Event type: Seminar
Events
Fernando Pascoal Dos Santos
(University of Amsterdam)
The complexity of human cooperation under indirect reciprocity
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Indirect reciprocity is a key mechanism to understand the evolution of cooperation among unrelated individuals. It involves reputations and complex information processing, arising from social interactions. By helping someone, individuals may improve their reputation, which may be shared in a population and change the predisposition of others to reciprocate in the future. The reputation of individuals depends, in turn, on social norms that define a good or bad action, offering a mathematically appealing way of studying the evolution of moral systems. Over the years, theoretical and empirical research has unveiled many features of cooperation under IR, exploring norms with varying degrees of complexity and information requirements. Often, however, it is assumed that reputations spread for free, and individuals can adopt arbitrarily complex norms and strategies. In this presentation, I will discuss a way to quantify the complexity of norms and strategies in indirect reciprocity and the challenges introduced by complex rules and costly reputation sharing. We will observe that cooperation can prevail with simple norms and costly reputation sharing, provided that individuals can anticipate the reporting intentions of their opponents. I will conclude by discussing future research directions and the prospects of applying mathematical biology methods to design prosocial artificial systems.
Anita Layton
(University of Waterloo)
His and Her Mathematical Models of Physiological Systems
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Imagine someone having a heart attack. Do you visualize the dramatic Hollywood portrayal of a heart attack, in which a man collapses, grabbing his chest in agony? Even though heart disease is the leading killer of women worldwide, the misconception that heart disease is a men’s disease has persisted. A dangerous misconceptions and risks women ignoring their own symptoms. Gender biases and false impressions are by no means limited to heart attack symptoms. Such prejudices exist throughout our healthcare system, from scientific research to disease diagnosis and treatment strategies. A goal of our research program is to address this gender equity, by identifying and disseminating insights into sex differences in health and disease, using computational modeling tools.
Marte Julie Sætra
(Simula Research Laboratory)
Computational modeling of ion concentration dynamics in brain tissue
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Over the past decades, computational neuroscientists have developed ever more sophisticated and morphologically complex neuron models. Most of these models assume that the intra- and extracellular ion concentrations remain constant over the simulated period and thus do not account for concentration-dependent effects on neuronal firing properties. Of the models that do incorporate ion concentration dynamics, few account for the electrodiffusive nature of intra- and extracellular ion transport. In this talk, I will present the first multicompartmental neuron model that accounts for ion concentration dynamics in a biophysically consistent manner [1]. I will also show how electrodiffusive modeling of neurons and glial cells can be used to explore the genesis of slow potentials in the brain [2].
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.
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https://mathbio.sas.upenn.edu/event_listing_type/seminar/page/2/
https://mathbio.sas.upenn.edu/event_listing_type/seminar/
