Integrating Flux Balance Analysis into Kinetic Models to Decipher the Dynamic Metabolism of Shewanella oneidensis MR-1

Abstract

Shewanella oneidensis MR-1 sequentially utilizes lactate and its waste products (pyruvate and acetate) during batch culture. To decipher MR-1 metabolism, we integrated genome-scale flux balance analysis (FBA) into a multiple-substrate Monod model to perform the dynamic flux balance analysis (dFBA). The dFBA employed a static optimization approach (SOA) by dividing the batch time into small intervals (i.e., ∼400 mini-FBAs), then the Monod model provided time-dependent inflow/outflow fluxes to constrain the mini-FBAs to profile the pseudo-steady-state fluxes in each time interval. The mini-FBAs used a dual-objective function (a weighted combination of “maximizing growth rate” and “minimizing overall flux”) to capture trade-offs between optimal growth and minimal enzyme usage. By fitting the experimental data, a bi-level optimization of dFBA revealed that the optimal weight in the dual-objective function was time-dependent: the objective function was constant in the early growth stage, while the functional weight of minimal enzyme usage increased significantly when lactate became scarce. The dFBA profiled biologically meaningful dynamic MR-1 metabolisms: 1. the oxidative TCA cycle fluxes increased initially and then decreased in the late growth stage; 2. fluxes in the pentose phosphate pathway and gluconeogenesis were stable in the exponential growth period; and 3. the glyoxylate shunt was up-regulated when acetate became the main carbon source for MR-1 growth.

Metabolic Resource Allocation in Individual Microbes Determines Ecosystem Interactions and Spatial Dynamics

“…What is unique to the dFBA formulation of COMETS is the implementation of additional environment-dependent constraints on these uptake/secretion fluxes. Upper bounds on uptake fluxes for the dFBA calculation are estimated based on a concentration-dependent saturating function, in analogy with Michaelis-Menten kinetics (Feng et al, 2012). Given an environmental concentration C m of m (in a given box), the upper bound to u m is given by the following saturation curve: …”

Multiscale Metabolic Modeling: Dynamic Flux Balance Analysis on a Whole-Plant Scale

“…a large number of kinetic parameters (Mahadevan et al, 2002;Hjersted and Henson, 2006;Oddone et al, 2009), in this study, SOA-based dFBA was computed by integrating a dynamic FPM. A similar approach has been presented by Feng et al (2012), who integrated FBA into a kinetic model to get insight into the dynamics of the metabolism of the unicellular Shewanella oneidensis. Another study applies dFBA by coupling a human whole-body, physiologically based, pharmacokinetic model with a hepatocyte stoichiometric model in order to investigate the effects of drug-or illness-induced changes in hepatic metabolism on a whole-body scale (Krauss et al, 2012).…”

Multiscale Model of Mycobacterium tuberculosis Infection Maps Metabolite and Gene Perturbations to Granuloma Sterilization Predictions

“…Dynamic CBM CBM that replaces the steady-state assumption with a pseudosteady-state assumption to capture changes in the system or the environment over time (49)(50)(51)(52).…”

“…Dynamic CBMs describe bacterial growth in changing environments by relaxing the steady-state assumption of CBMs and instead assuming pseudo-steady states over short time periods (49)(50)(51)(52). CBMs have been coupled with gene expression data to probe metabolic changes in M. tuberculosis in response to hypoxia (53) or with spatial models of competing bacterial species to determine bacterial ecosystem dynamics (54).…”