Mathematical Biology Seminar
Location: Vincent Hall 209, Fridays 3:30 PM
For more information please email:
- 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
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
- 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
- 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
gene circuits to synthetic bacteriophages.
Abstract: Synthetic biology is a recent multidisciplinary
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))
(University of Texas at Austin and National Institutes of Health)
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.