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Title: Emerging ensembles of kinetic parameters to identify experimentally observed phenotypes
Authors: Colombo, R
Damiani, C
Gilbert, D
Heiner, M
Mauri, G
Pescini, D
Keywords: Ensembles;Fluxes;Kinetic parameters;Mechanistic simulations;Metabolism;ODEs
Issue Date: 2018
Publisher: BioMed Central
Citation: BMC Bioinformatics, 2018, 19 (251)
Abstract: Background: Determining the value of kinetic constants for a metabolic system in the exact physiological conditions is an extremely hard task. However, this kind of information is of pivotal relevance to effectively simulate a biological phenomenon as complex as metabolism. Results: To overcome this issue, we propose to investigate emerging properties of ensembles of sets of kinetic constants leading to the biological readout observed in different experimental conditions. To this aim, we exploit information retrievable from constraint-based analyses (i.e. metabolic flux distributions at steady state) with the goal to generate feasible values for kinetic constants exploiting the mass action law. The sets retrieved from the previous step will be used to parametrize a mechanistic model whose simulation will be performed to reconstruct the dynamics of the system (until reaching the metabolic steady state) for each experimental condition. Every parametrization that is in accordance with the expected metabolic phenotype is collected in an ensemble whose features are analyzed to determine the emergence of properties of a phenotype. In this work we apply the proposed approach to identify ensembles of kinetic parameters for five metabolic phenotypes of E. Coli, by analyzing five different experimental conditions associated with the ECC2comp model recently published by Hädicke and collaborators. Conclusions: Our results suggest that the parameter values of just few reactions are responsible for the emergence of a metabolic phenotype. Notably, in contrast with constraint-based approaches such as Flux Balance Analysis, the methodology used in this paper does not require to assume that metabolism is optimizing towards a specific goal.
ISSN: 1471-2105
Appears in Collections:Dept of Computer Science Research Papers

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