Literature DB >> 19154080

Nested uncertainties in biochemical models.

J Schaber1, W Liebermeister, E Klipp.   

Abstract

Dynamic modelling of biochemical reaction networks has to cope with the inherent uncertainty about biological processes, concerning not only data and parameters but also kinetics and structure. These different types of uncertainty are nested within each other: uncertain network structures contain uncertain reaction kinetics, which in turn are governed by uncertain parameters. Here, the authors review some issues arising from such uncertainties and sketch methods, solutions and future directions to deal with them.

Mesh:

Year:  2009        PMID: 19154080     DOI: 10.1049/iet-syb:20070042

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


  15 in total

Review 1.  Bridging the gaps in systems biology.

Authors:  Marija Cvijovic; Joachim Almquist; Jonas Hagmar; Stefan Hohmann; Hans-Michael Kaltenbach; Edda Klipp; Marcus Krantz; Pedro Mendes; Sven Nelander; Jens Nielsen; Andrea Pagnani; Natasa Przulj; Andreas Raue; Jörg Stelling; Szymon Stoma; Frank Tobin; Judith A H Wodke; Riccardo Zecchina; Mats Jirstrand
Journal:  Mol Genet Genomics       Date:  2014-04-13       Impact factor: 3.291

2.  Modelling reveals novel roles of two parallel signalling pathways and homeostatic feedbacks in yeast.

Authors:  Jörg Schaber; Rodrigo Baltanas; Alan Bush; Edda Klipp; Alejandro Colman-Lerner
Journal:  Mol Syst Biol       Date:  2012       Impact factor: 11.429

3.  Automated ensemble modeling with modelMaGe: analyzing feedback mechanisms in the Sho1 branch of the HOG pathway.

Authors:  Jörg Schaber; Max Flöttmann; Jian Li; Carl-Fredrik Tiger; Stefan Hohmann; Edda Klipp
Journal:  PLoS One       Date:  2011-03-30       Impact factor: 3.240

4.  Automated Bayesian model development for frequency detection in biological time series.

Authors:  Emma Granqvist; Giles E D Oldroyd; Richard J Morris
Journal:  BMC Syst Biol       Date:  2011-06-24

5.  Analyzing the functional properties of the creatine kinase system with multiscale 'sloppy' modeling.

Authors:  Hannes Hettling; Johannes H G M van Beek
Journal:  PLoS Comput Biol       Date:  2011-08-11       Impact factor: 4.475

6.  A Nonlinear Mixed Effects Approach for Modeling the Cell-To-Cell Variability of Mig1 Dynamics in Yeast.

Authors:  Joachim Almquist; Loubna Bendrioua; Caroline Beck Adiels; Mattias Goksör; Stefan Hohmann; Mats Jirstrand
Journal:  PLoS One       Date:  2015-04-20       Impact factor: 3.240

7.  Mathematical modeling of plant metabolism-from reconstruction to prediction.

Authors:  Thomas Nägele; Wolfram Weckwerth
Journal:  Metabolites       Date:  2012-09-06

8.  A workflow for mathematical modeling of subcellular metabolic pathways in leaf metabolism of Arabidopsis thaliana.

Authors:  Thomas Nägele; Wolfram Weckwerth
Journal:  Front Plant Sci       Date:  2013-12-24       Impact factor: 5.753

9.  Mathematical modeling reveals that metabolic feedback regulation of SnRK1 and hexokinase is sufficient to control sugar homeostasis from energy depletion to full recovery.

Authors:  Thomas Nägele; Wolfram Weckwerth
Journal:  Front Plant Sci       Date:  2014-07-28       Impact factor: 5.753

10.  Solving the differential biochemical Jacobian from metabolomics covariance data.

Authors:  Thomas Nägele; Andrea Mair; Xiaoliang Sun; Lena Fragner; Markus Teige; Wolfram Weckwerth
Journal:  PLoS One       Date:  2014-04-02       Impact factor: 3.240

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