Abstract
The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments.
Highlights
Improving understanding of metabolic behaviour is vital to drive research outcomes in medical and biotechnology fields
Whilst these models allow for dynamic simulations and control analysis, their coverage does not extend far beyond the glycolytic pathway: they typically contain less than 20 reactions, which is, by far, not enough to understand the global dynamics of metabolism
Workflow for Model Construction We developed a workflow for systematically converting metabolic reconstructions into large-scale kinetic models of metabolism
Summary
Improving understanding of metabolic behaviour is vital to drive research outcomes in medical and biotechnology fields. For the well-studied model organism Saccharomyces cerevisiae (baker’s yeast), many kinetic models are available in BioModels Database [1] Whilst these models allow for dynamic simulations and control analysis, their coverage does not extend far beyond the glycolytic pathway: they typically contain less than 20 reactions, which is, by far, not enough to understand the global dynamics of metabolism. Larger models such as the yeast consensus model [2] contain more than 1000 reactions, but merely define the stoichiometry of the metabolic network [3], which can only be studied using techniques such as Elementary Mode Analysis [4] or Flux Balance Analysis (FBA) [5]. Ways to circumvent the lack of data, by looking at network reaction control under parameter uncertainty, have been developed [7,8,9,10], but these techniques do not provide explicit kinetic solutions to the system
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