title: Inference of Boolean networks using sensitivity regularization.
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.
authors.affiliation: Institute for Systems Biology, Seattle, WA 98103, USA.
authors.forename: Wenbin
authors.initials: W
authors.lastname: Liu
authors.forename: Harri
authors.initials: H
authors.lastname: Lähdesmäki
authors.forename: Edward R
authors.initials: ER
authors.lastname: Dougherty
authors.forename: Ilya
authors.initials: I
authors.lastname: Shmulevich