RESEARCH ARTICLE
Exploring the Dynamics of Large-Scale Biochemical Networks: A Computational Perspective
Ralf Steuer*, a, b
Article Information
Identifiers and Pagination:
Year: 2011Volume: 5
First Page: 4
Last Page: 15
Publisher ID: TOBIOIJ-5-4
DOI: 10.2174/1875036201105010004
Article History:
Received Date: 13/07/2010Revision Received Date: 24/08/2010
Acceptance Date: 01/10/2010
Electronic publication date: 02/02/2011
Collection year: 2011
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
The complexity of even comparatively simple biochemical systems necessitates a computational description to explore and eventually understand the dynamics emerging from the underlying networks of cellular interactions. Within this contribution, several aspects relating to a computational description of large-scale biochemical networks are discussed. Topics range from a brief description of the rationales for computational modeling to the utilization of Monte Carlo methods to explore dynamic properties of biochemical networks. The main focus is to outline a path towards the construction of large-scale kinetic models of metabolic networks in the face of incomplete and uncertain knowledge of kinetic parameters. It is argued that a combination of phenotypic data, large-scale measurements, heuristic assumptions about generic rate equations, together with appropriate numerical schemes, allows for a fast and efficient way to explore the dynamic properties of biochemical networks. In this respect, several recently proposed strategies that are based on Monte Carlo methods are an important step towards large-scale kinetic models of cellular metabolism.
