title: 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.
authors: Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA, USA. senger@vt.edu
authors: Ryan S
authors: RS
authors: Senger
authors: Hadi
authors: H
authors: Nazem-Bokaee
mesh_terms: D000255:Adenosine Triphosphate / Q000378:metabolism; D000465:Algorithms; D050260:Carbohydrate Metabolism; D046969:Clostridium acetobutylicum / Q000235:genetics / Q000254:growth & development / Q000378:metabolism*; D004926:Escherichia coli / Q000235:genetics / Q000254:growth & development / Q000378:metabolism*; D016680:Genome, Bacterial; D008957:Models, Genetic; D010100:Oxygen / Q000378:metabolism; D012984:Software
chemical_list: D000255:Adenosine Triphosphate; D010100:Oxygen