Scientific

This is the prototype of an agent based model for a closed universe of a population experiencing a contagion-based epidemic, in which risk factors, movement, time of incubation and asymptomatic infection are all parameters. The model allows the operator to intervene at any step and change parameters, thus analytically visualizing the effect of policies like more testing, contract tracing, and shelter in place. Under current development, CovidSimMV is an ABM that supports a Multiverse of different environments, in which agents move from one to another according to ticket with stops. Each universe has its own characteristic mix of residents, transients and attached staff, and persons are able to adopt different roles and characteristics in different universes. The fundamental disease characteristics of incubation, asymptomatic infection, confirmed cases will be preserved. The Multiverse model will support a rich diversity of environments and interpersonal dynamics. These are JavaScript programs that can be run in a browser as HTML files. The code is open source, and available on github.com/ecsendmail.

Given a probability preserving system (X, \mu, T) and a set U of positive measure contained in X we denote by N(U,U) the set of integers n such that the measure of U intersected with T^n(U) is positive. These sets are called return-time sets and are of very special nature. For instance, Poincaré recurrence theorem tells us that the set must have bounded gaps while Sarkozy-Furstenberg theorem tells us that it must have a square. The subject of this talk is a very old question (going back to Følner-1954 if not earlier) whether they give rise to the same family of the sets as when we restrict ourselves to compact group rotations. This was answered negatively by Kříž in 1987 and recently it was proved by Griesmer that a return-time set need not contain any translate of a return-time set arising from compact group rotations. In this talk, I will try to sketch some of these proofs and give a flavour of results and questions in this direction.

Diffusion is the enemy of life. This is because diffusion is a ubiquitous feature of molecular motion that is constantly spreading things out, destroying molecular aggregates. However, all living organisms, whether single cell or multicellular have ways to use the reality of molecular diffusion to their advantage. That is, they expend energy to concentrate molecules and then use the fact that molecules move down their concentration gradient to do useful things. In this talk, I will show some of the ways that cells use diffusion to their advantage, to signal, to form structures and aggregates, and to make measurements of length and size of populations. Among the examples I will describe are signalling by nerves, cell polarization, bacterial quorum sensing, and regulation of flagellar molecular motors. In this way, I hope to convince you that living organisms have made diffusion their friend, not their enemy.

Furstenberg's conjecture on the dimension of the intersection of x2,x3-invariant Cantor sets can be restated as a bound on the dimension of linear slices of the product of x2,x3-Cantor sets, which is a self-affine set in the plane. I will discuss some older and newer variants of this, where the self-affine set is replaced by a self-similar set such as the Sierpinski triangle, Sierpinski carpet or (support of) a complex Bernoulli convolution. Among other things, I will show that the intersection of the Sierpinski carpet with circles has small dimension, but on the other hand the Sierpinski carpet can be covered very efficiently by linear tubes (neighborhoods of lines). The latter fact is a recent result joint with A. Pyörälä, V. Suomala and M. Wu.

Multiscale multicellular models combine representations of subcellular biological networks, cell behaviors, tissue level effects and whole body effects to describe tissue outcomes during development, homeostasis and disease. I will briefly introduce these simulation methodologies, the CompuCell3D simulation environment and their applications, then focus on a multiscale simulation of an acute primary infection of an epithelial tissue infected by a virus like SARS-CoV-2, a simplified cellular immune response and viral and immune-induced tissue damage. The model exhibits four basic parameter regimes: where the immune response fails to contain or significantly slow the spread of viral infection, where it significantly slows but fails to stop the spread of infection, where it eliminates all infected epithelial cells, but reinfection occurs from residual extracellular virus and where it clears the both infected cells and extracellular virus, leaving a substantial fraction of epithelial cells uninfected. Even this simplified model can illustrate the effects of a number of drug therapy concepts. Inhibition of viral internalization and faster immune-cell recruitment promote containment of infection. Fast viral internalization and slower immune response lead to uncontrolled spread of infection. Existing antivirals, despite blocking viral replication, show reduced clinical benefit when given later during the course of infection. Simulation of a drug which reduces the replication rate of viral RNA, shows that a low dosage that provides only a relatively limited reduction of viral RNA replication greatly decreases the total tissue damage and extracellular virus when given near the beginning of infection. However, even a high dosage that greatly reduces the rate of RNA replication rapidly loses efficacy when given later after infection. Many combinations of dosage and treatment time lead to distinct stochastic outcomes, with some replicas showing clearance or control of the virus (treatment success), while others show rapid infection of all epithelial cells (treatment failure). This switch between a regime of frequent treatment success and frequent failure occurs is quite abrupt as the time of treatment increases. The model is open-source and modular, allowing rapid development and extension of its components by groups working in parallel.