Systematic parameter estimation in data-rich environments for cell signalling dynamics | Zendy

Hieu T. Nim | Zendy; Le Luo | Zendy; MarieVéronique Clément | Zendy; Jacob White | Zendy; Lisa TuckerKellogg | Zendy

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Systematic parameter estimation in data-rich environments for cell signalling dynamics

Author(s) -

Hieu T. Nim,

Le Luo,

MarieVéronique Clément,

Jacob White,

Lisa TuckerKellogg

Publication year - 2013

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btt083

Subject(s) - tensin , ode , computer science , ordinary differential equation , biological system , mathematical optimization , software , discretization , mathematics , algorithm , pten , differential equation , chemistry , biology , apoptosis , mathematical analysis , biochemistry , pi3k/akt/mtor pathway , programming language

Computational models of biological signalling networks, based on ordinary differential equations (ODEs), have generated many insights into cellular dynamics, but the model-building process typically requires estimating rate parameters based on experimentally observed concentrations. New proteomic methods can measure concentrations for all molecular species in a pathway; this creates a new opportunity to decompose the optimization of rate parameters.

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