# Event type: Seminar

## Events

### Bhargav Karamched
(Florida State University)

Mechanisms Underlying Spatiotemporal Patterning in Microbial Collectives

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We describe a spatial Moran model that captures mechanical interactions and directional growth in spatially extended populations. The model is analytically tractable and completely solvable under a mean-field approximation and can elucidate the mechanisms that drive the formation of population-level patterns. As an example, we model a population of E. coli growing in a rectangular microfluidic trap. We show that spatial patterns can arise because of a tug-of-war between boundary effects and growth rate modulations due to cell-cell interactions: Cells align parallel to the long side of the trap when boundary effects dominate. However, when cell-cell interactions exceed a critical value, cells align orthogonally to the trap’s long side. This modeling approach and analysis can be extended to directionally growing cells in a variety of domains to provide insight into how local and global interactions shape collective behavior. As an example, we discuss how our model reveals how changes to a cell-shape describing parameter may manifest at the population level of the microbial collective. Specifically, we discuss mechanisms revealed by our model on how we may be able to control spatiotemporal patterning by modifying cell shape of a given strain in a multi-strain microbial consortium.

### Nancy Rodriguez
(University of Colorado Boulder)

A story on relocation strategies, the Allee effect, and the Ideal Free Distribution

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It is well known that relocation strategies in ecology and in economics can make the difference between extinction and persistence. In this talk I present a unifying model for the dynamics of ecological populations and street vendors, an important part of many informal economies. I discuss the effects of chemotactic movement of populations subject to the Allee Effect by discussing the existence of equilibrium solutions subject to various boundary conditions and the evolution problem when the chemotaxis effect is small. On an interesting note, I present numerical simulations, which show that in fact chemotaxis can help overcome the Allee effect as well as some partial analytical results in this direction on a bounded domain. We can make this precise in unbounded domains. I will conclude by making a connection to the Ideal Free Distribution and other movement strategies under competition.

### Naoki Masuda
(SUNY Buffalo)

Temporal network epidemiology

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Contact networks on which epidemic spreading occurs vary over time. Epidemic processes on such temporal networks are complicated by complexity of both network structure and temporal dimensions. We discuss two mathematical modeling topics on “temporal network epidemiology. First, we analyze how concurrency, i.e., the number of partnerships that an individual (i.e., node of the network) simultaneously owns affects the epidemic threshold. We particularly use a temporal network model with which we can vary the degree of concurrency while preserving the structure of the aggregate, static network. Second, we analyze the epidemic threshold and dynamics when each node switches between a high-activity state and a low-activity state in a Markovian manner. This assumption facilitates theoretical analyses and also allows us to produce distributions of inter-event times resembling heavy-tailed distributions, which are prevalent in empirical data. We argue that it is not the tail of the distribution but the small values of inter-event time that impact epidemic dynamics.

### Richard Bonneau
(Flatiron Institute)

Contracting ML and probabilistic methods for navigating time and space in genomics

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I will describe new methods for spatial transcriptomics and spacial genomics and contrast these methods with previous single cell and longitudinal genomics analysis approaches. I will focus first on methods for determining differential expression for spatial transcriptomic methods. I will then contract these early probabilistic methods with new methods built on variational auto encoders and generative ML approaches. Lastly I will describe bottlenecks, such as integrating imaging and genomic data in these studies. Prospects for building computational pipelines to integrate time, space and genomic coordinate will not be discussed, but will become apparent after deep reflection following the talk (but only if you pay attention).

### Adrian Lam
(Ohio State University)

Some PDEs in Evolution of Dispersal

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In the first part of the talk, we discuss the multi-species competition in a spatial domain, particularly the result of A. Hastings and some recent progress on the conjecture by Dockery et al. concerning the evolution of slow dispersal, i.e. when other things are equal, the slowest diffuser can competitively exclude other competitors. We then discuss the effect of adding passive drift and how it changes the evolutionary dynamics so that fast/intermediate diffuser is selected. In the second part, we will discuss the effect of mutation, and the associated moving Dirac solutions in a nonlocal PDE model proposed by Perthame and Souganidis. This latter equation describes a population structured by space and a phenotypic trait, and can be understood as competition of infinitely many species with different rate of dispersal.

