Literature DB >> 15604106

Investigating the dynamic behavior of biochemical networks using model families.

Marc Daniel Haunschild1, Bernd Freisleben, Ralf Takors, Wolfgang Wiechert.   

Abstract

MOTIVATION: Supporting the evolutionary modeling process of dynamic biochemical networks based on sampled in vivo data requires more than just simulation. In the course of the modeling process, the modeler is typically concerned not only with a single model but also with sequences, alternatives and structural variants of models. Powerful automatic methods are then required to assist the modeler in the organization and the evaluation of alternative models. Moreover, the structure and peculiarities of the data require dedicated tool support.
SUMMARY: To support all stages of an evolutionary modeling process, a new general formalism for the combinatorial specification of large model families is introduced. It allows for automatic navigation in the space of models and excludes biologically meaningless models on the basis of elementary flux mode analysis. An incremental usage of the measured data is supported by using splined data instead of state variables. With MMT2, a versatile tool has been developed as a computational engine intended to be built into a tool chain. Using automatic code generation, automatic differentiation for sensitivity analysis and grid computing technology, a high performance computing environment is achieved. MMT2 supplies XML model specification and several software interfaces. The performance of MMT2 is illustrated by several examples from ongoing research projects. AVAILABILITY: http://www.simtec.mb.uni-siegen.de/ CONTACT: wiechert@simtec.mb.uni-siegen.de.

Mesh:

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Year:  2004        PMID: 15604106     DOI: 10.1093/bioinformatics/bti225

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  8 in total

1.  Efficient classification of complete parameter regions based on semidefinite programming.

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2.  Sloppy models, parameter uncertainty, and the role of experimental design.

Authors:  Joshua F Apgar; David K Witmer; Forest M White; Bruce Tidor
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3.  Dynamic Bayesian sensitivity analysis of a myocardial metabolic model.

Authors:  D Calvetti; R Hageman; R Occhipinti; E Somersalo
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4.  Understanding regulation of metabolism through feasibility analysis.

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5.  Integrating a dynamic central metabolism model of cancer cells with a hybrid 3D multiscale model for vascular hepatocellular carcinoma growth.

Authors:  Alexey Lapin; Holger Perfahl; Harsh Vardhan Jain; Matthias Reuss
Journal:  Sci Rep       Date:  2022-07-20       Impact factor: 4.996

6.  Visualizing regulatory interactions in metabolic networks.

Authors:  Stephan Noack; Aljoscha Wahl; Ermir Qeli; Wolfgang Wiechert
Journal:  BMC Biol       Date:  2007-10-16       Impact factor: 7.431

7.  Stimulus design for model selection and validation in cell signaling.

Authors:  Joshua F Apgar; Jared E Toettcher; Drew Endy; Forest M White; Bruce Tidor
Journal:  PLoS Comput Biol       Date:  2008-02       Impact factor: 4.475

8.  How informative is your kinetic model?: using resampling methods for model invalidation.

Authors:  Dicle Hasdemir; Huub C J Hoefsloot; Johan A Westerhuis; Age K Smilde
Journal:  BMC Syst Biol       Date:  2014-05-22
  8 in total

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