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Back to Current MBI Seminars
Past
MBI Seminars: 2007-2008
Recruiting Talks:
Tuesday, January 22, 12:30pm
Cockins Hall, Room 240
Speaker: Patrick Shipman
Title: Growth and Symmetry: Pattern Formation on Plants
Tiling planforms dominated by diamonds (such as the diamond-shaped seeds
on a sunflower head), hexagons, or ridges (such as those on saguaro
cacti) are observed on many plants. We analyze PDE models for the
formation of these patterns that incorporate the effects of growth and
biophysical and biochemical mechanisms. The aim is to understand both the
underlying symmetries and the information specific to the mechanisms. The
patterns are compared to Voronoi tessellations, and we will start to draw
a bigger picture of growth and symmetry in biological systems.
Friday, February 1, 12:30pm
Cockins Hall, Room 240
Speaker: Samuel Isaacson
Title: Mathematical Problems From Molecular Cell Biology
We will explain the need for stochastic reaction-diffusion models appropriate for studying the dynamics of gene and signaling networks within biological cells. In particular, we will describe our work developing a stochastic reaction-diffusion method that can incorporate the complex geometry of cellular architecture, and the application of this method to a model for eukaryotic gene expression and nuclear transport. This work raised the question of what the reaction-diffusion master equation (RDME), a lattice based stochastic reaction-diffusion model, approximates as the lattice spacing is decreased. We will discuss our recent work proving that in the continuum limit reaction effects are lost in the RDME model. While this may seem a negative feature, we will also show how the RDME for finite lattice spacings may be interpreted as an asymptotic approximation to a spatially-continuous stochastic reaction-diffusion model due to Smoluchowski. We will conclude with a brief introduction to a new, long term, modeling project we have begun, developing stochastic-reaction diffusion models of gene/signaling networks involved in several cardiac conditions.
Wednesday, February 6, 10:00am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Xiaoming Zheng
Title: Numerical Simulations of tumor growth, angiogensis and chemotherapy
Mathematical modeling and computer simulations play increasingly important roles in cancer research, and the most important one is providing a framework to capture various mechanisms underlying tumor growth and angiogenesis(new blood vessels formation). A tumor develops through avascular, angiogenic and vascularized stages to be malignant. Initially a tumor utilizes nutrient diffusing from parent vessels to grow but only up to a limited size (~2mm in diameter). The key to reach malignant stage is the angiogenesis process, where the tumor obtains extra source of nutrient and begins to grow out of control. In this talk, I will first present a mathematical model serving as framework for cancer research to simulate the growth of a tumor through all these stages, then describe a chemotherapy model to predict drug efficacy in the condition of vascular and morphological heterogeneity. Finally I will present a new angiogenesis model that addresses the proper relationship between endothelial cells (lining up the blood vessels) proliferation and migration, which is the key to understand vasculogenesis (formation of blood vessel plexus in embryo) and angiogenesis processes.
Tuesday, February 12, 12:30pm
Cockins Hall, Room 240
Speaker: Xiao Wang
Title: Mathematical Modeling of Synthetic Networks Reveals Noise-induced Gene Regulation Mechanisms
Bistable systems are very common modules in natural biological systems. In this work, well-characterized biological components are used to construct a genetic toggle switch in S. cerevisiae through mutual inhibition. Mathematical modeling is combined with molecular biology to design and construct the genetic toggle switch. We show that, guided by modeling predictions, we can achieve bistability by tuning the system. I will illustrate the artificial "cell differentiation", both experimentally and mathematically, by starting the switch from the third state, which represents the state that expression of both repressors are turned off. This work demonstrates the use of synthetic gene networks to uncover general regulatory mechanisms in natural biological systems.
Monday, February 18, 12:30pm
Cockins Hall, Room 240
Speaker: Daniel Coombs
Title: Mathematical approaches to understanding T cell activation
T cells of the immune system are activated by interactions with antigen-presenting cells (APC). T cell receptors (TCR) on the T cell surface transiently bind to defined signatures of infection (antigens) on the APC. Productive TCR-antigen binding leads to biochemical signals within the T cell and an immune response. T cell responses occur even when the antigen is present at very low concentrations. It has been suggested that during the T cell-APC interaction, over the course of minutes to hours, presented antigens can bind to a series of TCR and that such "serial engagement" is a key determinant of the T cell response. In this talk I will describe the biological questions in more detail and show how mathematical models can be used to interpret experimental data and propose experimentally testable hypotheses.
