Abstract
BackgroundGenome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic phenotype.ResultsWe develop DynamicME, an algorithm enabling time-course simulation of cell metabolism and protein expression. DynamicME correctly predicted the substrate utilization hierarchy on a mixed carbon substrate medium. We also found good agreement between predicted and measured time-course expression profiles. ME models involve considerably more parameters than metabolic models (M models). We thus generate an ensemble of models (each model having its rate constants perturbed), and then analyze the models by identifying archetypal time-course metabolite concentration profiles. Furthermore, we use a metaheuristic optimization method to calibrate ME model parameters using time-course measurements such as from a (fed-) batch culture. Finally, we show that constraints on protein concentration dynamics (“inertia”) alter the metabolic response to environmental fluctuations, including increased substrate-level phosphorylation and lowered oxidative phosphorylation.ConclusionsOverall, DynamicME provides a novel method for understanding proteome allocation and metabolism under complex and transient environments, and to utilize time-course cell culture data for model-based interpretation or model refinement.
Highlights
Genome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic phenotype
Objectives and outline of this study Here, we develop a method to simulate the cellular dynamics of metabolism, protein expression, and macromolecular composition in response to environmental changes
We found that DynamicME correctly predicted substrate utilization hierarchy under a five-carbon mixed substrate medium
Summary
Genome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic phenotype. Today’s genomescale models of E. coli account for over 2,000 metabolic reactions, and over 4,000 steps involved in the macromolecular expression machinery [2,3,4]. Recent studies have been approaching the classic problem of understanding the mechanisms and Genome-scale modeling of cell metabolism Computing the genotype-phenotype relationship is a fundamental challenge for computational biologists. Constraint-based reconstruction and analysis (COBRA) provides one approach for systems-level computation of biological networks using genome-scale biochemical network reconstructions [5]. Flux Balance Analysis (FBA) [6] in particular simulates flux distributions through a metabolic network by optimizing a cellular objective, such as maximizing growth rate subject to physicochemical, regulatory and environmental constraints.
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