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Evaluating a common semi-mechanistic mathematical model of gene-regulatory networks

Alexandru, Mizeranschi, Zheng, Huiru, Thompson, Paul and Dubitzky, Werner (2015) Evaluating a common semi-mechanistic mathematical model of gene-regulatory networks. BMC Systems Biology 2015, 9(Supp (S2). [Journal article]

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DOI: 10.1186/1752-0509-9-S5-S2


Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations into mechanisms underlying gene regulation. A key challenge in this area is the automated inference (reverse-engineering) of dynamic, mechanistic GRN models from gene expression time-course data. Common mathematical formalisms for representing such models capture two aspects simultaneously within a single parameter: (1) Whether or not a gene is regulated, and if so, the type of regulator (activator or repressor), and (2) the strength of influence of the regulator (if any) on the target or effector gene. To accommodate both roles, "generous" boundaries or limits for possible values of this parameter are commonly allowed in the reverse-engineering process. This approach has several important drawbacks. First, in the absence of good guidelines, there is no consensus on what limits are reasonable. Second, because the limits may vary greatly among different reverse-engineering experiments, the concrete values obtained for the models may differ considerably, and thus it is difficult to compare models. Third, if high values are chosen as limits, the search space of the model inference process becomes very large, adding unnecessary computational load to the already complex reverse-engineering process. In this study, we demonstrate that restricting the limits to the [−1, +1] interval is sufficient to represent the essential features of GRN systems and offers a reduction of the search space without loss of quality in the resulting models. To show this, we have carried out reverse-engineering studies on data generated from artificial and experimentally determined from real GRN systems.

Item Type:Journal article
Keywords:Modeling and simulation; gene regulation
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Life and Health Sciences > School of Biomedical Sciences
Faculty of Life and Health Sciences
Research Institutes and Groups:Biomedical Sciences Research Institute > Genomic Medicine
Biomedical Sciences Research Institute
Computer Science Research Institute > Smart Environments
Computer Science Research Institute
ID Code:32265
Deposited By: Professor Werner Dubitzky
Deposited On:08 Sep 2015 07:50
Last Modified:17 Oct 2017 16:19

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