Tuesday, February 19, 12:30pm
Cockins Hall, Room 240
Speaker: Andrew Nevai
Title: Spatial problems in mathematical ecology
In this talk, I will introduce two spatial problems in theoretical
ecology together with their mathematical solutions.
The first part of the talk concerns competition between plants for
sunlight. In it, I use a mechanistic Kolmogorov-type competition model
to connect plant population vertical leaf profiles (or VLPs) to the
asymptotic behavior of the resulting dynamical system. For different
VLPs, conditions can be obtained for either competitive exclusion to
occur or stable coexistence at one or more equilibrium points.
The second part of the talk concerns the spatial spread of infectious
diseases. Here, I use a family of SI-type models to examine the ability
of a disease, such as rabies, to invade or persist in a spatially
heterogeneous habitat. I will discuss properties of the disease-free
equilibrium and the behavior of the endemic equilibrium as the mobility
of healthy individuals becomes very small relative to that of infecteds.
The family of disease models consists variously of systems of difference
equations (which I will emphasize), ODEs, and reaction-diffusion
equations.
Wednesday, February 20, 11:30am
Cockins Hall, Room 240
Speaker: Luca Giuggioli
Title: Anomalous diffusion in animal movement
A growing amount of evidence points to the fact that
animals do not simply move by perfoming a Brownian walk.
By playing with the time spent before taking the next
steps as well as with the length of those steps, an animal
can perform more exotic random walks and optimize its movement statistics
according to the situation. I will show how mathematical formalisms such as the
so-called Generalized Master Equation or the differential equation
describing Fractional Brownian motion are good candidates
for describing anomalous statistics in movement patterns.
Application of these ideas to movement patterns of
some Mediterranean seabird (Puffinus mauretanicus and Calonectris diomedea)
as well as some rodent species, such as the deer mouse, has been instrumental
in quantifying their food searching strategies.
Tuesday, February 26, 11:30am
Cockins Hall, Room 240
Speaker: Sivan Rottenstreich
Title: A Coalescent Analysis of the Population Structure Statistic $F_{st}$
Populations are often divided into subpopulations. Determining whether a population evolves as a single population or as several interacting subpopulations is a crucial step in answering many genetic and ecological questions. Biologists use a statistic called $F_{st}$ to perform hypothesis tests for the existence of population subdivision and to estimate migration rates between subpopulations.
In this talk I consider $F_{st}$ under a stochastic model of evolution for a subdivided population. I use coalescent theory to prove rigorous results describing the distribution of $F_{st}$ under this model. I show that under different scaling limits on the parameters of the model, $F_{st}$ converges to different distributions in the large population limit. I show that the key quantity in determining the distribution of $F_{st}$ is the product of mutation rate and population size. Finally, I comment on the implication of my results for biological applications.
Seminar Series:
Friday, September 21, 10:30-11:30am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Gary Stormo, Department of Genetics, Washington University School of Medicine
Title: Experimental and Computational Approaches to Protein-DNA Interactions
and Gene Regulation
This talk will describe some of our combined experimental and
computational
methods for determining the specificity of DNA-binding proteins and
for discovering regulatory sites in genomic DNA sequences. It will cover
aspects of the algorithms we have developed and the types of experiments
we employ to test the predictions and refine the models. Examples from
bacteria, yeast and worms will be described.
Tuesday, October 16, 3:30-4:30pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Chih-Wen Shih, MBI - Long-term visitor
Title: Multistability and Convergence of Dynamics for Hopfield-type Neural Networks with Delays
I will present our recent works on the existence of multiple stationary solutions and convergence of dynamics (every solution converges to one of the equilibria as time goes to infinity) for the Hopfield-type neural networks with delays. The theory is obtained through formulating parameter conditions motivated by a geometrical observation on the single neuron equation. We first construct 3^n equilibria for the n-neuron network. Positively invariant regions for the flows generated by the system and basins of attraction for the 2^n, among these 3^n, stationary solutions are established. The theory is also extended to the existence of 2^n limit cycles for the networks with time-periodic inputs. For the convergence of dynamics, some conditions can be imposed to show that the system is strongly order preserving so that quasiconvergence is generic for the networks, as the self-feedback time lags are small for the neurons with negative self-connection weights.
