Integrating Kinetic Model of E. coli with Genome Scale Metabolic Fluxes Overcomes Its Open System Problem and Reveals Bistability in Central Metabolism

Overflow metabolism refers to the production of seemingly wasteful by-products by cells during growth on glucose even when oxygen is abundant. Two theories have been proposed to explain acetate overflow in Escherichia coli – global control of the central metabolism and local control of the acetate pathway – but neither accounts for all observations. Here, we develop a kinetic model of E. coli metabolism that quantitatively accounts for observed behaviours and successfully predicts the response of E. coli to new perturbations. We reconcile these theories and clarify the origin, control, and regulation of the acetate flux. We also find that, in turns, acetate regulates glucose metabolism by coordinating the expression of glycolytic and TCA genes. Acetate should not be considered a wasteful end-product since it is also a co-substrate and a global regulator of glucose metabolism in E. coli. This has broad implications for our understanding of overflow metabolism.

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Model Balancing: A Search for In-Vivo Kinetic Constants and Consistent Metabolic States

Metabolites ◽

10.3390/metabo11110749 ◽

2021 ◽

Vol 11 (11) ◽

pp. 749

Author(s):

Wolfram Liebermeister ◽

Elad Noor

Keyword(s):

Kinetic Constants ◽

Omics Data ◽

Central Metabolism ◽

E Coli ◽

Metabolic Fluxes ◽

Metabolic States ◽

Wide Range ◽

Metabolite Concentrations ◽

Metabolic Models

Enzyme kinetic constants in vivo are largely unknown, which limits the construction of large metabolic models. Given measured metabolic fluxes, metabolite concentrations, and enzyme concentrations, these constants may be inferred by model fitting, but the estimation problems are hard to solve if models are large. Here we show how consistent kinetic constants, metabolite concentrations, and enzyme concentrations can be determined from data if metabolic fluxes are known. The estimation method, called model balancing, can handle models with a wide range of rate laws and accounts for thermodynamic constraints between fluxes, kinetic constants, and metabolite concentrations. It can be used to estimate in-vivo kinetic constants, to complete and adjust available data, and to construct plausible metabolic states with predefined flux distributions. By omitting one term from the log posterior—a term for penalising low enzyme concentrations—we obtain a convex optimality problem with a unique local optimum. As a demonstrative case, we balance a model of E. coli central metabolism with artificial or experimental data and obtain a physically and biologically plausible parameterisation of reaction kinetics in E. coli central metabolism. The example shows what information about kinetic constants can be obtained from omics data and reveals practical limits to estimating in-vivo kinetic constants. While noise-free omics data allow for a reasonable reconstruction of in-vivo kcat and KM values, prediction from noisy omics data are worse. Hence, adjusting kinetic constants and omics data to obtain consistent metabolic models is the main application of model balancing.

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Control and regulation of acetate overflow in Escherichia coli

10.1101/2020.08.18.255356 ◽

2020 ◽

Author(s):

Pierre Millard ◽

Brice Enjalbert ◽

Sandrine Uttenweiler-Joseph ◽

Jean-Charles Portais ◽

Fabien Létisse

Keyword(s):

Escherichia Coli ◽

Glucose Metabolism ◽

Kinetic Model ◽

Local Control ◽

Overflow Metabolism ◽

Central Metabolism ◽

Global Regulator ◽

E Coli ◽

By Products ◽

Acetate Overflow

AbstractOverflow metabolism refers to the production of seemingly wasteful by-products by cells during growth on glucose even when oxygen is abundant. Two theories have been proposed to explain acetate overflow in Escherichia coli – global control of the central metabolism and local control of the acetate pathway – but neither accounts for all observations. Here, we develop a kinetic model of E. coli metabolism that quantitatively accounts for observed behaviors and successfully predicts the response of E. coli to new perturbations. We reconcile these theories and clarify the origin, control and regulation of the acetate flux. We also find that, in turns, acetate regulates glucose metabolism by coordinating the expression of glycolytic and TCA genes. Acetate should not be considered a wasteful end-product since it is also a co-substrate and a global regulator of glucose metabolism in E. coli. This has broad implications for our understanding of overflow metabolism.

