Difference between revisions of "Team:UCL/Model/Larginine"

Why we chose to develop a model of the arginine pathway

From a biological point of view, arginine constitutes about 5% of proteins in E. coli and is an important element of many processes (1)(for more details on biology of arginine please visit our project website. We chose to model the arginine part of the project because the dynamics of the inhibitory feedback loop and the effects of its perturbation are hard to intuitively predict based just on the knowledge of the pathway, but at the same time are not hard to model. This model was intended to help us to formalise analysis of the pathway during our experiments, to help us to describe the involvement of individual regulatory mechanisms in the pathway and to predict the quantity of arginine produced by E. coli strains with various modifications in the arginine production pathway.

We found several published models of the arginine pathway. The focus of the first model (2) was gene expression - in particular promoter activity of the elements of the arginine pathway - as a function of bacterial growth. The authors argue that the level of gene expression is a result of two mechanisms: specific (the pathway) and global. Two principles have been proposed:
1) During metabolic steady state, repression of transcription dominates. Under stable conditions the promoter activity never reaches its full activity.
2) Maximum promoter activity depends on global regulation of growth and metabolism: promoter activity peaks only during transient adaptation in response to dynamic changes – this is linked to the global regulation of the promoter activity.
What was important to keep in mind when developing our model is that the repressive activity varied at most three-fold amongst the seven targets of argR present in the pathway, and that self-repression of argR was ten-fold weaker. Additionally, the observed enzyme concentrations followed the just-in-time rule, according to which concentrations of enzymes decrease along the pathway, and thus the observed concentrations were: argA > argCBH > argD > argE > argF > argG (with argI being the exception).

The drawback was that the published code of this model contained only the argA and argR parameters, whereas we were interested in understanding the whole arginine pathway. However, in the future, we could use the model to include global regulatory mechanisms in our model: the experimental modifications we planned are likely to affect the growth rate of the bacteria and thus our model may deviate from the experimental data because of not taking the effects of the global machinery on promoter activity into account.

The second model (3) is a mathematical description of the arginine pathway and its cross-section with the pyrimidine pathway. The model is divided into three segments where each segment contains only one regulatory step, whereas other steps are considered non-rate limiting and not linked to other pathways.

In comparison to the second model, we wanted our model to include more steps of the arginine pathway and exclude those elements of the pyrimidine pathway which do not converge with the arginine pathway. However, the publication was a useful guide when it comes to estimating parameters of our model.

Because of the reasons mentioned above and because we wanted to have a simplified model which focuses on the part of the arginine pathway we were going to modify in the lab, which models the pathway at a protein rather than gene level and which is easy to use by scientists without an expertise in modelling, we decided to develop our own model. We chose to build our model using Simbiology because of its user-friendly interface with a number of build-in options for testing and further use of the model.

References

1. Charlier, D., and Glansdorff, N. (2004) in Biosynthesis of Arginine and Polyamines (Bock, A., Curtiss, R., Kaper, J. B., Neidhardt, F. C., Nystrom, T., Rudd, K. E., and Squires, C. L., eds) module 3.6.1.10, American Society for Microbiology, Washington, DC.
2. Gerosa, L., Kochanowski, K., Heinemann, M. and Sauer, U. (2013) ‘Dissecting specific and global transcriptional regulation of bacterial gene expression.’, Molecular systems biology. European Molecular Biology Organization, 9, p. 658. doi: 10.1038/msb.2013.14.
3. Caldara, M., Dupont, G., Leroy, F., Goldbeter, A., De Vuyst, L. and Cunin, R. (2008) ‘Arginine Biosynthesis in Escherichia coli: experimental perturbation and methematical modeling’, Journal of Biological Chemistry. American Society for Biochemistry and Molecular Biology, 283(10), pp. 6347–6358. doi: 10.1074/jbc.M705884200.

Overview of the model

As you can see in the diagram below (Fig. 1), our model describes the metabolic pathway in which arginine is obtained from glutamine and glutamate acetyl-CoA, and includes the inhibitory feedback loops. The green species are the metabolites of the pathway, the yellow circles the reactions producing them. The blue species are the enzymes and enzyme repressor complexes at each stage of the reaction, the red circles represent the repression. The orange species is argR, which is responsible for most of the repression.

Dashed lines were used to indicate species which do not get used up in the reactions, e.g. enzymes, and are both reactants and products of the reactions. Reverse arrows indicate reversible reactions and closed-circle arrows indicate that species are replenished and kept at a constant level.

The reactions in the pathway are all modelled with Michaelis-Menten kinetics, while the repression reactions are modelled using reversible mass action.

Figure 1. Overview of the model.

---