A modified formulation which bears a spirit of ignoring the delays can also be developed to derive certain componentwise dynamical property. An iteration argument is then constructed to conclude that every solution of the network converges to a single equilibrium as time tends to infinity.
Friday, October 26, 1:30-2:30pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Astrid Prinz, Department of Biology, Emory University
Title: Neuron and neuronal network stability
Monday, October 29, 1:30-2:30pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Peter Thomas, Department of Mathematics, Case Western Reserve University
Title: Noise-induced limit cycle transitions in coupled oscillator networks
Many motor control systems contain central pattern generator
(CPG) neural networks. CPG networks typically can produce one or more
stable rhythmic behaviors, for example corresponding to different
quadruped gaits. In the marine mollusk {\it Aplysia californica}, a CPG
network controlling feeding movements can produce biting, swallowing and
rejection behaviors by changing the phase relationships of activity in
individual network units. We investigate simplified $D_4$-equivariant
models of a CPG neural network in which two stable limit cycles coexist,
with the goal of understanding how noise (stochastic perturbations)
might facilitate switching from one activity pattern to another. In
particular we investigate conditions under which random
perturbations of the deterministic network dynamics are simultaneously
weak enough to preserve the form of the deterministic limit cycle
attractors, and strong enough to induce spontaneous switching between
them.
Joint work with Hillel J. Chiel (Biology)
Case Western Reserve University.
Tuesday, November 6, 1:30-2:30pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Seth Sullivant, Department of Mathematics, Harvard University
Title: Algebraic statistical models
Many statistical models have algebraic structure in that
they are defined either parametrically or implicitly by polynomial
conditions on a natural parameter space. These algebraic
statistical models are ubiquitous in statistics and the algebraic
structure can often be exploited to answer statistical or
probabilistic questions. I will try to illustrate these two points
with examples of Gaussian conditional independence models,
conditional inference for log-linear models, and algebraic
invariants of phylogenetic models.
Friday, November 9, 3:30-4:30pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Ronald Xu, Department of Biomedical Engineering, The Ohio State University
Title: Intraoperative, Multimodal, Dynamic Imaging of Cancer
Accurate assessment of tumor margins and the recognition of occult disease within adjacent peritumoral tissues and regional lymph node basins are important oncologic principles that help to minimize recurrence rates and improve long-term patient outcomes. However, most of currently utilized modalities for cancer detection and imaging are single modal, static, and focus primarily on preoperative image acquisition. Although each modality contributes a piece of a complex puzzle, co-registration between different modalities becomes a significant challenge, especially in a dynamic, intraoperative environment such as an operating room. We are developing portable multi-modal imaging systems for intraoperative, dynamic cancer detection and imaging. In this talk, I will address the current status of the technology development, the clinical potentials, and the need for mathematicians' contribution.
Tuesday, November 20, 1:30-2:30pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Dominik Wodarz, Department of Ecology and Evolutionary Biology, University of California, Irvine
Title: HIV coinfection, viral evolution in vivo, and the development of AIDS
It is commonly thought that virus evolution in vivo can contribute to or
correlate with the progression of HIV infection from the asymptomatic phase
towards AIDS. The virus evolves towards immune escape, increased replication
kinetics, and a higher degree of cell killing, leading to the depletion of
the T helper cell population. Mathematical models of in vivo HIV evolution
have been useful in shaping our understanding of the disease process.
However, the models considered so far assume that one cell can only harbor
one virus particle. Recent data, however, indicate that one cell can be
infected by more than one virus particle, a process called co-infection. I
will discuss a mathematical model that studies the effect of co-infection on
HIV evolution in vivo and on the process of disease progression. This gives
rise to some counter-intuitive insights that find some support in
experimental data. It also gives rise to a theory for why natural SIV
infection does not progress to AIDS despite the presence of high virus loads
and high virus diversity in some cases.
Tuesday, November 27, 3:30-4:30pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Marshall Hampton, Department of Mathematics, University of Minnesota, Duluth
Title: Modeling and Mysteries of Mammalian Hibernation
Hibernation in small mammals involves extreme physiological changes, as well as some puzzling dynamics. Every one to two weeks, hibernating mammals arouse to normal temperatures but do not eat or drink. This consumes most of their fat reserves. Why do they do it? After reviewing the phenomenon of mammalian hibernation, a simple model based on one possible answer to that question will be presented (joint work with Matthew T. Andrews). Some future directions in modeling, experiment, and bioinformatics will be described as well.
