Complexity of the BN and the PBN Models of GRNs and Mappings for Complexity Reduction Chapter uri icon


  • Constructing computational models of genomic regulation faces several major challenges. While the advances in technology can help in obtaining more and better quality gene expression data, the complexity of the models that can be inferred from data is often high. This high complexity impedes the practical applications of such models, especially when one is interested in developing intervention strategies for disease control, for example, preventing tumor cells from entering a proliferative state. Thus, estimating the complexity of a model and designing strategies for complexity reduction become crucial in problems such as model selection, construction of tractable sub-network models, and control of the dynamical behavior of the model. In this chapter we discuss these issues in the setting of Boolean networks and probabilistic Boolean networks two important classes of network models for genomic regulatory networks.

author list (cited authors)

  • Das, S., Caragea, D., Welch, S., Hsu, W. H., & Ivanov, I. V.

citation count

  • 0

complete list of authors

  • Das, Sanjoy||Caragea, Doina||Welch, Stephen||Hsu, William H||Ivanov, Ivan V

Book Title

  • Handbook of Research on Computational Methodologies in Gene Regulatory Networks

publication date

  • January 2010