Mathematical Biology Seminar
Organizers: Alexandru Hening, Tung Nguyen, Anne Shiu
Date Time |
Location | Speaker | Title – click for abstract | |
---|---|---|---|---|
10/29 1:00pm |
BLOC 302 | Arnold Mathijssen University of Pennsylvania |
Non-equilibrium diffusion and transport driven by active matter
Diffusion is essential in biological processes, generating molecular fluxes that sustain life. Classically, these fluxes are described by Fick’s laws for random walks near thermal equilibrium. However, active systems locally inject energy into their surrounding medium, driving non-equilibrium diffusion for which a general theory remains elusive. We distinguish between two types of systems: active baths and active carpets. In the former, suspensions of active particles can enhance mixing and transport cargo using collective hydrodynamic entrainment. For active carpets, biological activity is highly concentrated on surfaces, such as biofilms. They can generate flows to attract nutrients from the bulk, by clustering and forming topological defects. Moreover, active carpets produce anisotropic and space-dependent diffusion, for which we derived generalized Fick’s laws. We solved two archetypal problems using these laws: First, considering sedimentation towards an active carpet, we find a self-cleaning effect where surface-driven fluctuations can repel particles. Second, considering diffusion from a source to an active sink, say nutrient capture by suspension feeders, we derived the enhanced molecular flux compared to thermal diffusion. Hence, our results could elucidate certain non-equilibrium properties of active coating materials and life at interfaces. | |
11/07 3:00pm |
BLOC 624 | Jay Brett Johns Hopkins University |
Modeling Ocean Biology under Climate Change
Mathematical modeling of ecosystems in the ocean allows for examination of the impacts of the changing physical environment on the populations of organisms. In this talk, two examples of interpreting the possible impacts of a warming climate will be considered. The first example is a quantification of the propagation of uncertainty from growth parameters in a phytoplankton model to uncertainty in the change of total growth under climate change. This shows that uncertainty driven by the biological model can be as large as that driven by the physical models. The second example is the development of a coral reef health model to understand how increased bleaching or preventative interventions will impact decadal-scale coral population dynamics. Coral reef loss is a climate tipping point, and the coupling of this model to other climate tipping systems will be discussed. https://bg.copernicus.org/articles/18/3123/2021/ | |
11/11 1:00pm |
BLOC 302 | Daniel Gomez University of New Mexico |
Spikes and Small Targets with Lévy Flights
The fractional order “s” of a Lévy flight controls the algebraic decay of its corresponding jump length distribution. When the fractional order s is s>1/2 , s=1/2, or s<1/2 the corresponding one-dimensional Lévy flights is qualitatively similar to Brownian motion in one-, two-, and three-dimensions. In this talk I will describe how this correspondence emerges in the asymptotic analysis of two fractional problems: the characterization of spike equilibrium solutions to singularly perturbed reaction-diffusion systems, and the analysis of the first hitting-time (FHT) for a Lévy flight to a small target. These two fractional problems are inspired by questions in pattern formation and optimal foraging theory. Our asymptotic results provide qualitative insights into how the fractional order affects the linear stability of spike solutions, as well as how target sparsity determines the optimal fractional order minimizing the first hitting-time. | |
11/22 10:00am |
ZOOM | Declan Stacy University of Virginia |
Stochastic Extinction: An Average Lyapunov Function Approach
In the analysis of stochastic models of ecology, epidemics, and turbulence it is a central problem to determine the stability of an invariant subset M_0 of M for an M-valued Markov process X_t. For example, ecologists wish to determine whether a subset of species will coexist or go extinct. Using average Lyapunov functions we can show that (under some mild technical assumptions) both the stability of M_0 and the rate of convergence of X_t to M_0 (in the stable case) can be determined entirely by a type of Lyapunov exponent which only depends on the steady state behavior(s) of X_t on M_0. This is an improvement over previous results which require constructing a Lyapunov function and analyzing its rate of change on the entirety of M. In the talk we will omit the proofs of the main results and instead focus on examples of applying the theory to various stochastic differential equations and jump processes used to model ecosystems and epidemics. |