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Postdoctoral Research Forum
Postdoctoral Research Forum Abstracts
Presentation Materials: PPT
Presentation Materials: PDF
Presentation Materials: PPT
My research involves using mathematical methods such as ordinary and partial differential equations to model biological phenomena, including the development and analysis of mean field models arising from spatially explicit or agent-based models. I will describe examples of biological problems for which these approaches are useful.
Presentation Materials: PPT Our laboratories are using several techniques including immunohistochemistry, physiology, electron microscopy,tissue culture and HPLC to better understand the role of the cerebellum in controling and coordinating both motor activity and higher cognitive functions. Currently, we are carrying out 2 research projects. In one, we are defining the role of a peptide called corticotropin releasing factor (CRF) in adult and developing animals. In the adult CRF acts to modulate neuronal activity, whereas in the embryonic and early postnatal brain it appears to play a developmental role in establishing circuits and insuring survival of neurons. We will continue our analysis of CRF and its interactions with its 2 known receptors defined as the type 1 and type 2 CRF receptor. In a second series of studies, we are determining if damage to the cerebellum is associated with the loss of cognitive functions, in particular those associated with autism. Our initial studies indicate that use of a drug that is associated with a high incidence of autism in the human population induces changes in the morphological and physiological characteristics of a specific population of neurons in the cerebellum.
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We describe three different types of challenges for dynamical system modeling in biology. In the first case we look at large networks of enzymatic reactions in the cell. A mathematical model is easy to write, but there is not enough experimental data to determine the parameters in the model. We show that we are still able to derive qualitative information about the system. In the second case we look at fast and slow axonal transport. Experimental data is available, but there is no confirmed model. We design a mathematical model, and attempt to validate it based on the experimental data. In the third case we are interested in the distribution of ionic channels along dendrites. Both experimental data and a model are available, but we run into computational difficulties when we try to use the model to compute the distribution of channels. We design a new computational method, applicable to that specific model and input data.
In this short talk, a methodology known as hierarchical statistical modeling is presented. I show how it could be applied in a spatial setting that links ambient air pollution to human health outcomes.
Presentation Materials: PPT My research interests are in the field of Bioinformatics & Computational Biology. My group is currently working on (i) Development of computational tools to annotate transcriptional regulatory regions in mammalian genomes (ii) Development of pattern recognition methods and statistical models to identity transcription factor binding sites, model transcriptional modules and networks in hematopoiesis cell lineages (iii) Development of robust databases and visualization tools for genomic data and annotations. We provide the annotations of promoter regions and first exons in the human genome to the UCSC genome browser, in collaboration with Zhang lab in Cold Spring Harbor Laboratory. Three major projects we are currently working on are:
More information about all the above resources is available at my lab web-site: http://bioinformatics.med.ohio-state.edu.
I am interested in developing statistical and mathematical approaches useful in predicting the behavior of cellular systems. In my work in predictive microbiology I have applied techniques such as dynamical systems modeling, robust non-parametric regression and chemical systems modeling. Current work focuses on developing new approaches for studying complex diseases such as coronary artery disease and cancer.
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Presentation Materials: PPT Gene expressions may differ in different cell types (e.g., normal and diseased tissues) even for genes not involved in the disease process such as housekeeping genes. We recommend (negative) control genes be included in microarray experiments, and their observed differential expressions be used to normalize the observed differentials of the target genes. This project of selecting suitable housekeeping genes as control genes will make use of my experience developing statistical methods that are in use in clinical "equivalence" trials.
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Presentation Materials: PPT Alzheimer's disease (AD) is the major dementing illness of the elderly, with prevalence expected to reach over 10 million cases in the U.S. in the coming decades. The disease is characterized pathologically by the appearance of hallmark lesions in select regions of the brain. Lesion appearance correlates temporally with neurodegeneration, and is the major surrogate marker for disease diagnosis. Because of these considerations, we focus on intracellular lesion formation as a target for diagnostic and therapeutic discovery. Above all, we seek to clarify the protein misfolding events that accompany lesion formation. We have begun by developing methods for modeling protein misfolding biochemically using purified components. We have also selected small, drug like inhibitors of the model reaction, which may have therapeutic potential. We seek to clarify the mechanism of these reactions in detail, and to determine the feasibility of our approach for treatment of AD. Three aspects of our work would benefit from mathematical modeling. First, the model reaction, which is a logistic process, must be cast in the form of elementary rate constants. This will allow us to test hypotheses concerning events that initiate or accelerate the reaction. Second, the interaction of drug-like inhibitors with the system must be modeled so that hypotheses concerning mechanism can be tested. This is essential for assessing the feasibility of our therapeutic strategy. Finally, we seek to model lesion formation at the whole brain level, so that utility of various inhibitory mechanisms for treatment of authentic disease can be predicted.
