Analysis of optimality in natural and perturbed metabolic networks

TLDR

Flux balance analysis predicts steady‑state metabolic fluxes by assuming optimal growth, a premise that may not hold for engineered or non‑evolved bacterial strains. The study introduces MOMA to test whether knockout metabolic fluxes minimally adjust from the wild‑type configuration, aiming to improve predictions and shed light on evolutionary optimization. MOMA uses quadratic programming to locate the flux distribution nearest to the wild‑type state that satisfies gene‑deletion constraints. Experimental data show that MOMA predictions agree better with mutant fluxes and growth rates than FBA, confirming its utility for modeling perturbed metabolic networks.

Abstract

An important goal of whole-cell computational modeling is to integrate detailed biochemical information with biological intuition to produce testable predictions. Based on the premise that prokaryotes such as Escherichia coli have maximized their growth performance along evolution, flux balance analysis (FBA) predicts metabolic flux distributions at steady state by using linear programming. Corroborating earlier results, we show that recent intracellular flux data for wild-type E. coli JM101 display excellent agreement with FBA predictions. Although the assumption of optimality for a wild-type bacterium is justifiable, the same argument may not be valid for genetically engineered knockouts or other bacterial strains that were not exposed to long-term evolutionary pressure. We address this point by introducing the method of minimization of metabolic adjustment (MOMA), whereby we test the hypothesis that knockout metabolic fluxes undergo a minimal redistribution with respect to the flux configuration of the wild type. MOMA employs quadratic programming to identify a point in flux space, which is closest to the wild-type point, compatibly with the gene deletion constraint. Comparing MOMA and FBA predictions to experimental flux data for E. coli pyruvate kinase mutant PB25, we find that MOMA displays a significantly higher correlation than FBA. Our method is further supported by experimental data for E. coli knockout growth rates. It can therefore be used for predicting the behavior of perturbed metabolic networks, whose growth performance is in general suboptimal. MOMA and its possible future extensions may be useful in understanding the evolutionary optimization of metabolism.