Literature DB >> 21639589

Critical perspective on the consequences of the limited availability of kinetic data in metabolic dynamic modelling.

R S Costa1, D Machado, I Rocha, E C Ferreira.   

Abstract

Detailed kinetic models at the network reaction level are usually constructed using enzymatic mechanistic rate equations and the associated kinetic parameters. However, during the cellular life cycle thousands of different reactions occur, which makes it very difficult to build a detailed large-scale ldnetic model. In this work, we provide a critical overview of specific limitations found during the reconstruction of the central carbon metabolism dynamic model from E. coli (based on kinetic data available). In addition, we provide clues that will hopefully allow the systems biology community to more accurately construct metabolic dynamic models in the future. The difficulties faced during the construction of dynamic models are due not only to the lack of kinetic information but also to the fact that some data are still not curated. We hope that in the future, with the standardization of the in vitro enzyme protocols the approximation of in vitro conditions to the in vivo ones, it will be possible to integrate the available kinetic data into a complete large scale model. We also expect that collaborative projects between modellers and biologists will provide valuable kinetic data and permit the exchange of important information to solve most of these issues.

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Year:  2011        PMID: 21639589     DOI: 10.1049/iet-syb.2009.0058

Source DB:  PubMed          Journal:  IET Syst Biol        ISSN: 1751-8849            Impact factor:   1.615


  7 in total

1.  KiMoSys: a web-based repository of experimental data for KInetic MOdels of biological SYStems.

Authors:  Rafael S Costa; André Veríssimo; Susana Vinga
Journal:  BMC Syst Biol       Date:  2014-08-13

Review 2.  Predictive sulfur metabolism - a field in flux.

Authors:  Alexander Calderwood; Richard J Morris; Stanislav Kopriva
Journal:  Front Plant Sci       Date:  2014-11-18       Impact factor: 5.753

3.  Simultaneous parameters identifiability and estimation of an E. coli metabolic network model.

Authors:  Kese Pontes Freitas Alberton; André Luís Alberton; Jimena Andrea Di Maggio; Vanina Gisela Estrada; María Soledad Díaz; Argimiro Resende Secchi
Journal:  Biomed Res Int       Date:  2015-01-06       Impact factor: 3.411

4.  Structural control of metabolic flux.

Authors:  Max Sajitz-Hermstein; Zoran Nikoloski
Journal:  PLoS Comput Biol       Date:  2013-12-19       Impact factor: 4.475

Review 5.  In Silico Prediction of Large-Scale Microbial Production Performance: Constraints for Getting Proper Data-Driven Models.

Authors:  Julia Zieringer; Ralf Takors
Journal:  Comput Struct Biotechnol J       Date:  2018-07-06       Impact factor: 7.271

Review 6.  A Review of Dynamic Modeling Approaches and Their Application in Computational Strain Optimization for Metabolic Engineering.

Authors:  Osvaldo D Kim; Miguel Rocha; Paulo Maia
Journal:  Front Microbiol       Date:  2018-07-31       Impact factor: 5.640

7.  SBMLSimulator: A Java Tool for Model Simulation and Parameter Estimation in Systems Biology.

Authors:  Alexander Dörr; Roland Keller; Andreas Zell; Andreas Dräger
Journal:  Computation (Basel)       Date:  2014-12-18
  7 in total

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