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EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model

Scientific Reports ◽

10.1038/srep39647 ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 18

Author(s):

Oliver Hädicke ◽

Steffen Klamt

Keyword(s):

Escherichia Coli ◽

Reference Model ◽

Model Organism ◽

Solution Space ◽

Scale Model ◽

Reference Network ◽

Central Metabolism ◽

E Coli ◽

A Genome ◽

Genome Scale

Abstract Genome-scale metabolic modeling has become an invaluable tool to analyze properties and capabilities of metabolic networks and has been particularly successful for the model organism Escherichia coli. However, for several applications, smaller metabolic (core) models are needed. Using a recently introduced reduction algorithm and the latest E. coli genome-scale reconstruction iJO1366, we derived EColiCore2, a model of the central metabolism of E. coli. EColiCore2 is a subnetwork of iJO1366 and preserves predefined phenotypes including optimal growth on different substrates. The network comprises 486 metabolites and 499 reactions, is accessible for elementary-modes analysis and can, if required, be further compressed to a network with 82 reactions and 54 metabolites having an identical solution space as EColiCore2. A systematic comparison of EColiCore2 with its genome-scale parent model iJO1366 reveals that several key properties (flux ranges, reaction essentialities, production envelopes) of the central metabolism are preserved in EColiCore2 while it neglects redundancies along biosynthetic routes. We also compare calculated metabolic engineering strategies in both models and demonstrate, as a general result, how intervention strategies found in a core model allow the identification of valid strategies in a genome-scale model. Overall, EColiCore2 holds promise to become a reference model of E. coli’s central metabolism.

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Essentiality of local topology and regulation in kinetic metabolic modeling

10.1101/806703 ◽

2019 ◽

Author(s):

Gaoyang Li ◽

Wei Du ◽

Huansheng Cao

Keyword(s):

Metabolic Networks ◽

Functional Organization ◽

Dynamic Regulation ◽

E Coli ◽

Metabolic Fluxes ◽

Regulation Of Metabolism ◽

Coupled Reactions ◽

Highly Correlated ◽

Genome Scale ◽

Local Topology

AbstractGenome-scale metabolic networks (GSMs) are mathematic representation of a set of stoichiometrically balanced reactions. However, such static GSMs do not reflect or incorporate functional organization of genes and their dynamic regulation (e.g., operons and regulons). Specifically, there are numerous topologically coupled local reactions through which fluxes are coordinated; and downstream metabolites often dynamically regulate the gene expression of their reactions via feedback. Here, we present a method which reconstructs GSMs with locally coupled reactions and transcriptional regulation of metabolism by key metabolites. The proposed method has outstanding performance in phenotype prediction of wild-type and mutants in Escherichia coli (E. coli), Saccharomyces cerevisiae (S. cerevisiae) and Bacillus subtilis (B. subtilis) growing in various conditions, outperforming existing methods. The predicted growth rate and metabolic fluxes are highly correlated with those experimentally measured. More importantly, our method can also explain the observed growth rates by capturing the ‘real’ (experimentally measured) changes in flux between the wild-types and mutants. Overall, by identifying and incorporating locally organized and regulated functional modules into GSMs, Decrem achieves accurate predictions of phenotypes and has broad applications in bioengineering, synthetic biology and microbial pathology.

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Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth

10.1101/2020.03.23.003236 ◽

2020 ◽

Cited By ~ 1

Author(s):

Colton J. Lloyd ◽

Jonathan Monk ◽

Laurence Yang ◽

Ali Ebrahim ◽

Bernhard O. Palsson

Keyword(s):

Amino Acids ◽

Growth Conditions ◽

Anaerobic Growth ◽

Metabolic Phenotype ◽

E Coli ◽

Scale Models ◽

Protein Components ◽

K 12 ◽

Genome Scale ◽

Prosthetic Groups

AbstractSustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated genome-scale models of metabolism and gene expression (ME-models) have the unique ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we use the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME model mostly agree with the standard biomass objective function used in models of metabolism alone (M models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of prebiotic amino acids in the proteins used to sustain anaerobic growth (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli. Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles.

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