Friday, November 30, 3:30-4:30pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Kun Huang, Department of Biomedical Informatics, OSU
Title: Imaging-Based Phenotyping
We present a workflow designed to quantitatively characterize the 3-D
structural attributes of macroscopic tissue specimens acquired at a micron
level resolution using light microscopy. This workflow includes four major
components: (i) Serial-section image acquisition, (ii) image
preprocessing, (iii) image analysis involving 2-D pair-wise registration,
2-D segmentation and 3-D reconstruction, and (iv) visualization and
quantification of phenotyping parameters. Several new algorithms have been
developed within each workflow component. The biological applications of
our work include a study of the morphological change in a mouse placenta
induced by knocking out the retinoblastoma gene and our ongoing study on
breast tumor microenvironment modeling.
Tuesday, December 4, 3:30-4:30pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Gregory Smith, Department of Applied Science, The College of William and Mary
Title: Modeling local control of calcium-induced calcium release in cardiac myocytes
I will present a probability density approach to modeling localized Ca influx via L-type Ca channels and Ca-induced Ca release mediated by clusters of ryanodine receptors during excitation-contraction coupling in cardiac myocytes. Coupled advection-reaction equations are derived relating the time-dependent probability density of subsarcolemmal subspace and junctional sarcoplasmic reticulum [Ca] conditioned on "Ca release unit" state. When these equations are solved numerically using a high-resolution finite difference scheme and the resulting probability densities are coupled to ordinary differential equations for the bulk myoplasmic and sarcoplasmic reticulum [Ca], a realistic but minimal model of cardiac excitation-contraction coupling is produced. Modeling Ca release unit activity using this probability density approach avoids the computationally demanding task of resolving spatial aspects of global Ca signaling, while accurately representing heterogeneous local Ca signals in a population of diadic subspaces and junctional sarcoplasmic reticulum depletion domains. The probability density approach is validated for a physiologically realistic number of Ca release units and benchmarked for computational efficiency by comparison to traditional Monte Carlo simulations. [This is joint work with George S. B. Williams, Marco A. Huertas, Eric A. Sobie, and M. Saleet Jafri.]
Friday, December 7, 3:30-4:30pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Jon Bell, University of Maryland, Baltimore County
Title: On the notion of degeneracy as applied to immunology and population dynamics and an introduction to an inverse problem in neuroscience
I will discuss two projects I have worked on this fall. The first I'll title "Degeneracy-driven dynamics of selective repertoires". A repertoire of recognizers (say antibodies, enzymes, predators, ...) rely on a diversity of resources (respectively, signals like antigens, substrates, prey,...). Biological interactions are often not specific. This limited specificity is called "degeneracy" (Edelman and Gally, PNAS, 2001). We will introduce degeneracy into population models (the Verhulst and Lotka-Volterra models) and show some preliminary analysis and numerical behavior of the generalized Verhulst model. The second topic is titled "Identifying dendritic tree structure from voltage measurements". Given various assumptions on the class of dendrites, what types of voltage and current data imposed at the soma and/or terminal points can one use to determine the topological structure of the dendrite. I will discuss some background on dendrites and introduce assumptions and an approach to address one such inverse problem.
Tuesday, December 11, 3:30-4:30pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Raghu Machiraju, Department of Computer Science and Engineering, The Ohio State University
Title: Reconstructing Cellular Architectures from Microscopy Data
There is much interest in creating 3D models of cellular architecture of various tissue samples. Such tissue samples are often obtained to support phenotyping studies for cancer research. They are available for later analysis in the form of 3D microscopy images (confocal, histology, etc.). We describe a method that uses a series of segmentation and geometric visualization algorithms to reconstruct the cellular architecture as 3D models. By inspecting these 3D models one can fathom the changes wrought to cellular arrangements with the progression of disease. To demonstrate the efficacy of our methods, we describe their deployment on two specific phenotyping studies.
To be announced
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Yannis Kevrekidis, Department of Applied and Computational Mathematics, Princeton University
Title: Equation-Free Modeling and Computation for Complex/Multiscale Systems
In current modeling practice for complex/multiscale systems, the best available descriptions often come at a fine level (atomistic, stochastic, microscopic, individual-based) while the questions asked and the tasks required by the modeler (prediction, parametric analysis, optimization and control) are at a much coarser, averaged, macroscopic level. Traditional modeling approaches start by first deriving macroscopic evolution equations from the microscopic models, and then bringing our arsenal of mathematical and algorithmic tools to bear on these macroscopic descriptions. Over the last few years, and with several collaborators, we have developed and validated a mathematically inspired, computational enabling technology that allows the modeler to perform macroscopic tasks acting on the microscopic models directly.
