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Upcoming MBI Seminars:
Tuesday Seminar
Series
Organizer: Tasos Matzavinos and Erich Grotewold
Postdoctoral
Seminars
Organizer: Andrew Oster
Journal Club on Mitochondria and ER Calcium Signalling
Organizer: David Terman
Journal Club on Analysis of Biological Pathways
Organizer: Baltazar Aguda
Past MBI Seminars:
2007-2008
Past MBI Seminars:
2006-2007
Past MBI Seminars:
2005-2006
Past
MBI Seminars: 2004-2005
Past
MBI Seminars: 2003-2004
Past
MBI Seminars: 2002-2003
Recruiting Talks
Thursday, March 13; 12:30-1:30pm
Mathematics Building, Room 240
Speaker: Van Savage, Department of Systems Biology, Harvard Medical School
Title: Linking individuals with ecosystems: New models for understanding physiological and ecological systems
It has long been known that metabolic rate, heart rate, and lifespan scale in a systematic and inter-related way with body size and temperature across species. These scaling relationships hold over an astronomical range in body size (~21 orders of magnitude) and across taxonomically diverse organisms that live in a myriad of environments. Moreover, these relationships for body mass are usually well approximated by power laws with exponents that are simple multiples of 1/4, and for body temperature by exponential Boltzmann-Arrhenius factors. I will describe a model to explain these relationships that focuses on the cardiovascular system and the kinetics of biochemical reactions. I will also discuss recent work of mine that shows how finite-size corrections and asymmetric branching can refine the original model's predictions. I will then present my work that builds on these scaling relationships to examine critical physiological and ecological processes. At the physiological level, I will discuss models to explore, for example, tumor growth dynamics, cell size, and why an elephant sleeps much less than a mouse. At the ecological level, I will outline a trait-based framework to investigate the effects of fluctuating environments on ecosystems and the effects of temperature on predator-prey interactions. Together, these have the potential to gauge the impact of climate change on ecosystem dynamics and stability.
Seminar Series
(recent seminars)
Friday, March 7,10:30-11:30am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Michael Schimek, Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria and Insitute of Biostatistics and Analyses, Masaryk University, Brno, Czech Republic
Title: A median absolute deviation approach for the analysis of noisy CGH array data
CGH array experiments have become a powerful technique for analyzing changes in DNA by comparing a test DNA to a reference DNA. These experiments produce a huge amount of data and special statistical techniques are required for the detection of the alterations. Our intention was to design a method that is able to find gene copy number changes in highly noisy data. The quantile smoothing approach (Eilers and Menezes, 2005) is used for pre-processing of the data. Then, based on the assumption of rank order dependence of the probes and the jump character of gene copy number changes, the breakpoints are detected. Our method is sequential and is based on monitoring changes in variability of the distribution of the log ratios using a moving window of fixed width. The variability of the distribution of log ratios is estimated in each window applying a median absolute deviation concept. The idea behind is that the variability is increased in those windows which cover breakpoints. When the variability of a window exceeds some critical level, the breakpoint is detected. The critical level is derived as quantile of the empirical distribution of variability of the dataset. Finally merging (Willenbrock and Fridlyand, 2005) is used to control the false positives. Performance of our method is demonstrated using simulated and publicly available data sets in comparison with DNAcopy (Olshen et al., 2004).
References:
- Eilers, P. H. C. and de Menezes, R. X. (2005) Quantile smoothing of array CGH data. Bioinformatics, 21, 1146-1153
- Olshen, A. B. et al. (2004) Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics, 5, 557-572
- Willenbrock, H. and Fridlyand, J. (2005) A comparison study: applying segmentation to array CGH data for downstream analyses. Bioinformatics, 21, 4084-4091
Tuesday, March 11, 3:30-4:30pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Lonnie Welch, School of Electrical Engineering and Computer Science, Ohio University
Title: New Frontiers in Computational Genomics
Genomics researchers produce vast quantities of data that require detailed analysis. The amount of information makes it impossible to manually analyze the data. Thus, many bioinformatics software tools have been developed for the purpose of analyzing large-scale data distributed across numerous public data repositories. The discipline of bioinformatics, like the field of genomics, is in its infancy. This talk will illustrate this point by highlighting recent findings of the international ENCyclopedia Of DNA Elements initiative (referred to as the ENCODE project), and by presenting exciting opportunities for computational genomics research.
