Literature DB >> 19045835

Control, responses and modularity of cellular regulatory networks: a control analysis perspective.

F J Bruggeman1, J L Snoep, H V Westerhoff.   

Abstract

Cells adapt to changes in environmental conditions through the concerted action of signalling, gene expression and metabolic subsystems. The authors will discuss a theoretical framework addressing such integrated systems. This 'hierarchical analysis' was first developed as an extension to a metabolic control analysis. It builds on the phenomenon that often the communication between signalling, gene expression and metabolic subsystems is almost exclusively via regulatory interactions and not via mass flow interactions. This allows for the treatment of the said subsystems as 'levels' in a hierarchical view of the organisation of the molecular reaction network of cells. Such a hierarchical approach has as a major advantage that levels can be analysed conceptually in isolation of each other (from a local intra-level perspective) and at a later stage integrated via their interactions (from a global inter-level perspective). Hereby, it allows for a modular approach with variable scope. A number of different approaches have been developed for the analysis of hierarchical systems, for example hierarchical control analysis and modular response analysis. The authors, here, review these methods and illustrate the strength of these types of analyses using a core model of a system with gene expression, metabolic and signal transduction levels.

Mesh:

Year:  2008        PMID: 19045835     DOI: 10.1049/iet-syb:20070065

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


  9 in total

1.  Signal transduction networks in cancer: quantitative parameters influence network topology.

Authors:  David J Klinke
Journal:  Cancer Res       Date:  2010-02-23       Impact factor: 12.701

2.  Inferring predominant pathways in cellular models of breast cancer using limited sample proteomic profiling.

Authors:  Yogesh M Kulkarni; Vivian Suarez; David J Klinke
Journal:  BMC Cancer       Date:  2010-06-15       Impact factor: 4.430

3.  Modular bond-graph modelling and analysis of biomolecular systems.

Authors:  Peter J Gawthrop; Edmund J Crampin
Journal:  IET Syst Biol       Date:  2016-10       Impact factor: 1.615

4.  Modeling regulatory cascades using Artificial Neural Networks: the case of transcriptional regulatory networks shaped during the yeast stress response.

Authors:  Maria E Manioudaki; Panayiota Poirazi
Journal:  Front Genet       Date:  2013-06-20       Impact factor: 4.599

5.  Quantification of Interactions between Dynamic Cellular Network Functionalities by Cascaded Layering.

Authors:  Thomas P Prescott; Moritz Lang; Antonis Papachristodoulou
Journal:  PLoS Comput Biol       Date:  2015-05-01       Impact factor: 4.475

6.  Noise management by molecular networks.

Authors:  Frank J Bruggeman; Nils Blüthgen; Hans V Westerhoff
Journal:  PLoS Comput Biol       Date:  2009-09-18       Impact factor: 4.475

7.  Cancer cell growth and survival as a system-level property sustained by enhanced glycolysis and mitochondrial metabolic remodeling.

Authors:  Lilia Alberghina; Daniela Gaglio; Cecilia Gelfi; Rosa M Moresco; Giancarlo Mauri; Paola Bertolazzi; Cristina Messa; Maria C Gilardi; Ferdinando Chiaradonna; Marco Vanoni
Journal:  Front Physiol       Date:  2012-09-12       Impact factor: 4.566

8.  Gene network requirements for regulation of metabolic gene expression to a desired state.

Authors:  Jan Berkhout; Bas Teusink; Frank J Bruggeman
Journal:  Sci Rep       Date:  2013       Impact factor: 4.379

9.  (Im)Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis.

Authors:  Fei He; Vincent Fromion; Hans V Westerhoff
Journal:  BMC Syst Biol       Date:  2013-11-21
  9 in total

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