Literature DB >> 17091578

Combinatorial complexity and dynamical restriction of network flows in signal transduction.

J R Faeder1, M L Blinov, B Goldstein, W S Hlavacek.   

Abstract

The activities and interactions of proteins that govern the cellular response to a signal generate a multitude of protein phosphorylation states and heterogeneous protein complexes. Here, using a computational model that accounts for 307 molecular species implied by specified interactions of four proteins involved in signalling by the immunoreceptor FcepsilonRI, we determine the relative importance of molecular species that can be generated during signalling, chemical transitions among these species, and reaction paths that lead to activation of the protein tyrosine kinase (PTK) Syk. By all of these measures and over two- and ten-fold ranges of model parameters--rate constants and initial concentrations--only a small portion of the biochemical network is active. The spectrum of active complexes, however, can be shifted dramatically, even by a change in the concentration of a single protein, which suggests that the network can produce qualitatively different responses under different cellular conditions and in response to different inputs. Reduced models that reproduce predictions of the full model for a particular set of parameters lose their predictive capacity when parameters are varied over two-fold ranges.

Entities:  

Mesh:

Substances:

Year:  2005        PMID: 17091578     DOI: 10.1049/sb:20045031

Source DB:  PubMed          Journal:  Syst Biol (Stevenage)        ISSN: 1741-2471


  27 in total

1.  Leveraging modeling approaches: reaction networks and rules.

Authors:  Michael L Blinov; Ion I Moraru
Journal:  Adv Exp Med Biol       Date:  2012       Impact factor: 2.622

Review 2.  Translational systems approaches to the biology of inflammation and healing.

Authors:  Yoram Vodovotz; Gregory Constantine; James Faeder; Qi Mi; Jonathan Rubin; John Bartels; Joydeep Sarkar; Robert H Squires; David O Okonkwo; Jörg Gerlach; Ruben Zamora; Shirley Luckhart; Bard Ermentrout; Gary An
Journal:  Immunopharmacol Immunotoxicol       Date:  2010-06       Impact factor: 2.730

3.  Hierarchical graphs for rule-based modeling of biochemical systems.

Authors:  Nathan W Lemons; Bin Hu; William S Hlavacek
Journal:  BMC Bioinformatics       Date:  2011-02-02       Impact factor: 3.169

4.  Internal coarse-graining of molecular systems.

Authors:  Jérôme Feret; Vincent Danos; Jean Krivine; Russ Harmer; Walter Fontana
Journal:  Proc Natl Acad Sci U S A       Date:  2009-04-03       Impact factor: 11.205

Review 5.  Modeling for (physical) biologists: an introduction to the rule-based approach.

Authors:  Lily A Chylek; Leonard A Harris; James R Faeder; William S Hlavacek
Journal:  Phys Biol       Date:  2015-07-16       Impact factor: 2.583

Review 6.  Multiscale models of cell signaling.

Authors:  Sameer S Bajikar; Kevin A Janes
Journal:  Ann Biomed Eng       Date:  2012-04-03       Impact factor: 3.934

7.  Compartmental and Spatial Rule-Based Modeling with Virtual Cell.

Authors:  Michael L Blinov; James C Schaff; Dan Vasilescu; Ion I Moraru; Judy E Bloom; Leslie M Loew
Journal:  Biophys J       Date:  2017-10-03       Impact factor: 4.033

8.  Generalizing Gillespie's Direct Method to Enable Network-Free Simulations.

Authors:  Ryan Suderman; Eshan D Mitra; Yen Ting Lin; Keesha E Erickson; Song Feng; William S Hlavacek
Journal:  Bull Math Biol       Date:  2018-03-28       Impact factor: 1.758

9.  RuleMonkey: software for stochastic simulation of rule-based models.

Authors:  Joshua Colvin; Michael I Monine; Ryan N Gutenkunst; William S Hlavacek; Daniel D Von Hoff; Richard G Posner
Journal:  BMC Bioinformatics       Date:  2010-07-30       Impact factor: 3.169

10.  Domain-oriented reduction of rule-based network models.

Authors:  N M Borisov; A S Chistopolsky; J R Faeder; B N Kholodenko
Journal:  IET Syst Biol       Date:  2008-09       Impact factor: 1.615

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.