Graduation Year

2024

Date of Submission

12-2024

Document Type

Campus Only Senior Thesis

Degree Name

Bachelor of Arts

Department

Physics

Reader 1

Sarah Marzen

Reader 2

Adam Landsberg

Terms of Use & License Information

Terms of Use for work posted in Scholarship@Claremont.

Abstract

We propose an information-theoretic framework to analyze the relationship between memory and prediction in gene regulatory networks (GRNs). Further, by reformulating total predictable information (TPI) using entropy-based metrics, we enable practical estimation in high-dimensional modeling environments. This reformulation supports a Python-based simulation pipeline for exploring GRN dynamics, integrating SBMLtoODEjax for efficient numerical methods and autodiscjax for perturbation-driven analysis.

Utilizing Markov and semi-Markov models to calculate forward and reverse time causal states through their dwell time distributions, the pipeline leverages these features of ϵ-machines to quantify how GRNs memorize and predict. This approach may identify pathways for shifting GRNs between attractor states, with implications for experimental biophysics, developmental biology, and potentially therapeutics.

Available for download on Wednesday, December 09, 2026

This thesis is restricted to the Claremont Colleges current faculty, students, and staff.

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