Mitigating biomass composition uncertainties in flux balance analysis using ensemble representations

Abstract

The biomass equation is a critical component in genome-scale metabolic models (GEMs): it is used as the de facto objective function in flux balance analysis (FBA). This equation accounts for the quantities of all known biomass precursors that are required for cell growth based on the macromolecular and monomer compositions measured at certain conditions. However, it is often reported that the macromolecular composition of cells could change across different environmental conditions; the use of the same single biomass equation in FBA, under multiple conditions, is questionable. Thus, we first investigated the qualitative and quantitative variations of macromolecular compositions of three representative host organisms, Escherichia coli, Saccharomyces cerevisiae and Cricetulus griseus, across different environmental/genetic variations. While macromolecular building blocks such as DNA, RNA, protein, and lipid composition vary notably, variations in fundamental biomass monomer units such as nucleotides and amino acids are not appreciable. We further observed that while macromolecular compositions are similar across taxonomically closer species, certain monomers, especially fatty acids, vary substantially. Based on the analysis results, we subsequently propose a new extension to FBA, named "Flux Balance Analysis with Ensemble Biomass (FBAwEB)", to embrace the natural variation in selected components of the biomass equation. The current study clearly highlights that certain components of the biomass equation are very sensitive to different conditions, and the ensemble representation of biomass equation in the FBA framework enables us to account for such natural variations accurately during GEM-guided in silico simulations.