title: Hybrid modeling of the crosstalk between signaling and transcriptional networks using ordinary differential equations and multi-valued logic.
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 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.
authors.affiliation: Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany.
authors.forename: Faiz M
authors.initials: FM
authors.lastname: Khan
authors.forename: Ulf
authors.initials: U
authors.lastname: Schmitz
authors.forename: Svetoslav
authors.initials: S
authors.lastname: Nikolov
authors.forename: David
authors.initials: D
authors.lastname: Engelmann
authors.forename: Brigitte M
authors.initials: BM
authors.lastname: Pützer
authors.forename: Olaf
authors.initials: O
authors.lastname: Wolkenhauer
authors.forename: Julio
authors.initials: J
authors.lastname: Vera
mesh_terms: D019295:Computational Biology / Q000379:methods; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D015398:Signal Transduction / Q000235:genetics*; D049490:Systems Biology; D014157:Transcription Factors / Q000235:genetics
chemical_list: D014157:Transcription Factors
keywords: Cancer; Chemoresistance; E2F1; Feedback loop; Non-linear motif; Signaling