Reconstruction of Mammalian Cell Cycle Regulatory Network from Microarray Data Using Stochastic Logical Networks

  • Bartek Wilczyński
  • Jerzy Tiuryn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4695)


We present a novel algorithm for reconstructing the topology of regulatory networks based on the Stochastic Logical Network model. Our method, by avoiding the computation of the Markov model parameters is able to reconstruct the topology of the SLN model in polynomial time instead of exponential as in previous study [29]. To test the performance of the method, we apply it to different datasets (both synthetic and experimental) covering the expression of several cell cycle regulators which have been thoroughly studied [18,11]. We compare the results of our method with the popular Dynamic Bayesian Network approach in order to quantify the ability to reconstruct true dependencies. Although both methods able to recover only a part of the true dependencies from realistic data, our method gives consistently better results than Dynamic Bayesian Networks in terms of the number of correctly reconstructed edges, sensitivity and statistical significance.


Bayesian Network Boolean Network Minimum Description Length Regulatory Dependency Dynamic Bayesian Network 
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 2007

Authors and Affiliations

  • Bartek Wilczyński
    • 1
    • 2
  • Jerzy Tiuryn
    • 2
  1. 1.Institute of Mathematics, Polish Academy of Sciences, Śniadeckich 8, 00-956 WarsawPoland
  2. 2.Institute of Informatics, Warsaw University, Banacha 2, 02-089 WarsawPoland

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