Inference of Boolean Networks Using Sensitivity Regularization

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

doi:10.1155/2008/780541

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.

Highlights

The execution of various developmental and physiological processes in cells is carried out by complex biomolecular systems
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
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, for small sample sizes

Summary

Introduction

The execution of various developmental and physiological processes in cells is carried out by complex biomolecular systems. Such systems are dynamic in that they are able to change states in response to environmental cues and exhibit multiple steady states, which define different cellular functional states or cell types. There is accumulating evidence that living systems, as manifestations of their underlying networks of molecular interactions, are poised at the critical boundary between an organized and a disorganized state, indicating that cellular information processing is optimal in the critical regime, affording the cell with the ability to exhibit complex coordinated macroscopic behavior [4,5,6,7,8].

Methods

Results

Conclusion