Tuesday, March 25, 10:30-11:30am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Khalid Boushaba, Department of Mathematics, The Ohio State University
Title: Mathematical modeling of angiogenesis in the zebrafish embryo
Angiogenesis in the zebrafish embryo begins after the first day of development. During this time the intersegmental vessels in the trunk develop from the dorsal aorta in the first wave of embryonic angiogenesis. Previous work suggests a link between VEGF and Syndecan-2, which may function as a co-receptor for VEGF. We are currently developing equations that include terms expressing reaction, diffusion, and cell movement biased by "convection" like terms to model this interaction. These terms model the chemotactic influences on cells, and hence the interaction of the cells with the extracellular matrix that results in their directed movement towards the diffusible growth factor. Using this approach as a framework, we expect to develop mathematical models for angiogenesis for zebrafish that are both predictive and descriptive of growth factor signaling and extracellular matrix interactions during cell migration. Based on the high degree of conservation of signaling pathways involved in angiogenesis, we expect that modeling these processes in zebrafish will be directly applicable to tumor angiogenesis.
Tuesday, March 25, 2:30-3:30pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Ilya Ioshikhes, Department of Biomedical Informatics and Department of Molecular & Cellular Biochemistry, OSU
Title: DNA as a sequence of dinucleotides
DNA is commonly known as a long double-stranded molecule. Each strand is a polymer of simple units called nucleotides: adenine (A), cytosine (C), guanine (G), and thymine (T). The strands are anti-parallel (oppositely directed) and complementary: A in one DNA strand is chemically bound to T in the opposite strand, C bound to G, and vice versa. Nucleotide order defines basic properties of DNA as of a transmitter of hereditary information. From the bioinformatics standpoint, DNA is commonly considered as nucleotide sequence.
However, for many biological processes (particularly those related to gene regulation) DNA properties are defined by order of dinucleotides (successive nucleotides) in the sequence. This is shown on several examples from our research.
- Nucleosomes are basic units of DNA packing in the cell nucleus. Each nucleosome is ~146 bp of DNA wrapped around symmetrical particle of 8 specific protein molecules called histones. Nucleosome positioning along DNA is important in gene regulation and is largely defined by DNA mechanical properties, facilitating sequence-specific affinity of DNA segments to the histone octamer. The DNA mechanical properties (curvature and bending) are defined by its dinucleotide content and order, rather than by those of single nucleotides.
- Most of transcription factors TFs (proteins regulating DNA transcription - synthesis of mRNA on one of the DNA strands) bind DNA in a sequence-specific manner. Existing approaches to computational mapping of the TF binding sites (TFBS) aim at finding motifs with nucleotide sequence most similar to those of experimentally known binding sites. However considering TFBS as dinucleotide sequence results in more efficient mapping.
- Micro-RNAs (miRNA) are short RNA molecules that may bind mRNA in a sequence-specific manner, driven by the rules of sequence complementarity. This results in post-transcriptional gene regulation, primarily mRNA silencing preventing protein synthesis on the mRNA sequence. Existing algorithms of mapping of the miRNA targets combine sequence analysis with calculation of miRNA/mRNA binding energy. The latter is also calculated based on the RNA dinucleotide content and order, not on those of single nucleotides.
Overall, although DNA basic properties are defined by sequence of nucleotides, its biophysical properties essential for gene regulation are largely defined by sequence of dinucleotides.
Tuesday, April 15, 2:30-3:30pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Ralf Bundschuh, Physics Department, OSU
Title: Computational Prediction of Insertional RNA Editing
In organisms with RNA editing the messenger RNA is
modified by base substitutions, deletions, or
insertions with respect to the genomic template. Especially,
in the case of deletions and insertions this makes
it very difficult to identify genes in organisms
with RNA editing. I will discuss several computational
approaches based on the iteration of dircrete transfer
matrices that address this challenge. In Physarum
polycephalum, one of the model organisms for RNA editing,
these methods have lead to the discovery of a significant
number of new genes and even of a new type of editing.
