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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
biochemistry, genetics and molecular biology
Hybrid modeling of the crosstalk between signaling and transcriptional networks using ordinary differential equations and multi-valued logic
Biochimica et Biophysica Acta - Proteins and Proteomics, Volume 1844, No. 1 PART B, Year 2014
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Description
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 hundreds 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 regulations. 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 compromise 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. Guest Editor: Yudong Cai. © 2013 Elsevier B.V.
Authors & Co-Authors
Khan, Faiz Muhammad
Germany, Rostock
Universität Rostock
Schmitz, Ulf
Germany, Rostock
Universität Rostock
Nikolov, Svetoslav G.
Bulgaria, Sofia
Institute of Mechanics Bulgarian Academy of Sciences
Engelmann, David
Germany, Rostock
Universität Rostock Uniklinikum Und Medizinische Fakultät
Pützer, Brigitte M.
Germany, Rostock
Universität Rostock Uniklinikum Und Medizinische Fakultät
Wolkenhauer, Olaf
Germany, Rostock
Universität Rostock
South Africa, Stellenbosch
Stellenbosch University
Vera, Julio
Germany, Rostock
Universität Rostock
Germany, Erlangen
Friedrich-alexander-universität Erlangen-nürnberg
Statistics
Citations: 29
Authors: 7
Affiliations: 5
Identifiers
Doi:
10.1016/j.bbapap.2013.05.007
ISSN:
15709639
e-ISSN:
18781454
Research Areas
Cancer
Study Approach
Qualitative
Quantitative