Regulatory patterns in molecular interaction networks

Abstract

Understanding design principles of molecular interaction networks is an important goal of molecular systems biology. Some insights have been gained into features of their network topology through the discovery of graph theoretic patterns that constrain network dynamics. This paper contributes to the identification of patterns in the mechanisms that govern network dynamics. The control of nodes in gene regulatory, signaling, and metabolic networks is governed by a variety of biochemical mechanisms, with inputs from other network nodes that act additively or synergistically. This paper focuses on a certain type of logical rule that appears frequently as a regulatory pattern. Within the context of the multistate discrete model paradigm, a rule type is introduced that reduces to the concept of nested canalyzing function in the Boolean network case. It is shown that networks that employ this type of multivalued logic exhibit more robust dynamics than random networks, with few attractors and short limit cycles. It is also shown that the majority of regulatory functions in many published models of gene regulatory and signaling networks are nested canalyzing.

gene regulation; signaling; mathematical model; nested canalyzing function; robustness

References in corpus (3)

• 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

Cited by in corpus (11)

• Identification of control targets in Boolean molecular network models via computational algebra
• Boolean nested canalizing functions: a comprehensive analysis
• Molecular Network Control Through Boolean Canalization
• The Influence of Canalization on the Robustness of Boolean Networks
• The Number of Multistate Nested Canalyzing Functions
• Revealing the canalizing structure of Boolean functions: Algorithms and applications
• Bounds on the Average Sensitivity of Nested Canalizing Functions
• Quantifying the Total Effect of Edge Interventions in Discrete Multistate Networks
• Structure-based approach can identify driver nodes in ensembles of biologically-inspired Boolean networks
• Stability of Linear Boolean Networks
• Multistate nested canalizing functions