Past Semesters

October 28

Speaker: Hyukpyo Hong (University of Wisconsin — Madison)

Title: Ubiquitous Asymptotic Robustness in Biochemical Systems

Abstract: Living systems maintain stable internal states despite environmental fluctuations. Absolute concentration robustness (ACR) is a striking homeostatic phenomenon in which the steady-state concentration of a species remains invariant despite changes in total supply. In this talk, we introduce a previously underappreciated phenomenon, namely asymptotic ACR (aACR): approximate robustness can emerge solely from the network structure, without requiring exact ACR motifs or negligible parameters. We find that aACR is more pervasive than classical ACR and prove that this ubiquity stems solely from network structure. This notion of aACR would provide a rigorous and practical tool to analyze robust responses in broad biochemical systems.

November 4:

Speaker: Ruby Kim (University of Michigan)

Title: Mechanistic Modeling of Dopamine Rhythms

Abstract: The circadian clock shapes nearly 24-hour periodic rhythms throughout the body, from the activity of individual cells to our daily sleep/wake cycles. These rhythms can be self-sustained (for example, people still show circadian patterns even in total darkness), but they are also strongly influenced by the environment, especially light. Mathematical models of circadian rhythms can provide insight into many intriguing phenomena, including jet lag, mid-afternoon fatigue, and how animals sense the changing seasons. In this talk, I will present recent work using mathematical models to investigate the daily regulation of dopamine, a neurotransmitter involved in motivation, attention, and mood. Our models reveal how circadian control of dopamine metabolism leads to time-of-day effects of dopamine reuptake inhibitors commonly used to treat fatigue, ADHD, and depression. Furthermore, by modeling the collective activity of dopaminergic neurons, we identify intrinsic ~4-hour ultradian oscillations that arise independently of circadian input. These limit cycle oscillations exhibit flexible periods that can be modulated by pharmacological intervention. Overall, this talk will highlight the utility of mechanistic models for generating testable hypotheses about neuromodulatory systems.

November 11: Swati Patel (Oregon State University)

Title: Persistence and Elimination of Structured Populations Under Pulsed Control

Abstract: The efficacy of control interventions of harmful biological populations, such as chemotherapy against cancer cells, antibiotics against bacteria, and insecticide sprays against agricultural pests, are exacerbated in many cases by population structure. That is, when the population is heterogenous, such as through phenotypic plasticity, life stages, or spatially and the efficacy of control varies, then populations can harbor in states less vulnerable (or entirely resistant) to the control agent. In this talk, we develop results on the persistence (or elimination) of broad classes of structured population models with pulsed control interventions. With the goal of generalizing principles, we aim to establish when control will be effective and when will there be an optimal strategy for structured populations. We demonstrate the implications of our results in two empirical examples.

November 18: Cody FitzGerald (Northwestern University)

Title: Data-driven modeling of complex biological systems

Abstract: Finding simple data-driven and interpretable models of complex biological systems requires engaging with experimental and real-world data, which often come with a range of challenges. Typical biological data sets often feature sources of extrinsic and intrinsic noise, infrequent temporal sampling of the dynamics, and, usually, not all relevant states of the system can be experimentally measured. Additionally, for many biological systems, the true dimensionality is unknown. In this talk, I walk through a series of recent modeling projects using experimental and real-world data, each featuring a different data-related challenge. In particular, I will discuss using wolves as an ecological control strategy to limit the spread of Chronic Wasting Disease in deer populations, an inference problem in which we fit 13,824 separate gene regulatory network models to an RNA-seq data set in an attempt to learn what can honestly be said about the underlying regulatory mechanism, and using model selection for partially observed systems to infer a physiological model of the thermoregulatory system for Arctic hibernators. Finally, I will describe recent ongoing work using a super high-dimensional spatio-temporal data set from the City of Chicago, in which we attempt to understand the city as a cellular organism and infer its "organelles" directly from data.

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