Delving deeper : relating the behaviour of a metabolic system to the properties of its components using symbolic metabolic control analysis
Date
2018-11-28
Journal Title
Journal ISSN
Volume Title
Publisher
Public Library of Science
Abstract
High-level behaviour of metabolic systems results from the properties of, and interactions
between, numerous molecular components. Reaching a complete understanding of metabolic
behaviour based on the system’s components is therefore a difficult task. This problem
can be tackled by constructing and subsequently analysing kinetic models of metabolic
pathways since such models aim to capture all the relevant properties of the system components
and their interactions. Symbolic control analysis is a framework for analysing pathway
models in order to reach a mechanistic understanding of their behaviour. By providing algebraic
expressions for the sensitivities of system properties, such as metabolic flux or steadystate
concentrations, in terms of the properties of individual reactions it allows one to trace
the high level behaviour back to these low level components. Here we apply this method to
a model of pyruvate branch metabolism in Lactococcus lactis in order to explain a previously
observed negative flux response towards an increase in substrate concentration. With this
method we are able to show, first, that the sensitivity of flux towards changes in reaction
rates (represented by flux control coefficients) is determined by the individual metabolic
branches of the pathway, and second, how the sensitivities of individual reaction rates
towards their substrates (represented by elasticity coefficients) contribute to this flux control.
We also quantify the contributions of enzyme binding and mass-action to enzyme elasticity
separately, which allows for an even finer-grained understanding of flux control. These analytical
tools allow us to analyse the control properties of a metabolic model and to arrive at a
mechanistic understanding of the quantitative contributions of each of the enzymes to this
control. Our analysis provides an example of the descriptive power of the general principles
of symbolic control analysis.
Description
CITATION: Christensen, C. D., Hofmeyr, J. H. S. & Rohwer, J. M. 2018. Delving deeper: Relating the behaviour of a metabolic system to the properties of its components using symbolic metabolic control analysis. PLoS ONE, 13(11):e0207983, doi:10.1371/journal.pone.0207983.
The original publication is available at http://journals.plos.org/plosone
Publication of this article was funded by the Stellenbosch University Open Access Fund.
The original publication is available at http://journals.plos.org/plosone
Publication of this article was funded by the Stellenbosch University Open Access Fund.
Keywords
Metabolic systems, Symbolic control analysis, Models of metabolic pathways
Citation
Christensen, C. D., Hofmeyr, J. H. S. & Rohwer, J. M. 2018. Delving deeper: Relating the behaviour of a metabolic system to the properties of its components using symbolic metabolic control analysis. PLoS ONE, 13(11):e0207983, doi:10.1371/journal.pone.0207983