Tuesday, April 22, 2:30-3:30pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Aleix Martinez, Electrical & Computer Engineering, The Ohio State University
Title: Bayes Optimal Pattern Recognition in the Biosciences/engineering
Many problems in biology and engineering can be formulated as a pattern recognition one. In such problems, linear methods are preferred for their simplicity and tractability. Unfortunately, linear methods have many limitations, of which several are still unknown. Understanding these limitations is a key to advancing the current state of the art. In this talk, we will address these issues within the context of feature extraction and classification. We will define where linear feature extraction methods do not work and how this knowledge can be used to propose algorithms that are guaranteed to work in a large number of applications. Possible applications in biology will be discussed. Time permitting, we will sketch the problem posed by classical normalization procedures. In particular, that of norm normalization, generally used to make shape descriptors invariant to scale and rotation, and for modeling mtDNA in genetics. Open problems will be outlined during the course of the talk.
Bio: Aleix M. Martinez is an assistant professor in the Department of Electrical and Computer Engineering at The Ohio State University (OSU), where he is the founder and director of the Computational Biology and Cognitive Science Lab. He is also affiliated with the Department of Biomedical Engineering and to the Center for Cognitive Science. Prior to joining OSU, he was affiliated with the Electrical and Computer Engineering Department at Purdue University and with the Sony Computer Science Lab. He currently serves as an associate editor of IEEE Transactions on Pattern Analysis and Machine Intelligence and Image and Vision Computing.
Tuesday, May 6, 2:30-3:30pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Anand Rangarajan, Department of Computer and Information Science and Engineering, University of Florida
Title: Shape Matching, Atlas Construction and Classification of Hippocampal Datasets
When we seek to compare shapes parameterized as a set of unlabeled points, we face the twin problems of i) shape correspondence and ii) shape deformation. While the problems of determining optimal shape correspondences or shape deformations may not arise in indexing situations, they are important in deformable shape registration - the problem of taking one shape onto another while least deforming the ambient space. Over the past few years, we have shown the
efficacy of i) simultaneously solving for the correspondences and the deformation: TPS-RPM, ii) simultaneously clustering and matching the two shapes: JCM, iii) using the Jensen-Shannon divergence to solve for the deformation without parameterizing the correspondences, and iv) finding a deformation which minimizes a closed-form distance between two Gaussian mixture models for the shapes. Furthermore, we have shown that groupwise registration and atlas construction of point-sets can be performed in an unbiased manner using the aforementioned distance measures. The clinical problem that we are interested in is the retrospective and prospective classification of subjects with either left or right anterior temporal epileptic focii (and scheduled for lobectomy) in the left or right hippocampus respectively. We empirically demonstrate the importance of shape-based features (as against volume-based features) in the automated classification of LATL and RATL subjects.
Tuesday, May 20, 2:30-3:30pm
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Dan Janies, Department of Biomedical Informatics, OSU
Title: Genomic and geographic analysis of the evolution and spread of infectious disease
Emerging infectious diseases present critical issues of public health and economic welfare. As demonstrated by the coordinated international response to SARS, novel diseases are being addressed via rapid genomic sequencing. However, our ability to make sense of these data lags behind acquisition.
First, genomic analyses such as the reconstruction of phylogenetic trees are computationally difficult, requiring novel algorithmic approaches and high performance computers. Next, even when phylogenetic trees are produced, we have hardly begun to understand how disease-causing organisms evolve and travel over various hosts and geography to become epidemics. To these ends we have created an interactive genomic and geographic map using phylogenetic trees and GoogleTM Earth to reconstruct the evolution and spread of avian influenza lineages (H5N1) over the past decade. By examining a phylogenetic tree of H5N1 projected onto the globe we have studied visually and statistically whether and where key genotypes in viral proteins are correlated with host shifts and resistance to therapeutic drugs.
