will specifically benefit the cell with the mutation, as the increased sugar yield will be physically present at the cell. The mutation will thus confer a fitness advantage, potentially allowing it to take over the culture.

By contrast, if a cell produces a secreted protein that is of higher efficiency, it will disperse and benefit random cells in the culture. To investigate this difference, we created our artificial selection model.

Phage replication

The phage display system requires infected E. coli to dominate the system and not be outcompeted by uninfected E. coli. Our phage replication model verifies that this is indeed the expected outcome.

Genetic Stability Tool

Our projects involve having multiple fusion proteins expressed, each of which uses a genetically identical carrier protein (e.g. ice-nucleation protein or the M13 pVIII gene). The presence of repeated sequences in DNA (i.e. the same sequence in multiple locations) can lead to genetic instability.

However, it ought to be possible to design and synthesise different versions of the genes that code for the same amino acids but use different codons and so are as distinct as possible.

To prove this, we made our Genetic Stability Tool.

References

  • Van Zyl WH, Lynd LR, Den Haan R, McBride EJ (2007) [http://www.springerlink.com/content/4l4m28lp06120253/ Consolidated bioprocessing for bioethanol production using Saccharomyces cerevisiae]. Advances in Biochemical Engineering/Biotechnology 108: 205-235 (doi: 10.1007/10_2007_061).

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