We call this the "equation-free" approach, since it circumvents the
step of obtaining accurate macroscopic descriptions. We will argue that the backbone of this approach is the design of (computational) experiments. Traditional continuum numerical algorithms can thus viewed as protocols for experimental design (where "experiment" means a computational experiment set up and performed with a model at a different level of description). Ultimately, what makes it all possible is the ability to initialize computational experiments at will. Short bursts of appropriately initialized computational experimentation through matrix-free numerical analysis and systems theory tools like variance reduction and estimation- bridge microscopic simulation with macroscopic modeling. I will also discuss some recent developments in data mining algorithms, exploring large complex data sets to find good "reduction coordinates".
Tuesday, January 8, 3:30-4:30pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Tim Newman, Department of Physics, Arizona State University
Title: Modeling multicellularity: from cell rheology to gastrulation
I will present an overview of recent work focused on constructing computer models of developmental systems. In particular, we have devised an algorithm, called the Subcellular Element Model, which is able to simulate large numbers (thousands) of deformable cells in three dimensions. I will describe the inner workings of the algorithm, and indicate that the method is capable of modeling developmental systems over a wide range of scales - capturing cell visco-elasticity at small scales, and long-ranged coordinated cell movement at large scales. Modeling of primitive streak extension in the chick embryo will be discussed as a concrete application.
Tuesday, January 22, 3:30-4:30pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: George Karniadakis, Department of Applied Mathematics, Brown University
Title: Effect of Red Blood Cells on Platelet Aggregation: DPD simulations
The growth rate of platelet thrombi in vessels as affected by blood flow rate has been the subject of in vivo experiments, theory and computer modeling. In this talk, we will first review a relatively new
mesoscopic method, Dissipative Particle Dynamics or DPD, which is particularly suited to modeling complex fluids. DPD can be seamlessly interfaced with MD and the
Navier-Stokes equations for a multiscale modeling approach. I will then apply the method to platelet aggregation with and without the presence of red blood cells (RBCs). Finally, I will
discuss recent results on modeling RBCs at the spectrin level and
how to coarse-grain RBCs models using mean field theory to obtain
effective properties.
Tuesday, February 19, 3:30-4:30pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Cassandra L. Smith, Department of Biomedical Engineering, Boston University
Title: The Nature and Nurture of Schizophrenia
Our research program focuses on understanding the biochemical connections between genetic and environmental factors linked to schizophrenia, with a long-term goal of preventing and/or reducing illness severity. Disparate results by us and others, on schizophrenia, and related severe neuropsychiatric illnesses, point to common metabolic perturbances, and/or hypersensitivities in the balance between folate, methionine, and sulfur metabolism. These intersecting pathways require the essential nutrients: folate (vitamin B9), cobolamin (vitamin B12), vitamin B6 and methoinine. Folate derivatives are required for the biosynthesis of nucleic acids (RNA and DNA), glutathione, S-adenosyl methionine (SAM), proteins, and lipids. The synthesis of glutathione, the major intracellular antioxidant and product of the transsulfuration pathway, competes with the synthesis of SAM, the major product of the methionine pathway. More than 100 methyl transferases use SAM in vivo including enzymes linked to DNA methylation (an epigenetic phenomenon), dopamine metabolism and schizophrenia. Schizophrenia is linked to multiple enzymes in these pathways through genetic studies. Environmental factors (e.g. paternal age, maternal starvation, folate deficiency, and infections) linked to disease impact these pathways and occur early in development. Early life is uniquely associated with exceptionally large amounts of DNA replication and post-replication DNA methylation (epigenetic) changes. Severe perturbations to the intersecting metabolic pathways and/or the balance between them will be lethal to a cell. However, less severe perturbations will have multiple complex cellular effects. In fact, models of common human diseases like schizophrenia require an understanding of the affected pathway(s) dynamics, including gene-environment interactions, exemplified by these studies.
