Event type: Seminar

During development, cells figure out their location and fate through morphogen concentration gradients. The most commonly accepted mechanism for morphogen gradient formation is diffusion from a local source combined with degradation. Recently, however, there has been growing experimental evidence for an alternative mechanism, based on the direct delivery of morphogens via thin and long cellular protrusions known as cytonemes. In this talk, we will address the effects of various cytoneme transport mechanisms. We then explore the stochasticity from the discrete nature of transport. To deal with the complex nature of the stochastic process, we introduce the strong Markov property and queuing theory.

Interfaces that evolve with a fluid flow abound in nature and engineering. They are subject to intense research in many different fields, from meteorology to medical sciences or the petroleum industry. However, the mathematical analysis of these free boundary problems is still emerging, which impedes a deeper understanding necessary for accurate numerical methods. In this talk, we will give an overview of certain fluid-fluid and fluid-structure interfaces problem (inhomogeneous Navier-Stokes, Boussinesq, Muskat and Peskin models). The focus is placed on global-in-time results (versus finite-time singularities), with initial interfaces that are not just small perturbations. The analysis is thus purely nonlinear. Particular attention is given to initial data in critical spaces and the challenging non-local effects of viscosity contrasts.

Here we consider a game theoretic model of multilevel selection in which individuals compete based on their payoff and groups also compete based on the average payoff of group members. Our focus is on the Prisoners’ Dilemma: a game in which individuals are best off cheating, while groups of individuals do best when composed of many cooperators. We analyze the dynamics of the two-level replicator dynamics, a nonlocal hyperbolic PDE describing deterministic birth-death dynamics for both individuals and groups. Comparison principles and an invariant property of the tail of the population distribution are used to characterize the threshold level of between-group selection dividing a regime in which the population converges to a delta function at the equilibrium of the within-group dynamics from a regime in which between-group competition facilitates the existence of steady-state densities supporting greater levels of cooperation. In particular, we see that the threshold selection strength and average payoff at steady state depend on a tug-of-war between the individual-level incentive to be a defector in a many-cooperator group and the group-level incentive to have many cooperators over many defectors. We also find that lower-level selection casts a long shadow: if groups are best off with a mix of cooperators and defectors, then there will always be fewer cooperators than optimal at steady state, even in the limit of infinitely strong competition between groups.

Turing instabilities in reaction diffusion systems describe potential mechanisms for pattern formation in qualitative models of microbiological processes. A recent direction of research has been to incorporate bulk-membrane coupling (BMC) into these models which introduces a process of attachment and detachment to and from the cell membrane. In these models chemical species can therefore undergo periods of bulk- and membrane-bound diffusion in addition to prescribed kinetics. Linear stability analysis and numerical simulations have revealed that differences between membrane and cytosol diffusivities can trigger Turing-like pattern-forming instabilities in BMC models. We further investigate the role of bulk-membrane coupling by analyzing its effect in a singularly perturbed model where the diffusivity of one membrane-bound species is asymptotically small. In this context, localized solutions are known to exist and can be approximated using asymptotic methods. Additionally, the linear stability and long time dynamics of these localized solutions leads to novel non-local eigenvalue problems and differential-algebraic systems. In this talk we will outline this asymptotic framework and highlight the role of bulk-membrane coupling in the stability properties of localized solutions.

I will discuss two mathematical problems in biological pattern formation. The first is on modeling concentric ring patterns formed in engineered bacterial colonies. I will first present a hybrid model that incorporates a detailed description of cell movement and cell signaling and explains the underlying mechanism of the ring pattern. I will then present a PDE model derived from the hybrid model that captures the biological phenomena equally well. The second concerns spatial pattern formation in reaction-diffusion systems. I will discuss a computational method that can be used to discover potentially all nonuniform steady states of the PDE system, describing the structure of the spatial patterns. The method uses techniques from numerical algebraic geometry. This talk is based on joint work with Min Tang from Shanghai Jiaotong University and Wenrui Hao from Penn State University.

Bayesian posterior distributions on phylogenetic trees remain difficult to sample despite decades of effort. The complex discrete and continuous model structure of trees means that recent inference methods developed for Euclidean space are not easily applicable to the phylogenetic case. Thus, we are left with random-walk Markov Chain Monte Carlo (MCMC) with uninformed tree modification proposals; these traverse tree space slowly because phylogenetic posteriors are concentrated on a small fraction of the very many possible trees. In this talk, I will describe our wild adventure developing efficient alternatives to random-walk MCMC, which has concluded successfully with the development of a variational Bayes formulation of Bayesian phylogenetics. This formulation leverages a “factorization

Neurons make use of their complex cellular and intracellular architecture to process and guide electrical and biochemical signals. To study this structure-function interplay, computational methods are detremental, since many parameters are not directly accessible in an experimental setting. This also means that the detailed three-dimensional morphology of cells and organelles needs to be included in modeling and simulation, which results in complex-domain problems, described by systems of coupled, nonlinear, partial differential equations. We have developed numerical discretization methods and fast solvers to address this general type of biological problem set, with a focus on optimal weak scalability on High Performance Computing infrastructures. We present some of the important biological problems revolving around cellular calcium signaling, coupled to electrical models, and the use of our NeuroBox Toolbox and the multiphysics platform uG4 to solve such ultrastructural 3D neuron models. Selected results show how neurons are capable of using their (intra)cellular architecture to fine-tune their response to exterior/network input.