The Influence of Network Topology on Reverse-engineering of Gene-regulatory Networks

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• 作者：Alexandru Mizeranschi ; Noel Kennedy ; Paul Thompson ; Huiru Zheng ; Werner Dubitzky ; ^{w.dubitzky@ulster.ac.uk" class="auth_mail}
• 关键词：Gene-regulation ; automated model inference ; rate law ; structure parameters
• 刊名：Procedia Computer Science
• 出版年：2014
• 年：2014
• 卷：29
• 期：Complete
• 页码：410-421
• 全文大小：1087 K

文摘

Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern computational biology investigations into gene regulation. A key challenge in this area is the automated inference (reverse-engineering) of dynamic, mechanistic GRN models from time-course gene expression data. Common mathematical formalisms used to represent such models capture both the relative weight or strength of a regulator gene and the type of the regulator (activator, repressor) with a single model parameter. The goal of this study is to quantify the role this parameter plays in terms of the computational performance of the reverse-engineering process and the predictive power of the inferred GRN models. We carried out three sets of computational experiments on a GRN system consisting of 22 genes. While more comprehensive studies of this kind are ultimately required, this computational study demonstrates that models with similar training (reverse-engineering) error that have been inferred under varying degrees of a priori known topology information, exhibit considerably different predictive performance. This study was performed with a newly developed multiscale modeling and simulation tool called MultiGrain/MAPPER.