Postdoc Seminars:
Monday, October 15, 4:15-5:15pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Hermann Eberl, Canada Research Chair in Applied Mathematics
in Life Science and Engineering, Department of Mathematics and Statistics, University of Guelph
Title: Growth vs. Disinfection of Bacterial Biofilms in Slow Flowing
Environments
Biofilms are accumulations of bacteria on a surface in aqueous systems.
The bacteria attach to the surface and produce a gel-like polymeric
matrix in which the cells themselves are embedded and in which vivid
microbial communities develop. It is well accepted that biofilm
communities are more difficult to eradicate with biocides than planktonic
communities, which causes big problems in a medical and industrial
context.
Among experimental biofilm researchers there is little doubt that the
hydrodynamic conditions in the environment affect biofilm processes.
In contrast, most biofilm modellers set their studies in a hydrostatic
context, in part due to the increased complexity that comes with the
Navier-Stokes equations.
We study a mathematical model for the formation of spatially
heterogenous biofilm morphologies and their response to biocides.
This model will be coupled with a simplified (compared to the full
Navier-Stokes equations) description of bulk hydrodynamics.
We show some analytical results and numerical simulations.
Thursday, November 1, 10:30-11:30am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Judy Day, Mathematical Biosciences Institute, The Ohio State University
Title: Mathematical approaches to modeling, understanding, and controlling
the acute inflammatory response to pathogen and endotoxin
This talk will consist of three segments which cover some of my past
and present research on modeling, understanding, and controlling the
acute inflammatory response. In the first part, a four dimensional
differential equation model of the acute inflammatory response is
presented in the context of repeated endotoxin administrations.
Lipopolysaccharide (LPS) or endotoxin can induce an acute inflammatory
response comparable to a bacterial infection. In experiments with
repeated endotoxin administration the observation that a
preconditioning dose can blunt the inflammatory response is known as
endotoxin tolerance. Our findings support the hypothesis that
endotoxin tolerance and other related phenomena can be considered as
dynamic manifestations of a unified acute inflammatory response. The
second part will touch on some mathematical results, regarding the
behavior of transients, that were inspired by the endotoxin tolerance
work. In the third and final part we use the previously mentioned
model to investigate a prospective tool known as nonlinear model
predictive control (NMPC), which may help determine suitable dose
regimens in complex clinical settings. The advantage of this approach
over other control algorithms is that it combines both a prediction of
the future state of the system from a mathematical model and feedback
from real time data measurements to successively update a sequence of
control moves that will help to optimize the desired outcome for a
specific scenario.
Thursday, November 8, 10:30-11:30am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Huseyin Coskun, Mathematical Biosciences Institute, The Ohio State University
Title: A Mathematical Framework for Crawling Cell Motion
A 1--D mathematical model for ameboid cell movements using linear
viscoelastic fluid dynamics with free boundary formulation and model based
inverse problem formulation will be discussed. Based on the model, the
inverse problem can be posed: depending on the constitutive relations and
governing equations, what kind of characteristic properties must the model
parameters and unknowns have in order to reproduce a given movement of the
cell, provided that the velocity field at any point is given? The inverse
problem provides the model parameters that give some insight, principally
into the mechanical aspect, but also, through qualitative reasoning, into
chemical and biophysical aspects of the cell. Some numerical analysis and
results of the inverse problem are also discussed.
Thursday, November 29, 10:30-11:30am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Brian Ingalls, Department of Mathematics, University of Waterloo
Title: Control Theoretic approaches in Systems Biology
Systems biology addresses the biochemical and genetic networks
responsible for cellular activity. This activity must be closely
regulated in order for the cell to survive in a variable and
unpredictable environment. The mathematical tools of systems and
control theory were developed to aid in the design and analysis of man-
made self-regulating systems. Those same techniques can be used to
provide insight into the reverse-engineering of biochemical and genetic
self-regulating systems. This talk will present such an analysis: a
treatment of the role of negative feedback in the distribution of
robustness.
Thursday, December 6, 10:30-11:30am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Cyr Emile M'lan, Department of Statistics, University of Connecticut
Title: Estimating Global and Gene-Specificient Parameters in
Unbalanced Multifactorial ANOVA model of Microarray Data
The ability to measure thousands of mRNA transcript expressions
simultaneously using high-throughput genomic technology has revolutionized
the field of Genetics. In our study, RNA from a collection of
Lymphoblastoid Cell Lines were hybridized onto Affymetrix genechip arrays.
