A mathematical model of metabolism and regulation provides a systems-level view of how Escherichia coli responds to oxygen

Abstract

The efficient redesign of bacteria for biotechnological purposes, such as biofuel production, waste disposal or specific biocatalytic functions, requires a quantitative systems-level understanding of energy supply, carbon, and redox metabolism. The measurement of transcript levels, metabolite concentrations and metabolic fluxes per se gives an incomplete picture. An appreciation of the interdependencies between the different measurement values is essential for systems-level understanding. Mathematical modeling has the potential to provide a coherent and quantitative description of the interplay between gene expression, metabolite concentrations, and metabolic fluxes. Escherichia coli undergoes major adaptations in central metabolism when the availability of oxygen changes. Thus, an integrated description of the oxygen response provides a benchmark of our understanding of carbon, energy, and redox metabolism. We present the first comprehensive model of the central metabolism of E. coli that describes steady-state metabolism at different levels of oxygen availability. Variables of the model are metabolite concentrations, gene expression levels, transcription factor activities, metabolic fluxes, and biomass concentration. We analyze the model with respect to the production capabilities of central metabolism of E. coli. In particular, we predict how precursor and biomass concentration are affected by product formation.

Parametric identifier of metabolic network associated to hydrogen production in Escherichia coli based on robust sliding-mode differentiation

“…The reduction in the number of reactions is performed assuming that substrates and products of consecutive reactions are in pseudo steady-state condition (Ederer et al 2014). This reduction algorithm should not be performed on those reactions where any relevant mechanism of enzyme regulation (inhibition or activation) appears.…”

“…Values of Gibbs free energy to perform this analysis were taken from EcoCyc database (Ederer et al 2014). This analysis allows to know which are the reactions that are thermodynamically feasible but must be verified with a metabolic flux analysis to ensure that the proposed reactions are carried out by the cell.…”

Mathematical Models of Microbial Growth and Metabolism: A Whole-Organism Perspective

“…Here, W , x, and u are all allowed to be functions of time t. Classic models tend to be characterised by p = 0 or p = 1 (i. e. they have little or no structuring in terms of the internal state), by contrast, in fine-grained systems biology or in silico models, p may be on the order of many thousands 25 .…”

“…Thus, the construction and analysis of models capable of describing the cell as a whole system is challenging. One good example is the model of bacterial metabolism, where the state variables are metabolite concentrations, gene expression levels, transcription factor activities, metabolic fluxes, and biomass concentration 25 . However, in many cases the aim is not to describe the whole organism but instead to focus on specific subsystems of the cell, such as the assembly of the Z-ring 37 , or the electron transport chains of mitochondria 51 and purple non-sulfur bacteria 52 .…”

“…At the heart lies a simple principle, expressed by the following equation: where α i is the fraction of molecular building blocks devoted to the synthesis of machinery of type i, r i is a regulatory law (or r-function, for short), and r Σ = i r i is a normalising constant which assures that α i ∈ [0, 1] and i α i = 1. This approach based on the perspective of "allocation" or "investment" has been gaining widespread acceptance in recent years, no doubt prompted by advances in high-throughput transcriptomics and proteomics which make this point of view seem almost self-evident [22][23][24][25] . The indexing in eqn (1) can be fine-grained, with i corresponding to one particular protein, or more coarse-grained, with i corresponding to a co-regulated class of proteins, all involved in a particular metabolic or physiological function.…”