%0 Journal Article
%T Inference of Boolean Networks Using Sensitivity Regularization
%A Liu Wenbin
%A L£¿¡èhdesm£¿¡èki Harri
%A Dougherty Edward
%A Shmulevich Ilya
%J EURASIP Journal on Bioinformatics and Systems Biology
%D 2008
%I Springer
%X 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.
%U http://bsb.eurasipjournals.com/content/2008/780541