Algorithmic and Complexity Results for Decompositions of Biological Networks into Monotone Subsystems

  • Bhaskar DasGupta
  • German A. Enciso
  • Eduardo Sontag
  • Yi Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4007)


A useful approach to the mathematical analysis of large-scale biological networks is based upon their decompositions into monotone dynamical systems. This paper deals with two computational problems associated to finding decompositions which are optimal in an appropriate sense. In graph-theoretic language, the problems can be recast in terms of maximal sign-consistent subgraphs. The theoretical results include polynomial-time approximation algorithms as well as constant-ratio inapproximability results. One of the algorithms, which has a worst-case guarantee of 87.9% from optimality, is based on the semidefinite programming relaxation approach of Goemans-Williamson [14]. The algorithm was implemented and tested on a Drosophila segmentation network [7] and an Epidermal Growth Factor Receptor pathway model [25], and it was found to perform close to optimally.


Epidermal Growth Factor Receptor Epidermal Growth Factor Receptor Pathway Monotone System Monotone Dynamical System Monotone Component 
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-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bhaskar DasGupta
    • 1
  • German A. Enciso
    • 2
  • Eduardo Sontag
    • 3
  • Yi Zhang
    • 1
  1. 1.Department of Computer ScienceUniversity of IL at ChicagoChicagoUSA
  2. 2.Mathematical Biosciences InstituteColumbusUSA
  3. 3.Department of MathematicsRutgers UniversityNew BrunswickUSA

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