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Mathematical Biology Seminar Location: Vincent Hall 209, Fridays 3:30 PM

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For more information please email: jyfoo@math.umn.edu
• Friday Feb 10
  Prof. Paul Venturelli (UMN-Fisheries, Wildlife and Conservation Biology)
  "An individual-based model for predicting fish responses to delta formation"
• Friday Feb 17
  Prof. Duane Nykamp (UMN-Mathematics)
  "The influence of network structure on neuronal network dynamics"
• Friday Feb 24
  Prof. Yoichiro Mori (UMN-Mathematics)
  "Pump-leak models of cell volume and electrolyte control"
• Friday Mar 2
  Prof. Patrick Alford (UMN-Biomedical Engineering)
  "Mechano-adaptation in vascular smooth muscle: from hypertension to blast-induced vasospasm"
• Friday Mar 9
  Dr. Matt Parker (UMN Medicine)
  "Noise-induced large fluctuations in population systems"
• Friday Mar 16
  Spring break, no seminar
• Friday Mar 23
  David Dingli, MD, PhD, FRCP, FACP (Division of Hematology, Department of Internal Medicine, and Department of Molecular Medicine, Mayo Clinic, Rochester, MN)
  Title: Stochastic dynamics within hematopoiesis
  Abstract: Hematopoietic stem cells have the ability to self-renew and produce progeny cells that are able to differentiate into all types of circulating blood cells. The number of hematopoietic stem cells is small and they divide slowly. Therefore, stochastic effects may play an important role on the evolution of mutations in such populations. I will discuss estimates of the number of hematopoietic stem cells that are actively contributing to hematopoiesis and the rate of replication of such cells. I will then introduce a stochastic model to understand the dynamics of mutations in these cells. The evolution of such mutations will be discussed in the context of the acquired hematopoietic stem cell disorder paroxysmal nocturnal hemoglobinuria.
• Friday Mar 30
  Cancelled
  title TBA
• Friday Apr 6
  Qixuan Wang (UMN Mathematics)
  Title: 2D swimming at low Reynolds number
  Abstract: Cell migration is crucial for many biological processes. To date, a lot have been done for cells crawling. We are interested in another mode of migration---self-propelled swimming at low Reynolds number, in which the interaction between the cell and the extracellular matrix is absent. By mathematically generating general shape deformations of planar Stokes flow swimmers, we study those factors that play crucial roles in the swimming process and prescribe what kind of shape deformations may lead to more efficient swimming.
• Friday Apr 13
  Cancelled
• Friday Apr 20
  Prof. Meggan Craft
  Title "Predicting disease dynamics in African lion populations"
• Friday Apr 27
  Prof. Vincent Noireaux (UMN-Physics)
  Title: Molecular programming with a cell-free system: from synthetic gene circuits to synthetic bacteriophages.
  Abstract: Synthetic biology is a recent multidisciplinary research area that provides an experimental framework for broadening our knowledge of the molecular repertoire of biology through the construction of complex biochemical systems. While most of the studies are performed in vivo or in silico, only a few cell-free approaches have been proposed. I will present a cell-free transcription-translation toolbox, how it is prepared and how it is used to construct gene circuits in a test tube. In our last experiments, we were able to cell-free synthesize the bacteriophages ΦX174 and T7 from their entire genomes, composed of 13 and 60 genes respectively. We are investigating various aspects of this process, which links information, gene networks and self-organization of a functional biological unit. We are also returning to our original idea of artificial cell, developed by programming phospholipid vesicles with synthetic gene circuits.
• Thurs May 3 (Special Time 4:35 on THURSDAY this week. VinH209 (same room))
  Michael Buice (University of Texas at Austin and National Institutes of Health)
  Title: Neural Kinetic Theory: From Dynamics to Stochastics
  Abstract: The complexity of the human brain is necessarily inherited by theoretical and computational models. I will describe an approach to the mathematical analysis of neural networks which renders this complexity tractable, the application of many-body techniques to complex neural networks. This approach yields tractable equations for the collective dynamics of the network, as well as the network statistics in the form of "multi-neuron correlation functions." I will show how the network level correlations, specifically those arising from finite size effects, can impact fundamental dynamical properties such as stability. In addition, it will be seen that network heterogeneity can give rise to an effective stochastic equation obeyed by the individual neurons.