A bilevel optimization algorithm to identify enzymatic capacity constraints in metabolic networks

Abstract

Constraint-based models of metabolism seldom incorporate tight bounds, or capacity constraints, on intracellular fluxes due to the lack of experimental data. This can sometimes lead to inaccurate growth phenotype predictions. Meanwhile, other forms of data such as fitness profiling data from growth competition experiments have been demonstrated to contain valuable information for elucidating key aspects of the underlying metabolic network. Hence, the optimal capacity constraint identification (OCCI) algorithm is developed to reconcile constraint-based models of metabolism with fitness profiling data by identifying a set of flux capacity constraints that optimally fits a wide array of strains. OCCI is able to identify capacity constraints with considerable accuracy by matching 1155 in silico-generated growth rates using a simplified model of Escherichia coli central carbon metabolism. OCCI is expected to be a useful tool for integrating high-throughput fitness measurements with constraint-based models for elucidating metabolic network capacities.