Event type: Seminar

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01
Mar

Kelsey Gasior
(Ottawa University)

Mar 1, 2022 Online & DRL Room A1 Seminar
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02
Feb

Hye-Won Kang
(University of Maryland Baltimore County)

Stochastic Modeling of Reaction-Diffusion Processes in Biology
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Inherent fluctuations may play an important role in biochemical and biophysical systems when the system involves some species with low copy numbers. This talk will present the recent work on the stochastic modeling of reaction-diffusion processes in glucose metabolism. The first part of the talk introduces a compartment-based model for a simple glycolytic pathway using a continuous-time Markov jump process, which describes system features at different scales of interest. Then, we will see how the multiscale approximate method reduces the model complexity. We will briefly discuss how the compartment size in the spatial domain can affect the spatial patterns of the system. In the second part of the talk, I will show another example for glucose metabolism where we see different-sized enzyme complexes. We hypothesized that the size of multienzyme complexes is related to their functional roles. We will see two models: one using a system of differential equations and the other using the Langevin dynamics.
Feb 2, 2021 Online Seminar
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15
Mar

Veronica Ciocanel
(Duke University)

Mar 15, 2022 Online Seminar
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09
Feb

Peter Hinow
(University of Wisconsin Milwaukee)

Automated Feature Extraction from Large Cardiac Electrophysiological Data Sets
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A multi-electrode array-based application for the long-term recording of action potentials from electrogenic cells makes possible exciting cardiac electrophysiology studies in health and disease. With hundreds of simultaneous electrode recordings being acquired over a period of days, the main challenge becomes achieving reliable signal identification and quantification. We set out to develop an algorithm capable of automatically extracting regions of high-quality action potentials from terabyte size experimental results and to map the trains of action potentials into a low-dimensional feature space for analysis. Our automatic segmentation algorithm finds regions of acceptable action potentials in large data sets of electrophysiological readings. We use spectral methods and support vector machines to classify our readings and to extract relevant features. We show that action potentials from the same cell site can be recorded over days without detrimental effects to the cell membrane. The variability between measurements 24 h apart is comparable to the natural variability of the features at a single time point. Our work contributes towards a non-invasive approach for cardiomyocyte functional maturation, as well as developmental, pathological, and pharmacological studies. This is joint work with Viviana Zlochiver, Stacie Kroboth (Advocate Aurora Research Institute), and John Jurkiewicz (graduate student at UWM).
Feb 9, 2021 Online Seminar
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16
Feb

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.
Feb 16, 2021 Online Seminar
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23
Feb

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.
Feb 23, 2021 Online Seminar
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02
Mar

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.
Mar 2, 2021 Online Seminar
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09
Mar

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).
Mar 9, 2021 Online Seminar
Next Event
06
Oct

Sarah Olson
(Worcester Polytechnic Institute)

Dynamics of movement in complex environments
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In this talk, we will highlight two different types of movement in viscosity dominated environments
Oct 6, 2020 Online Seminar
Next Event
13
Oct

Naomi Leonard
(Princeton University)

Opinion Dynamics with Tunable Sensitivity
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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
Oct 13, 2020 Online Seminar
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