Khan, Faiz M., Schmitz, Ulf, Nikolov, Svetoslav, Engelmann, David, Pützer, Brigitte M., Wolkenhauer, Olaf, and Vera, Julio (2014) Hybrid modeling of the crosstalk between signaling and transcriptional networks using ordinary differential equations and multi-valued logic. Biochimica et Biophysica Acta. Protein Structure and Molecular Enzymology, 1844 (1). pp. 289-298.

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Abstract

A decade of successful results indicates that systems biology is the appropriate approach to investigate the regulation of complex biochemical networks involving transcriptional and post-transcriptional regulations. It becomes mandatory when dealing with highly interconnected biochemical networks, composed of hun- dreds of compounds, or when networks are enriched in non-linear motifs like feedback and feedforward loops. An emerging dilemma is to conciliate models of massive networks and the adequate description of non-linear dynamics in a suitable modeling framework. Boolean networks are an ideal representation of massive networks that are humble in terms of computational complexity and data demand. However, they are inappropriate when dealing with nested feedback/feedforward loops, structural motifs common in biochemical networks. On the other hand, models of ordinary differential equations (ODEs) cope well with these loops, but they require enormous amounts of quantitative data for a full characterization of the model. Here we propose hybrid models, composed of ODE and logical sub-modules, as a strategy to handle large scale, non-linear biochemical networks that include transcriptional and post-transcriptional regula- tions. We illustrate the construction of this kind of models using as example a regulatory network centered on E2F1, a transcription factor involved in cancer. The hybrid modeling approach proposed is a good compro- mise between quantitative/qualitative accuracy and scalability when considering large biochemical networks with a small highly interconnected core, and module of transcriptionally regulated genes that are not part of critical regulatory loops. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications.

Item ID:	69003
Item Type:	Article (Research - C1)
ISSN:	1570-9639
Copyright Information:	© 2013 Elsevier B.V. All rights reserved.
Date Deposited:	18 Dec 2023 04:00
FoR Codes:	31 BIOLOGICAL SCIENCES > 3101 Biochemistry and cell biology > 310114 Systems biology @ 100%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280102 Expanding knowledge in the biological sciences @ 100%
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