| Theoretical Analysis of Time-to-Peak Responses in Biological Reaction Networks. | |
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MedLine Citation:
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PMID: 20499193 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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Processing of information by signaling networks is characterized by properties of the induced kinetics of the activated pathway components. The maximal extent of pathway activation (maximum amplitude) and the time-to-peak-response (position) are key determinants of biological responses that have been linked to specific outcomes. We investigate how the maximum amplitude of pathway activation and its position depend on the input and wiring of a signaling network. For this purpose, we consider a simple reaction A→B that is regulated by a transient input and extended this to include back-reaction and additional partners. In particular, we show that a unique maximum of B(t) exists. Moreover, we prove that the position of the maximum is independent of the applied input but regulated by degradation reactions of B. Indeed, the time-to-peak-response decreases with increasing degradation rate, which we prove for small models and show in simulations for more complex ones. The identified dependencies provide insights into design principles that facilitate the realization dynamical characteristics like constant position of maximal pathway activation and thereby guide the characterization of unknown kinetics within larger protein networks. |
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Authors:
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Fabian J Theis; Sebastian Bohl; Ursula Klingmüller |
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Publication Detail:
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Type: Journal Article Date: 2010-05-25 |
Journal Detail:
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Title: Bulletin of mathematical biology Volume: 73 ISSN: 1522-9602 ISO Abbreviation: Bull. Math. Biol. Publication Date: 2011 May |
Date Detail:
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Created Date: 2011-04-07 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 0401404 Medline TA: Bull Math Biol Country: United States |
Other Details:
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Languages: eng Pagination: 978-1003 Citation Subset: IM |
Affiliation:
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Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany, fabian.theis@helmholtz-muenchen.de. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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