Reverse-Engineering Gene Networks that can Remember Using the Manifold Boundary Approximation Method

Authors

Andrew White, Brigham Young University
Mark Transtrum, Brigham Young University

Keywords

reverse-engineering, gene networks, manifold boundary approximation method

College

Physical and Mathematical Sciences

Department

Physics and Astronomy

Abstract

Observable biological behaviors result from the interactions of microscopic elements, which form complex systems that we can model mathematically. Ideally, mechanistic models should predict a biological system’s behavior without misrepresenting the system’s biochemistry. The method of model reduction known as the Manifold Boundary Approximation Method (MBAM) [2, 3] can help us identify which parts of a model are relevant for explaining a particular behavior. This project applies MBAM to gene transcription networks that exhibit a behavior known as “memory,” the ability to retain cellular decisions to activate or silence genes. We hypothesize that MBAM can help us model the behaviors of genetic “memory” systems in such a way that the resulting model accurately represents the systems’ underlying biochemistry. Furthermore, by applying MBAM to a hypothetical “Fully Connected Gene Network” (FCGN), we hope to reverse-engineer new gene network motifs that result in memory behavior.

Recommended Citation

White, Andrew and Transtrum, Mark (2017) "Reverse-Engineering Gene Networks that can Remember Using the Manifold Boundary Approximation Method," Journal of Undergraduate Research: Vol. 2017: Iss. 1, Article 307.
Available at: https://scholarsarchive.byu.edu/jur/vol2017/iss1/307