Resolving Cell Composition Through Simple Measurements, Genome-Scale Modeling, and a Genetic Algorithm

Abstract

The biochemical composition of a cell is very complex and dynamic. It varies greatly among different organisms and environmental conditions. Inclusion of proper cell composition data is critical for accurate genome-scale metabolic flux modeling using flux balance analysis (FBA). However, determining cell composition experimentally is currently time-consuming and resource intensive. In this chapter, a method for predicting cell composition using a genome-scale model and "easy to measure" culture data (e.g., glucose uptake rate, and specific growth rate) is presented. The method makes use of a genetic algorithm for nonlinear optimization of a biomass equation (a mathematical description of cell composition). As a case study, the method was used to optimize a biomass equation for Escherichia coli MG1655 under multiple growth environments. The availability of experimentally determined (13)C flux data allowed a direct comparison with FBA predicted fluxes through the TCA cycle. Results showed dramatic improvement upon optimization of the biomass equation. In a second case study, biomass equation optimization was also applied to Clostridium acetobutylicum, an organism with less available biochemical cell composition data in the literature. The method produced a biomass equation highly similar to one determined experimentally for the closely related Gram-positive Bacillus subtilis.