Inference of Boolean networks using sensitivity regularization. Academic Article uri icon

abstract

  • The inference of genetic regulatory networks from global measurements of gene expressions is an important problem in computational biology. Recent studies suggest that such dynamical molecular systems are poised at a critical phase transition between an ordered and a disordered phase, affording the ability to balance stability and adaptability while coordinating complex macroscopic behavior. We investigate whether incorporating this dynamical system-wide property as an assumption in the inference process is beneficial in terms of reducing the inference error of the designed network. Using Boolean networks, for which there are well-defined notions of ordered, critical, and chaotic dynamical regimes as well as well-studied inference procedures, we analyze the expected inference error relative to deviations in the networks' dynamical regimes from the assumption of criticality. We demonstrate that taking criticality into account via a penalty term in the inference procedure improves the accuracy of prediction both in terms of state transitions and network wiring, particularly for small sample sizes.

published proceedings

  • EURASIP J Bioinform Syst Biol

author list (cited authors)

  • Liu, W., Lhdesmki, H., Dougherty, E. R., & Shmulevich, I.

citation count

  • 20

complete list of authors

  • Liu, Wenbin||Lähdesmäki, Harri||Dougherty, Edward R||Shmulevich, Ilya

publication date

  • July 2008