This is a "chalk" talk in which I will briefly describe Folkman's idea for tumor growth and show how this idea may be modeled, at least in part. The model derives from the biochemical observation that growth factors expressed by an avascular tumor in response to hypoxia can stimulate endothelia in neighboring capillaries to grow branches that vascularize the tumor and relieve the hypoxia, thus stimulating rapid vascular tumor growth as well as encouraging such unpleasant side effects as metastasis.
Presentation Materials: PDF
Presentation Materials: PPT My research interests are in statistical genetics, genetic epidemiology, and bioinformatics. I focus on the development and applications of statistical and computational methods for linkage analysis, association mapping, and analysis of microarray gene expression data. The sort of data that render conventional methods infeasible, such as data from large families with complex relationships, is of a long-standing interest of mine. Other important issues in mapping, such as crossover interference, genetic heterogeneity, multiple testing, and haplotype analysis, are also of particular interest. More recently, I have also delved into issues in functional genomics and bioinformatics, in particular in cancer type and subtype classification based on gene expression data. I also enjoy working on applied projects, including collaborative research with medical doctors on genetic epidemiological studies.
My lab has a long-standing interest in understanding how signaling pathways elicit selective changes in gene transcription in mammalian cells. We use a combination of genetic mouse models, molecular genetics, biochemistry and cell biology to attack these problems. Most recently, we have become interested in understanding interactions between signaling pathways locating in the different cell types involved in complex biological processes of cancer cell progression and normal cellular differentiation. For example, a breast tumor is composed not only of the epithelial-cell derived tumor cell, but also stromal cells, endothelial cells, and immune cells including macrophages, B-cells and T-cells. It is the interaction of these cell types through complex signaling networks that are likely to be important for tumor cell progression and metastasis, and not just the action of individual signaling pathways within the epithelial tumor cell. Understanding and targeting such intercellular networks of communication holds great promise for new advances in the diagnosis and treatment of cancer. Recent advances in genomics and functional genomics makes it possible to begin studying such complex networks of interaction that control the overall behavior of different cell types. It is clear that computational and statistical tools will be necessary to model these complex interactions.
Presentation Materials: PPT My research involves the use of computational techniques, such as the immersed boundary method, to gain insight into the mechanism of growth and development of various biological tissues. Several computer simulations will be presented, including development of abnormal invaginations in the placental trophoblast bilayer; formation of cardinal lobes in the rat cerebellum; growth of cancer cells in the breast ductal system.
Presentation Materials: PPT
Most of my past and current research projects are concerned with understanding the formation of patterns and the dynamics of nonlinear waves in spatially extended systems modelled typically by partial differential equations on unbounded domains. Nonlinear waves correspond to interfaces, or defects, that are formed between co-existing patterns. Examples are the transmission of signals in optical fibers, the formation of hexagonal and stripe patterns in fluid convection, and the generation of spiral waves in catalytic chemical reactions. Motivated by experiments and numerical simulations, I aim to understand when and how patterns and defects are formed, how they behave under small perturbations, what other patterns or waves with a more complicated spatio-temporal behaviour can bifurcate from them, and how they interact with each other or with domain boundaries. To answer these questions, I use a mixture of analytical and geometric dynamical-systems techniques, and I have also developed numerical algorithms for the computation of waves and their bifurcations.
Presentation Materials: PPT
Presentation Materials: PPT Advances in protein separation techniques, mass spectroscopy, and bioinformatics has enabled the examination of the proteome, the protein complement of the genome. We utilize proteomics approaches including MALDI-TOF mass spectrometry to examine changes in protein expression during differentiation of mammalian neuronal cells in culture, to define protein-protein interactions following inhibition of nucleologenesis, and to identify phosphoproteins involved in cell cycle regulation. New SELDI-TOF mass spectrometry equipment available to the lab will be applied to these studies as well as to clinical based studies designed to discover and validate disease biomarkers from patient samples. Enormous amounts of data can be generated from these studies, and appropriate methods of data analysis and interpretation are required. This provides an opportunity for coordinated efforts between life scientists and computer scientists to design innovative methods for the acquisition and analysis of proteomic information.
Chemical databases often encode the structure of molecules in long binary string s called fingerprints. A general goal is to use these fingerprints to predict s ome specific biological activity of a molecule, such as its ability to kill cert ain cancer cells. It is well known that different classes of chemicals interact with cells via different mechanisms, and that small structural differences with in a class can produces large changes in biological activity. Under these circu mstances, simple implementations of support vector machines do not perform well. However, recent work (Wilton, et al. 2002) suggests that some specialized kernel smoothers may work well in distinguishing biologically active molecules. Our approach is first to form localized regions using the Jacard-Tanimoto metric, which is sensitive to the relative number of mismatched features, and then use "weighted triples" kernels within local neighb orhood. These utilize low order interactions of binary features in creating the underlying kernel function. The technique is illustrated by identifying key feature-combinations of a subclass of colchicines with high activity against H23 lung cancer cells.
