Join us for this in-person talk. Coffee and snacks will be served.

Abstract: Dr. Gulliver noticed 140 years ago that the size of the cell\'s nucleus is proportional to the size of the cell. In the intervening years, similar observations have been made about other, large structures that self-assemble in the cell. This raises a fascinating question: How does the cell, which is micrometers in length, measure its size with nothing more at its disposal than nanometer-sized proteins that diffuse, on occasion bump into each other, and transiently stick together? In this talk I will describe quantitative experiments and related theory that reveal general principles of how cells control the size of their internal structures. The case of self-assembly of actin cables in budding yeast is particularly interesting in this context, as it provides an example of a structure whose size is well matched to the size of the cell. I will describe experiments and theory pertaining to actin cables, and the general principles of cellular self-assembly we are learning from this model system.

This will be a hybrid event. Livestream Link: https://www.youtube.com/watch?v=lufAjs_V5Ys

The Insidious Neutrinos, Entropy, and Gravitational Collapse: what we learn about neutrinos, beyond standard model physics, and the creation of the elements, from the collapse of massive stars

The weakest forces of nature team up to engineer the demise of massive stars, compact objects, and maybe the odd causal horizon volume in the very early universe.

Stars make a Faustian bargain with gravitation and the weak interaction: Energy generation and, hence, promise of a longer life, in exchange for changing composition and the seemingly innocent loss of a little entropy through neutrino emission. It is a good deal for lower-mass stars like the sun. But the price proves to be too high for stars with masses in excess of ~ 8 solar masses, where the neutrino emission-induced loss of entropy and the nonlinear nature of gravitation combine with the weak interaction and exotic nuclear physics to cause collapse of the cores of these stars to neutron stars or black holes. Stars with masses in excess of ~ 100 solar masses likewise are vulnerable to instability because so much of their pressure support comes from radiation.

In fact, the nonlinear nature of gravitation means that self-gravitating systems get into trouble whenever their pressure support involves particles moving near light speed. Such objects are, in the words of my late research mentor, “Trembling on the verge of instability.”

That means that very subtle influences, from known, standard model weak interaction processes, but perhaps also from new, beyond-standard-model physics, can figure in the evolution of these objects. Collapse to neutron stars or black holes is the inevitable outcome, but clues about how these murders were committed may be found in nucleosynthesis (especially of the heaviest nuclei) and in the spectrum of remnant masses.

We will discuss how frontier issues in elementary particle physics, especially those involving the mysterious and ghostlike neutrinos, could figure prominently in what happens in these gravitational collapse events and their aftermath.

Abstract: Two salient features of empirical temporal (i.e., time-varying) network data are the time-varying nature of network structure itself and heavy-tailed distributions of inter-contact times. Both of them can strongly impact dynamical processes occurring on networks, such as contagion processes, synchronization dynamics, and random walks. In the first part of the talk, I introduce theoretical explanation of heavy-tailed distributions of inter-contact times by state-dynamics modeling approaches in which each node is assumed to switch among a small number of discrete states in a Markovian manner and the nodes' states determine time-dependent edges. This approach is interpretable, facilitates mathematical analyses, and seeds various related mathematical modeling, algorithms, and data analysis (e.g., theorizing on epidemic thresholds, random walks on metapopulation models, inference of mixtures of exponential distributions, new Gillespie algorithms, embedding of temporal network data), some of which we will also discuss. The second part of the talk is on modeling of temporal networks by static networks that switch from one to another at regular time intervals. This approach facilitates analytical understanding of diffusive and epidemic dynamics on temporal networks as well as an efficient algorithm for containing epidemic spreading as convex optimization. Finally, I will touch upon some of my interdisciplinary collaborations including those on static networks.

Event will take place in-person in 4448 East Hall and online via Zoom.

Zoom Webinar Link:

https://umich.zoom.us/j/98734707290

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