### Lisa Fauci
(Tulane University)

Spinning helices, heaving panels, and waving tails

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The observed gait of a swimmer arises from the interplay of internal force generation, the passive elastic properties of its body, and environmental features such as fluid viscosity, boundaries, and obstacles. One could question whether optimal swimming of a fish occurs when it is actuated at a frequency near a natural frequency determined by its material bending rigidity. We will share some insights that we have gained using computational models of a few systems that range from the Stokes regime to others where inertial forces are important.

### Jordan Rozum
(Pennsylvania State University)

Boolean Networks

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Boolean networks are a common modeling tool for studying the phenotypic changes that cells undergo in response stimuli. Examples include cell differentiation during embryogenesis, metastasis of cancer cells, and apoptosis. In these models, genes and proteins are represented by nodes in a network, and each node is assigned a Boolean activity variable that evolves in discrete time-steps such that the attractors of the resulting dynamics correspond to phenotypes of interest. In this talk, I will focus on the techniques we use to analyze these discrete dynamical systems. These techniques rely on the construction and iterative reduction of an auxiliary network that encodes dynamical information as graph structure (an example is diagrammed in the accompanying figure). Our goals include finding all attractors, identifying key feedback loops that govern attractor selection, and driving the system to a desired attractor from an arbitrary initial state. I will briefly cover several recent applications of these methods to empirical and statistical models. I will also discuss ways in which these discrete models — and the techniques we use to analyze them — are related to their ODE counterparts.

### Katrina Podsypanina
(Institut des Hautes Études Scientifiques [IHES])

Cancer cells and their epithelial neighbors

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I am a cancer researcher working on a framework for a new mathematical model of metastasis. The reason for making a new model is that, in mice, metastases can grow from cells that are still normal at the time of arriving to the future metastasis site. If the same happens in humans, it would be pretty important for cancer therapy. First, it would explain why anti-cancer therapies fail to prevent metastases in some patients: if the cells are yet non-malignant at the time of therapy, they would be spared by the treatment. Second, it may be possible to identify improved treatments based on the ability to kill these non-malignant cells. I hypothesize that dissemination of non-malignant epithelial cells occurs in parallel with tumor cells, and subsequent transformation at the ectopic sites is a source of some metastases. This scenario ties together two contradictory observations: that metastases are associated with large and rapidly growing primary tumors, while metastatic tumors themselves often take a long time to appear. During my recent sabbatical at the Institut des Hautes Etudes Scientifiques, together with Misha Gromov and his colleagues I started to interrogate publicly available data on mutation rates and/or profiles from cancer patients and healthy individuals to determine whether the earliest common ancestor predicted using phylogenetic methods in some primary-metastasis pairs has features of a non-malignant cell.

### Gustavo Martínez-Mekler
(Instituto de Ciencias Físicas, UNAM)

Fertilization Regulatory Networks

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Fertilization is one of the fundamental processes of living systems. Here I will address marine external fertilization and comment on recent work on mammals. I will show experiments that substantiate that sea urchin sperms exhibit chemotaxis as they swim towards the ovum. They are guided by flagellum internal [Ca2+] concentration fluctuations triggered by the binding of chemicals from the oocyte surroundings. Based on experiment, I present a family of logical regulatory networks for the [Ca2+] fluctuation signaling-pathway that reproduce previously observed electrophysiological behaviors and provide predictions, which have been confirmed with new experiments. These studies give insight on the operation of drugs that control sperm navigation. In this systems biology approach, global properties of the [Ca2+] discrete regulatory network dynamics such as

### Adriana Dawes
(Ohio State University)

Antagonistic motor protein dynamics in contractile ring structures

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Ring-shaped contractile structures play important roles in biological processes including wound healing and cell division. Many of these contractile structures rely on motor proteins called myosins for constriction. We investigate force generation by the Type II myosins NMY-1 and NMY-2 in ring channels, contractile structures in developing oocytes of the nematode worm C. elegans, as our model system. By exploiting the ring channel’s circular geometry, we derive a second order ODE to describe the evolution of the radius of the ring channel. By comparing our model predictions to experimental depletion of NMY-1 and NMY-2, we show that these myosins act antagonistically to each other, with NMY-1 exerting force orthogonally and NMY-2 exerting force tangentially to the ring channel opening. Stochastic simulations are currently being used to determine how NMY-1 and NMY-2 may be producing these antagonistic forces, with new tools from topological data analysis identifying persistent ring-like structures in the simulation data.

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https://mathbio.sas.upenn.edu/event_listing_type/seminar/page/4/

https://mathbio.sas.upenn.edu/event_listing_type/seminar/page/2/