Department of

Mathematics


Seminar Calendar
for Mathematical Biology Seminar events the year of Monday, April 16, 2018.

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More information on this calendar program is available.
Questions regarding events or the calendar should be directed to Tori Corkery.
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Thursday, March 29, 2018

1:00 pm in 347 Altgeld Hall,Thursday, March 29, 2018

Variable immunity and its consequences for disease

Tara Stewart (Animal Biology, UIUC)

Abstract: Infectious disease results from interactions between pathogens and susceptible hosts in the environment. For many populations, we have a limited understanding of the mechanisms that shape host susceptibility and how these mechanisms interact with ecological factors to regulate the spread of disease. Focusing on a simple one-host one-parasite system with environmental transmission, I use theoretical and empirical methods to explore the causes and consequences of variable host immunity. I demonstrate how integrating immune defenses into host-parasite interactions can improve our understanding of disease transmission in natural systems.

Thursday, April 19, 2018

1:00 pm in 347 Altgeld Hall,Thursday, April 19, 2018

Behavioral Contagion Type in Coupled Disease-Behavior Models

Matt Osborne (Mathematics, Ohio State University)

Abstract: Disease and behavior have long been recognized as coupled contagions, and mathematical models treating them as such have existed since the mid 2000s. In these models behavior and disease are often both classified as 'simple contagions'. However, the means of behavior spread may in fact be more complex. Using some tools of dynamical systems we investigate the difference between a coupled contagion model with behavior as a 'simple contagion' and one with behavior as a 'complex contagion'. We find that behavioral contagion type can have a significant impact on behavior-disease dynamics which can in turn have implications for potential public health interventions.

Thursday, April 26, 2018

1:00 pm in 347 Altgeld Hall,Thursday, April 26, 2018

Constraints on eco-evolutionary dynamics in bacterial communities

Seppe Kuehn (Physics, Illinois)

Abstract: Can we predict evolutionary and ecological dynamics in microbial communities? I argue that understanding constraints on biological systems provides a path forward to build predictive models. I present two vignettes which illustrate the power of elucidating constraints. First, we ask how constraints on phenotypic variation can be exploited to predict evolution. We select Escherichia coli simultaneously for motility and growth and find that a trade-off between these phenotypes constrains adaptation. Using genetic engineering, high-throughput phenotyping and modeling we show that the genetic capacity of an organism to vary traits can qualitatively depend on its environment, which in turn alters its evolutionary trajectory [eLife, 2017]. Our results suggest that knowledge of phenotypic constraints and genetic architecture can provide a route to predicting evolutionary dynamics. Second, in nature microbial populations are subjected to nutrient fluctuations but we know little about how communities respond to these fluctuations. Using automated long-term single cell imaging and custom continuous-culture devices we subject bacterial populations to nutrient fluctuations on multiple timescales. We find populations recover faster from large, frequent fluctuations. Our observation is explained by a model that captures constraints on the rate at which populations transition from planktonic and aggregated lifestyles.

Tuesday, September 25, 2018

1:00 pm in 243 Altgeld Hall,Tuesday, September 25, 2018

Evolutionary dynamics of non-Hodgkin’s lymphoma CAR T cell therapy and extinction-event times in diffusive public goods games

Greg Kimmel (Moffitt Cancer Institute)

Abstract: Non-Hodgkin Lymphoma (NHL) is the most common hematologic malignancy in the United States with an estimated 72,000 new cases (4.3% of all cancer cases) and 20,000 deaths (3.4% of all cancer deaths) in 2017; the median 5-year survival rate is 71%. There exist patients that develop refractory disease. These patients have a median overall survival of less than seven months. Chimeric antigen receptor (CAR) T-cell therapy for refractory NHL relies on expansion of engineered T- cells that specifically target tumor cells expressing CD19. Here we combine mathematical modeling with statistical data-analysis based on recent results of clinical studies of CAR T-cell dynamics in individual patients. We find that the success of therapy may depend on dynamic regulation of inflammatory cytokines in the tumor microenvironment, as well as on specific properties of the heterogeneous CAR T-cell population. Relative abundances of juvenile and effector T cells are key factors that drive the duration of treatment response, and the tumor-killing rate of this CD19-specific immunotherapy.