Team:Edinburgh/Modelling

Modelling

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

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, through sheer chance, knows the C programming language.

This led to three different attempts to model cellulase action.

Results

• C model
• Kappa model
• MATLAB model

Comparison of different modelling tools

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 β-glucosidase. By solving these governing equations using the numerical tool MATLAB the level of degradation is qualitatively predicted.

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.

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.

Synergy vs. non-synergy

Does the whole theory behind our projects - synergy - even work? What if enzymes were just secreted into the media instead?

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.

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...

Other models and calculations

Energy efficiency

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.

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

Evolutionary analysis of cell-display vs. secretion?

This idea is broadly suggested by Van Zyl et al (2007).

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.

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.

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.

Hooray a solution is here!

References

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