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Modeling and Analysis of Qualitative Behavior of Gene Regulatory Networks

  • Alvis Brazma
  • Karlis Cerans
  • Dace Ruklisa
  • Thomas Schlitt
  • Juris ViksnaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7699)

Abstract

We describe a hybrid system based framework for modeling gene regulation and other biomolecular networks and a method for analysis of the dynamic behavior of such models. A particular feature of the proposed framework is the focus on qualitative experimentally testable properties of the system. With this goal in mind we introduce the notion of the frame of a hybrid system, which allows for the discretisation of the state space of the network. We propose two different methods for the analysis of this state space. The result of the analysis is a set of attractors that characterize the underlying biological system.

Whilst in the general case the problem of finding attractors in the state space is algorithmically undecidable, we demonstrate that our methods work for comparatively complex gene regulatory network model of \(\lambda \)-phage. For this model we are able to identify attractors corresponding to two known biological behaviors of \(\lambda \)-phage: lysis and lysogeny and also to show that there are no other stable behavior regions for this model.

Keywords

Gene Regulatory Network Hybrid Automaton Transition Constant Strongly Connect Component Outgoing Transition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alvis Brazma
    • 1
  • Karlis Cerans
    • 2
  • Dace Ruklisa
    • 3
  • Thomas Schlitt
    • 4
  • Juris Viksna
    • 2
    Email author
  1. 1.European Molecular Biology Laboratory, European Bioinformatics InstituteEMBL-EBIHinxtonUK
  2. 2.Institute of Mathematics and Computer ScienceRigaLatvia
  3. 3.University of CambridgeCambridgeUK
  4. 4.King’s College LondonLondonUK

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