I will outline research done in my lab on computational modeling of auditory scene analysis - the perceptual process of separating a target sound source from a sound mixture that may contain many acoustic intrusions. In particular, speech segregation will be highlighted, and neural machanisms for computational auditory scene analysis will be discussed.
Presentation Materials: PDF
Presentation Materials: PPT In my presentation, I will summarize my current work in progress with respect to my recordings in the antennal lobe of insects. One problem that I face using the type of electrophysiological recording technique that I am using in Brian Smith's lab is being able to identify which spiking events belong to which neurons. I have tried a few different spike sorting techniques, and have encountered difficulties arising from the ability to identify spiking events before they are classified. I will present what I am planning to do to solve my current problems with spike sorting.
Our research involves the use of computational approaches, based on both bioinformatics and molecular biology, to study the structure and function of human genome and proteome. We combine biology and computational science with the goal of understanding the sequence, structural, and molecular basis of a wide range of biological phenomena. This work includes fundamental theoretical research, the design of effective algorithms, development of software tools, and applications to problems of biological significance. We are currently interested in the use of comparative genomics to identify disease-related genes, protein structure prediction, the study of protein-protein and protein-ligand interactions; the development of computational methods to identify protein function based on protein structure; and the use of these methods to characterize families of proteins and their specific biological functions. Protein Structure Prediction Much of our recent effort has been devoted to the development of new tools for homology modeling as well as fold recognition. Reliable homology models are essential for many of our studies on the structural origins of specificity in protein-protein interactions. Good homology models require good sequence alignments, and to this end we have developed a new position-specific scoring matrix that take three-dimensional context into account. This has allowed accurate fold recognition for distantly related proteins. Here we applied principle component analysis, and biophysical estimations for various molecular interactions. In addition, we developed metric-based indexing scheme to classify all known substructural elements necessary to reduce the redundancies in the selection of known structural templates. We also applied graph theory to both represent and model 3D protein structures again with the aim to detect similar substructures. Our goal in the coming year is the design of a fully automated structure prediction system for the entire genome. Protein-Protein Interactions The analysis of protein-protein interactions involves the recognition of features on protein surfaces that are involved in binding to other proteins. We are particularly interested in delineating features that dictate specificity versus affinity in binding interactions and in deriving general rules that may aid in the identification of interacting surfaces when there is no available structure of a complex. In this context, we used Hough transformation to index both geometric and biological critical points associated with the formation of protein complexes. We have assessed the contribution of individual amino acids to binding based on both structural and sequence profiling analysis. Specifically, residues important in protein-protein interactions are conserved and exhibit similar evolutionary constraints, which are weighted into the transformation. When this method is combined with our structure-based alignment tools, we are able to cluster protein families into functional subgroups and to detect novel sequence/structure/function relationships. We hope that our combined bioinformatics/molecular approach will yield new general insights, new methods, and new approaches toward the understanding of the structural basis of biological specificity. Genome Analysis The recent release of the complete human genome provides an unprecedented opportunity to integrate human genes and their functions in a complete positional context. However, at least three significant technical hurdles remain: first, to assemble a complete and nonredundant human transcript index; second, to accurately place the individual transcript indices on the human genome; and third, to functionally annotate all human genes. We assembled existing transcripts into gene-oriented transcript contigs. Each resulting assembly is aligned to the human genome as evidence of experimentally supported gene. This provides a physical map with annotations for a majority of the human transcripts. Such information can be immediately applied to the discovery of new genes and the identification of candidate genes for positional cloning.
Prsentation Materials: PPT Molecular cloning and genome sequencing have revealed the existence of a novel family of cation channels formed by homologues of transient receptor potential (TRP) protein initially identified from eyes of fruit flies. The TRP superfamily currently consists of several subfamilies including TRPC, TRPV and TRPM, each of which contains multiple family members. These channels are involved in many important physiological functions ranging from taste transduction, vision, muscle contraction, synaptic transmission fertilization to temperature, pressure, and pain sensations. Our work has focused on the structure and function of TRPC proteins. Using heterologous expression, intracellular Ca2+ imaging, patch-clamp recording, and protein binding studies, we have demonstrated that TRPC proteins form agonist-stimulated non-selective Ca2+ cation channels. They are activated by binding to inositol-trisphosphate receptors and inactivated by binding to calmodulin. More recently, we have identified multiple calmodulin binding sites from TRPC, as well as TRPV and TRPM proteins. Related Ca2+ binding proteins such as CaBP1 also bind to TRPs and prevent their activation. Thus multiple Ca2+ sensing proteins are associated with TRP channels at various sites, fine-tuning the activities of these channels.
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