The goal of this study is to discover the genes that are highly variable
in the human population subgroups. An ANOVA model was performed and the
effect of the Population subgroups were isolated and tested after
adjusting for confounding effects such as ChipLot, Operator, and Gender.
Here the complexity of our analysis lies in the fact that the covariates
Gender and Population subgroups have gene-specific effects while the
covariates ChipLot and Operator have a global effect (that is, they are
common to all genes). In addition, our microarray data is highly
unbalanced, therefore no results in the microarray literature is of any
help.
In this talk we discuss how multifactorial analysis of variance
containing both global and gene-specific parameters can be carried out
efficiently in spite of the large size of microarray data. We first
derive an analytical form of the solutions of the normal equations and
use these solutions to suggest a low-cost two-stage analysis. Our
procedure can be viewed as an extension of the work by Kerr et al. (2000)
for balanced (orthogonal) designs. We also review permutation tests for
both balanced and unbalanced ANOVA designs. All these results are applied
to our unbalanced microarray data. We also discuss how to get around of the computational complexities in computing ten of thousands of empirical
p-values efficiently based on large permutation size (more than 100,000).
Thursday, January 24, 10:30-11:30am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Henrik Bengtsson, Department of Statistics, University of California, Berkeley
Title: Copy-number estimation on the latest generation of high-density
oligonucleotide microarrays
In this presentation I will introduce the Affymetrix genotype and
copy-number microarray platform and show how it can be used to
estimate whole-genome copy numbers (CNs). In July 2003 Affymetrix
released the so called "10K SNP" chip, which was designed for
genotyping 10,000 single-nucleotide polymorphisms (SNPs) although
early on various groups also proposed methods for estimating CNs.
Since then, in less than four years, Affymetrix has released an
additional four genotyping & CN assays where the density of markers
has increased with an order of magnitude for each generation. With
the release of the GenomeWideSNP_6 ("GWS6") chip in June 2007 we now
have 900,000 SNPs and 900,000 non-polymorphic loci at hand, averaging
one CN marker per 1600 base pairs. This continuous and rapid
development of marker density, together with an increasing number of
samples per project, provides us not only with new opportunities but
also statistical and computational challenges. I will present a
low-level single-locus CN method, together with a bounded-memory
algorithm, that controls for PCR effects, non-balanced enzyme
mixtures, cross-talk between alleles due to sequence homologies, and
offset in obtained probe signals. The method is evaluated by
comparing it with other available methods.
Thursday, January 31, 10:30-11:30am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Erich Grotewold, Department of Plant Cellular and Molecular Biology, Plant Biotechnology Center, The Ohio State University
Title: From gene sequences to transcriptional regulatory networks
There is a growing interest in establishing the transcriptional regulatory networks that govern the expression of all the genes in an organism, a necessary step towards building an "in silico" cell. The wealth of available sequence information has provided a fertile ground for data mining making bioinformatics a valuable tool in aiding the establishment of connections in the complex web of gene interactions. However, there are significant experimental hurdles that slow down the production of experimental data that is essential for confirming predictions or for identifying connections difficult to anticipate by other means. I will describe some of the main experimental approaches involved in establishing gene regulatory motifs and explain how they are utilized in my laboratory to understand two aspects of plant epidermal cell differentiation: the formation of leaf hairs (trichomes) and the formations of pores (stoma) that allow gas exchange.
Thursday, February 14, 10:30-11:30am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Zhijun Wu, Department of Mathematics, Program on Bioinformatics and Computational Biology, Iowa State University
Title: The Solution of the Distance Geometry Problem for Protein Modeling
A well-known problem in protein modeling is the determination of the structure of a protein with a given set of inter-atomic or inter-residue distances obtained from either physical experiments or theoretical estimates. A general form of the problem is known as the distance geometry problem in mathematics, the graph embedding problem in computer science, and the multidimensional scaling problem in statistics. The problem has applications in many other scientific and engineering fields as well such as sensor network localization, image recognition, and protein classification. We describe the formulations and complexities of the problem in its various forms, and introduce a geometric buildup approach to the problem. Central to this approach is the idea that the coordinates of the atoms in a protein can be determined one atom at a time, with the distances from the determined atoms to the undetermined ones. The determination of each atom requires the solution of a small system of distance equations, which can usually be obtained in constant time. Therefore, in ideal cases, the coordinates of n atoms can be determined by a geometric buildup algorithm with O(n) distances in O(n) computing time instead of O(n2) distances in O(n2) computing time as required by a conventional singular-value decomposition algorithm. We present the general algorithm and discuss the methods for controlling the propagation of the numerical errors in the buildup process, for determining rigid vs. unique structures, and for handling problems with inexact distances (distances with errors). We show the results from applying the algorithm to a set of model protein problems with varying degrees of availability and accuracy of the distances and justify the potential use of the algorithm in protein modeling practice.
