Inferring Biologically Relevant Models: Nested Canalyzing Functions

TL;DR

This paper introduces an algorithm to identify all nested canalyzing functions fitting biological data, aiding the modeling of gene regulatory networks by focusing on biologically relevant models.

Contribution

It presents a novel algorithm for inferring nested canalyzing models from data, advancing the modeling of gene regulatory networks in systems biology.

Findings

Successfully applied to yeast cell cycle data

Identifies all compatible nested canalyzing models

Enhances understanding of gene regulation mechanisms

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.