Literature DB >> 19657015

Biological pathways as communicating computer systems.

Marta Z Kwiatkowska1, John K Heath.   

Abstract

Time and cost are the enemies of cell biology. The number of experiments required to rigorously dissect and comprehend a pathway of even modest complexity is daunting. Methods are needed to formulate biological pathways in a machine-analysable fashion, which would automate the process of considering all possible experiments in a complex pathway and identify those that command attention. In this Essay, we describe a method that is based on the exploitation of computational tools that were originally developed to analyse reactive communicating computer systems such as mobile phones and web browsers. In this approach, the biological process is articulated as an executable computer program that can be interrogated using methods that were developed to analyse complex software systems. Using case studies of the FGF, MAPK and Delta/Notch pathways, we show that the application of this technology can yield interesting insights into the behaviour of signalling pathways, which have subsequently been corroborated by experimental data.

Mesh:

Year:  2009        PMID: 19657015     DOI: 10.1242/jcs.039701

Source DB:  PubMed          Journal:  J Cell Sci        ISSN: 0021-9533            Impact factor:   5.285


  5 in total

Review 1.  The executable pathway to biological networks.

Authors:  Jasmin Fisher; Nir Piterman
Journal:  Brief Funct Genomics       Date:  2010-01       Impact factor: 4.241

2.  Computational biology: Biological logic.

Authors:  Lucas Laursen
Journal:  Nature       Date:  2009-11-26       Impact factor: 49.962

3.  The mEPN scheme: an intuitive and flexible graphical system for rendering biological pathways.

Authors:  Tom C Freeman; Sobia Raza; Athanasios Theocharidis; Peter Ghazal
Journal:  BMC Syst Biol       Date:  2010-05-17

4.  Construction of a large scale integrated map of macrophage pathogen recognition and effector systems.

Authors:  Sobia Raza; Neil McDerment; Paul A Lacaze; Kevin Robertson; Steven Watterson; Ying Chen; Michael Chisholm; George Eleftheriadis; Stephanie Monk; Maire O'Sullivan; Arran Turnbull; Douglas Roy; Athanasios Theocharidis; Peter Ghazal; Tom C Freeman
Journal:  BMC Syst Biol       Date:  2010-05-14

5.  The cell cycle switch computes approximate majority.

Authors:  Luca Cardelli; Attila Csikász-Nagy
Journal:  Sci Rep       Date:  2012-09-13       Impact factor: 4.379

  5 in total

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