Thursday, February 21, 10:30-11:30am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: David Chopp, Engineering Science and Applied Mathematics, Northwestern University
Title: Modeling Bacterial Biofilms
Bacterial Biofilms are the most ubiquitous form of life on the planet:
more than 90% of bacteria live in aggregations called biofilms.
Biofilms are primary cause for deaths of people with Cycstic Fibrosis,
cause Legionairre's disease, are a major source of nosocomial
infections, damage ships, and clog fluid based industrial and food
processing machinery causing billions of dollars of damage annually.
Biofilms are also used to improve performance of fertilizers, to
manufacture many household products, and to clean industrial runoff.
Biofilms exhibit complex behavior such as varying surface morphology,
cell-to-cell communication, and symbiotic relationships.
Consequently, it is important for many reasons to understand the
formation, growth, and characteristics of bacterial biofilms so that
they can be inhibited where they are undesirable and controlled where
they are used to our advantage. In this talk I will discuss our work
on modeling and simulation of bacterial biofilms. In particular, I
will discuss two biofilm systems: Pseudomonas aeruginosa biofilms
which are the most common cause of death for people with CF, and
autotroph/heterotroph systems that are used for nitrate and ammonia
removal from waste water in activated sludge reactors.
Models of Cell-Fate Journal Club: Inflammation/Sepsis and Cancer
Thursday, September 20, 2:00pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Judy Day, MBI
Thesis Chapter: PDF
Presentation: PPT
Thursday, October 11, 2:00pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Dustin Potter, MBI
Title: Data integration meets translational research: Where do we want to go and how might we get there?
Presentation: PPT
The broad problem of data integration as I see it is three-fold: How to enable the study of integrated data within the context of a biological question; how to integrate differing data types such as categorical or continuous data; and how to compensate for variations between data collected by different groups and/or technologies as well as biological diversity between different samples or cell types. I will lead a cursory discussion on these three issues and spend the remainder of the time introducing novel methods that have been proposed for addressing the question of data integration.
Monday, November 19, 11:00-12:00am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Jeffrey Parvin, Department of Biomedical Informatics, The Ohio State University
Title: The functions of the breast cancer associated tumor suppressor 1 and breast cancer networks
Paper: PDF
Mutations in the breast cancer associated tumor suppressor-1 protein, BRCA1, are linked to hereditary breast cancer, and low protein expression has been associated with non-familial (sporadic) breast cancer. My laboratory has been working to determine what biochemical reactions are at the heart of how BRCA1 functions to block tumorigenesis. We have used biochemical and cell biology experiments in order to identify biochemical reactions regulated by the enzymatic activity of BRCA1, and lately we have begun to use systems biology approaches in order to identify protein networks in which BRCA1 is key. In this talk, a collaborative study will be described in which a single large gene expression dataset was used to identify genes/proteins in a BRCA-centered-network. The basic principle applied in this study was that genes that function together in a complex process, such as breast tumorigenesis, would have their mRNA expression co-regulated. Thus, to find genes that function together, we analyzed co-expression with specific reference genes in order to find members of the network. Other systems data were applied to these to rank identified genes, and several top-ranked gene/protein were analyzed experimentally. One gene, called HMMR, was found to interact with BRCA1 at the centrosome and to regulate centrosome number in concert with BRCA1. Further, the genotype of HMMR was analyzed in patient cases, and specific haplotypes associated with the HMMR gene had an increased risk of breast cancer. Another gene/protein identified using the co-expression analysis was the Aurora-A kinase, and this gene was shown to regulate the enzymatic activity of BRCA1. This bioinformatics approach proved a powerful means for identifying key BRCA1 interactions that are important for controlling the centrosome and for which there is an important role in the etiology of breast cancer.
Monday, November 26, 11:00-12:00am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Brandilyn Stigler, Mathematical Biosciences Institute, The Ohio State University
Title: TBA
Paper: PDF
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