Journal of Undergraduate Research


reverse-engineering, gene networks, manifold boundary approximation method


Physical and Mathematical Sciences


Physics and Astronomy


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.

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