Speaker
Description
Genomic sequencing data can be used to identify gene-protein-reaction (GPR) rules for microbial species, which determine how the genes corresponding to enzymes catalyse specific metabolic reactions. GPRs can thus be combined to create genome-scale metabolic models (GEMs) for any organism for which a genome sequence is available. Mathematically, GEMs are large stoichiometric matrices that can be used to predict which reactions are vital for their metabolism. GEMs are then used to construct kinetic models, which are often very large systems of ODEs. In this talk, I will describe how GEMs are constructed and how flux balance analysis (FBA) is used to calculate a biologically feasible combination of fluxes for an organism at steady state. Next, I will extend FBA to consider dynamic environments (dFBA) to describe scenarios such as growth in a bioreactor where resources are finite. This requires a quasi-steady-state approximation where the intracellular metabolism is assumed to be at steady state. dFBA has been applied with much success to model microbial metabolism, both in industry and research. In the remainder of this talk, I will present my own work on extending dFBA to consider variable objective functions, including how an organism’s cellular objective might change in response to a dynamic environment. Notably, this will include an algorithm for solving these extended problems efficiently, minimising the number of times the LP problem must be solved.
