Abstract

Biological systems must navigate dynamic and complex environments while operating under constraints such as limited molecular resources, biochemical noise, and structural trade-offs. This dissertation investigates three distinct cases in which biological systems allocate resources and process information efficiently, revealing emergent simplicity from underlying complexity. The first study develops a generalized end-product inhibition model with cross-talk and an excess of regulators, providing a proof-of-principle that simple biochemical circuits could, in principle, dynamically adjust their responsiveness to high-dimensional environmental variation. This result highlights how such circuits may allow adaptive filtering of both dominant and subdominant fluctuation modes. The second study, conducted in collaboration with Professor Shankar Mukherji’s group, explores cellular organelle biogenesis as a resource allocation problem. By integrating mathematical modeling and experimental imaging, this work uncovers critical scaling relationships between organelle number and size. The results suggest that cellular systems optimize organelle biogenesis under limited resource pools, leading to distinct allocation strategies for de novo synthesis and fission-derived organelles. The third study addresses functional organization in microbial ecosystems, where community-level metabolic function emerges from complex species interactions. This work compares two regression-based approaches—Ensemble Quotient Optimization (EQO) and LASSO—to infer functional groups from microbial abundance data. By evaluating these methods under increasingly realistic conditions, the analysis highlights the trade-offs between statistical regularization and prior assumptions in functional group recovery. Together, these three cases provide complementary perspectives on how biological systems transform complex processes into simpler, computationally efficient frameworks. By integrating theoretical models, statistical inference, and experimental data, this thesis contributes to our understanding of biological information processing across molecular, cellular, and ecological scales.

Committee Chair

Mikhail Tikhonov

Committee Members

Erik Henriksen; Liz Mallott; Mikhail Tikhonov; Ralf Wessel; Shankar Mukherji

Degree

Doctor of Philosophy (PhD)

Author's Department

Physics

Author's School

Graduate School of Arts and Sciences

Document Type

Dissertation

Date of Award

5-8-2025

Language

English (en)

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Physics Commons

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