I will provide other examples of how our workflow system, available in prototype at supramap.osu.edu, can be used to inspire and test retrospective and predictive hypotheses of the evolution and geographic spread of microbial pathogens in animal and human populations.
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Postdoctoral Seminars
(recent seminars)
Thursday, March 6, 10:30-11:30am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Jonathan Rubin, Department of Mathematics, University of Pittsburgh
Title: The mathematics of respiration: it's all in your head
In mammals, the respiratory rhythm is maintained under a
wide range of conditions, depending on age, metabolic demand, and
environmental factors. This rhythm is driven by a pacemaker system in
the brainstem. Hence, a central question is, how does this pacemaker
system generate such robust, adaptable rhythms? One component of the
respiratory pacemaker system is the pre-Botzinger complex (pBC), a
collection of neurons that can exhibit bursts of activity under
appropriate conditions and that are coupled with synaptic excitation.
I will discuss the mathematical analysis of the mechanisms by which
synaptic coupling and heterogeneity can promote rhythmic activity in a
model pBC network. This analysis incorporates fast-slow
decomposition, bifurcation analysis, reduction of differential
equations to maps, and a bit of graph theory.
Thursday, March 13, 10:30-11:30am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Cecilia Diniz Behn, Department of Neurology, Beth Israel Deaconess Medical Center
Title: Delayed orexin signaling consolidates wake and sleep: physiology and modeling
Orexin-producing neurons are clearly essential for the regulation of
wakefulness and sleep as loss of these cells produces narcolepsy.
However, little is understood about how these neurons dynamically
interact with other wake- and sleep-regulatory nuclei to control
behavioral states. Using survival analysis of wake bouts in wild type
and orexin knockout mice, we characterized the fragmentation of
wakefulness observed in orexin knockout mice and identified a
surprisingly delayed onset (> 1 min) of functional orexin effects. We
incorporated these findings into a mathematical model of the mouse
sleep/wake network, and the resulting simulated behavior accurately
reflects the fragmented sleep/wake behavior of narcolepsy. Analysis
of the model geometry provides insight into the mechanism associated
with behavioral state instability in the simulated data and leads to
several predictions.
Thursday, March 20, 10:30-11:30am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Anthony Tongen, Department of Mathematics, James Madison University
Title: Biomechanical models applied to the rice blast fungus
The fungus Magnaporthe grisea, commonly referred to as the rice blast fungus, is responsible for destroying from 10% to 30% of the world's rice crop each year.
The fungus attaches to the rice leaf and forms a dome-shaped structure, the appressorium, in which enormous pressures are generated that are used to blast a penetration peg through the rice cell walls and infect the plant. We develop models for both the appressorial development and the penetration peg using exact, nonlinear, elasticity theory for shells and membranes. The model for appressorial design explains the shape of the appressorium, and its ability to maintain that shape under enormous increases in turgor pressure that can occur during the penetration phase. The model for the penetration peg provides the means of studying the effects of external surface stresses and the normal motion of material points on the cell surface.
Thursday, April 10, 10:30-11:30am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Paola Vera-Licona, BioMaPS Institute for Quantitative Biology and Mathematics Department, Rutgers University
Title: Reverse Engineering of Biological Network Models from Noisy Time-Course Data Using Evolutionary Algorithms and Computational Algebra Tools
The biotechnological advances in the last decade have enabled the possibility of a reverse problem formulation for the modeling of systems structure and dynamics of genetic and metabolic networks. Some major challenges for the development of these reverse engineering methods are related to the construction of efficient algorithms to build robust models with respect to data noise and feasible ways to combine gene expression data with a priori knowledge to produce functional predictions of such networks.
In this talk, we will introduce an evolutionary computation based reverse engineering algorithm for constructing the underlying network structure and dynamics from gene expression data and combine it, when available, with a priori knowledge; in our proposed method, gene expression data include wildtype time courses as well as knockout perturbations. Our framework is that of polynomial dynamical systems (PDS) enabling the use of computational algebra tools to efficiently describe structural characteristics of the desired models. Experiments on artificial genetic networks such as the segment polarity gene network in D. Melanogaster, show the performance of the proposed algorithm in constructing a robust (with respect to data noise) mathematical model.
