Computational Modeling of Ostrecoccus Tauri Central Metabolism Based on Statistical Thermodynamics

Developing predictive metabolic model based on the fundamental principles of physics is very critical to understand how cell structure affects function and subsequently investigate cellular dynamics relevant for bioenergy application. New computational tools are needed for quantitative modeling of cell that predict metabolite levels in high throughput manner, characterize thermodynamics and kinetics of individual reactions and energetics requirement of most likely metabolic pathways. In this talk, I will discuss our newly developed ODE-based optimization approach based on statistical thermodynamics for developing a central metabolic model of Ostreococcus tauri, the smallest known photoautotrophic eukaryote. We used the maximum entropy production principle to derive fluxes through a central metabolic network of Ostreococcus tauri . The predicted metabolite concentrations produced from maximum entropy production rate solution are compared to those typically expected from experiment using a loss function from which post-translational regulation of enzymes is inferred. Subsequently, we re-optimize the system with the inferred regulation and determine optimal rate constants for the metabolic network from the metabolite concentrations and reaction fluxes. At the end, I will discuss biophysical insights obtained from the modeling of Ostreococcus tauri that are relevant for biofuels production.