Inferring Biologically Relevant Models: Nested Canalyzing Functions

Abstract

Inferring dynamic biochemical networks is one of the main challenges in systems biology. Given experimental data, the objective is to identify the rules of interaction among the different entities of the network. However, the number of possible models fitting the available data is huge and identifying a biologically relevant model is of great interest. Nested canalyzing functions, where variables in a given order dominate the function, have recently been proposed as a framework for modeling gene regulatory networks. Previously we described this class of functions as an algebraic toric variety. In this paper, we present an algorithm that identifies all nested canalyzing models that fit the given data. We demonstrate our methods using a well-known Boolean model of the cell cycle in budding yeast.

References in corpus (5)

• The Yeast Cell-Cycle Network Is Robustly Designed
• Random Boolean Network Models and the Yeast Transcriptional Network
• Genetic networks with canalyzing Boolean rules are always stable
• A Computational Algebra Approach to the Reverse Engineering of Gene Regulatory Networks
• Reverse-engineering of polynomial dynamical systems

Cited by in corpus (1)

• Structure-based approach can identify driver nodes in ensembles of biologically-inspired Boolean networks