Event type: Seminar

Cell migration plays a central role in embryonic development, wound healing and immune surveillance. In 2008, Yoichiro Mori, Alexandra Jilkine and I published a model for the initial step of cell migration, the front-back chemical polarization that sets a cell’s directionality. (More detailed mathematical properties of this model were described by the same group in 2011.) Since then, progress has been made in investigating how that simple “wave-pinning" mechanism is shaped and tuned by feedback from other proteins, such as actin, from the cell’s environment (extracellular matrix), from interplay with larger signaling networks, and from cell-cell interactions. In this talk I will describe some of this progress, with emphasis on links to experiments on melanoma cell motility. If time permits, I will also briefly describe more recent work on collective cell migration that we are currently undertaking.

In this talk, we will highlight two different types of movement in viscosity dominated environments

I will present a general model of continuous-time opinion dynamics for an arbitrary number of agents that sense or communicate over a network and form real-valued opinions about an arbitrary number of options. Drawing from biology, physics, and social psychology, an attention parameter is introduced to modulate social influence and a saturation function to bound inter-agent and intra-agent opinion exchanges. This yields simply parameterized dynamics that exhibit the range of opinion formation behaviors predicted by model-independent bifurcation theory but not exhibited by linear models or existing nonlinear models. Behaviors include rapid and reliable formation of multistable consensus and dissensus states, even in homogeneous networks, as well as ultra-sensitivity to inputs, robustness to uncertainty, flexible transitions between consensus and dissensus, and opinion cascades. Augmenting the opinion dynamics with feedback dynamics for the attention parameter results in tunable thresholds that govern sensitivity and robustness. The model provides new means for systematic study of dynamics on natural and engineered networks, from information spread and political polarization to collective decision making and dynamic task allocation. This is joint work with Alessio Franci (UNAM, Mexico) and Anastasia Bizyaeva (Princeton). The talk is based on version 2 of the paper “A General Model of Opinion Dynamics with Tunable Sensitivity”, which will be available on Tuesday October 13, 2020 here: https://arxiv.org/abs/2009.04332v2

In the domain of computational/theoretical neuroscience a recently revived question is about the complexity of neural data. This question can be tackled by studying the dimensionality of such data: is neural activity high or low dimensional? How does the geometrical structure of neural activity depend on behavior, learning or the underlying connectivity? In my talk I will show how it is possible to link these three aspects (animal behavior, learning and underlying network connectivity) to the geometrical properties of neural data, with an emphasis on dimensionality phenomena. My results depart from neural recordings and aim at building understanding of neural dynamics by means of theoretical and computational tools. Such tools are mainly borrowed from the domain of neural networks dynamics, using a blend of large scale dynamical systems and statistical physics approaches.

Wild-type zebrafish (Danio rerio) are characterized by black and yellow stripes, which form on their body and fins due to the self-organization of thousands of pigment cells. Mutant zebrafish and sibling species in the Danio genus, on the other hand, feature altered, variable patterns, including spots and labyrinth curves. The longterm goal of my work is to better link genotype, cell behavior, and phenotype by helping to identify the specific alterations to cell interactions that lead to these different fish patterns. Using a phenomenological approach, we develop agent-based models to describe the behavior of individual cells and simulate pattern formation on growing domains. In this talk, I will overview our models and highlight how topological techniques can be used to quantitatively compare our simulations with in vivo images. I will also discuss future directions related to taking a more mechanistic approach to modeling cell behavior in zebrafish.