Exploring Memory in Boolean Network Models of Gene Regulation (Spring 2027)

Boolean networks are a widely used framework for modeling gene regulation, where each gene is either on or off and updates according to logical rules. Standard update schemes assume genes respond instantly to their regulators, but real biological processes involve delays and persistence. In this project, students will explore the "most permissive" update scheme (Paulevé et al., Nature Communications, 2020), which introduces a form of memory into Boolean network dynamics. The work has two components. On the mathematical side, students will investigate how adding memory changes the behavior of these systems. Giving the system more memory means incorporating more information about its trajectory, much as higher-order derivatives encode more information about a function; a central question is what new dynamical behavior emerges as the amount of memory grows. On the computational side, students will implement this "most permissive" update scheme and extensions as a new feature in boolforge, an open-source Python package for Boolean networks. Students will gain experience with mathematical modeling in biology, discrete dynamical systems, and delay equations. 

For more information contact Sean Campbell (seanpc@iastate.edu)

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Pre-requisites: