Integration of proteomics and gene expression into genome-scale cellular models

This project seeks to address one of the great challenges of biology: to construct an integrated computational model of a biological cell. The past decade has witnessed the construction of large or genome-scale network-based models of cellular metabolism for a number of biological organisms. These approaches however remain largely static, i.e. they do not permit the modelling of dynamic, time-dependent processes. On the other hand, small-scale dynamic models have been developed, using experimentally determined rate equations and parameters. To expand such models to large scales remains challenging due to heavy and error-prone procedures for model construction and the need for expensive and time-consuming experimental measurements.

We are developing the GRaPe software to bridge the gap between these two approaches and enable fast, efficient construction of large-scale dynamic models. GRaPe automatically generates large systems of generic kinetic equations to model enzymatic reactions and estimates parameters from time series of experimental data. However, to create an integrative cellular model requires the mRNA and protein dynamics to be integrated with the metabolic model. In this project, you will work on the integration of quantitative proteomics data into the existing GRaPe framework. This work will be carried out in close collaboration with an existing team, the COPY LoLA project, which aims to fully quantify the proteome of the model organism Saccharomyces cerevisiae based on the QconCAT approach. To test and validate the constructed models, you will apply them to analyse the role of differently regulated isoenzymes in the regulation of the stress response in yeast. We can also directly validate these predictions in the lab.

This is a fundamentally “dry” computational project requiring knowledge of Java programming, but the project also offers multiple opportunities to interact with “wet” lab scientists and acquire expertise in high-throughput “omics” technologies and data analysis.