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Representing Dynamic Biological Networks With Multi-Scale Probabilistic Models
Institute of Medical Systems Biology, Ulm University, Ulm, Germany.
Institute of Biochemistry and Molecular Biology, Ulm University, Ulm, Germany.
Institute of Medical Systems Biology, Ulm University, Ulm, Germany.
Institute of Medical Systems Biology, Ulm University, Ulm, Germany.
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2019 (English)In: Communications Biology, E-ISSN 2399-3642, Vol. 2, article id 21Article in journal (Refereed) Published
Abstract [en]

Dynamic models analyzing gene regulation and metabolism face challenges when adapted to modeling signal transduction networks. During signal transduction, molecular reactions and mechanisms occur in different spatial and temporal frames and involve feedbacks. This impedes the straight-forward use of methods based on Boolean networks, Bayesian approaches, and differential equations. We propose a new approach, ProbRules, that combines probabilities and logical rules to represent the dynamics of a system across multiple scales. We demonstrate that ProbRules models can represent various network motifs of biological systems. As an example of a comprehensive model of signal transduction, we provide a Wnt network that shows remarkable robustness under a range of phenotypical and pathological conditions. Its simulation allows the clarification of controversially discussed molecular mechanisms of Wnt signaling by predicting wet-lab measurements. ProbRules provides an avenue in current computational modeling by enabling systems biologists to integrate vast amounts of available data on different scales.

Place, publisher, year, edition, pages
Springer Nature, 2019. Vol. 2, article id 21
National Category
Natural Sciences Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:oru:diva-83320DOI: 10.1038/s42003-018-0268-3ISI: 000461149000001Scopus ID: 2-s2.0-85064456622OAI: oai:DiVA.org:oru-83320DiVA, id: diva2:1442592
Available from: 2020-06-17 Created: 2020-06-17 Last updated: 2020-12-15Bibliographically approved

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De Raedt, Luc

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