Thursday, April 17, 10:30-11:30am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Brynja Kohler, Department of Mathematics and Statistics, Utah State University
Title: Modeling Memory T-Cell Differentiation
The adaptive immune system has the convenient feature of being able to remember and defend the body against previously encountered pathogens, rendering long-term immunity to an individual who survives an initial acute infection. T-cell populations accomplish this task through their expansion and differentiation into subtypes of cells with effector (useful for eliminating pathogen) and memory (surviving) capabilities. Simple mathematical models using systems of ordinary differential equations can capture the dynamics of typical immune responses, and these models are useful for predicting proliferation and death rates of various subcategories of T cells. We discuss some findings based on parameter fitting in these basic models which assume a variety of differentiation pathways. We also present and discuss a T-cell population model that assumes that differentiation to memory cells is a continuous process dependent on the strength and duration of antigen exposure. This new model consists of a coupled pair of partial differential equations and results in a translating solution of the heat equation. Interestingly, this same mathematical model has been used to describe and analyze transport along nerve axons.
Thursday, April 24, 10:30-11:30am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Diogo Camacho, ENG Ctr For Adv Biotechnology, Boston University
Title: Gene network inference from time series expression data
The reverse engineering of biological network is a major focus of research in the post-omics era. Gene networks are conceptual representations of interactions between genes and may provide important information about the regulatory aspects of the biological system under study. Applications in biomedical engineering include the design of specific drug targets that could maximize the effect of its action across the network. A multitude of methods are available to infer gene networks from data, some of which have specific data requirements in order to satisfy their theoretical framework. I propose to present a new method to reverse engineer gene networks from time series data based on the estimation of gene interactions by least squares fitting. By iteratively selecting genes to be perturbed (i.e., to be knocked out), constraints can be imposed in the network, thereby helping in the inference process.
Thursday, May 1, 10:30-11:30am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Thomas Hillen, Department of Mathematical and Statistical Sciences, University of Alberta
Title: Mathematical Modelling of Cell Movement in Fibre Tissues
In current studies on cell movement in tissues, Friedl et al. have observed single metastatic cancer cells as they move through collagen network tissue. They show a characteristic form of movement, called "mesenchymal motion". Based on their observations, I will derive mathematical models for mesenchymal motion. On a mesoscopic level, I will formulate transport equations. To obtain macroscopic models in the form of advection-diffusion equations, I will use hyperboplic and parabolic scaling techniques. Numerical simulations of these models show interesting pattern formation in form of networks. I will discuss specific applications and present new results on steady states.
Thursday, May 22, 10:30-11:30am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Robert D. Guy, Department of Mathematics, University of Utah
Title: TBA
Thursday, May 29, 10:30-11:30am
MBI Lecture Hall - Jennings Hall, Room 355
Speaker: Judy Wang, Department of Statistics, North Carolina State University
Title: Analysis of Probe-level Microarray Data-a Quantile Approach
In this talk, I will introduce a quantile approach for analyzing GeneChip microarray data to detect differentially expressed genes through analyzing probe level measurements. The developed test makes no distributional assumptions, and it does not require estimating the unknown error density function. Our empirical studies with real experimental data show that detecting differences in the quartiles for the probe
level data is a valuable complement to the usual mixed model analysis based on Gaussian likelihood.
Aiming to improve the efficiency of the quantile rank score test at small samples, we propose an enhanced method to calibrate the intra-subject correlation estimation by sharing information across the "interesting" genes. The enhanced method shrinks the gene-specific correlation estimates towards a common value, with the degree of shrinkage depending on the variability of correlation coefficients across genes.
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Journal Club on Analysis of Biological Pathways
View Schedule, Location and other Details Here
Journal Club on Mitochondria and ER Calcium Signalling
Thursday, January 17, 3:30pm
MBI Conference Room - Jennings Hall, Room 360
Discussion on paper: PDF
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