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Ziv Bar-Joseph, Ph.D.
Assist Professor, Carnegie
Mellon University, Dept. of Computer Science |
MS, Hebrew University,
PhD, Massachusetts Institute of Technology |
Our group develops computational methods for understanding the dynamics, interactions and conservation of complex biological systems. As new high-throughput biological data sources become available, they hold the promise of revolutionizing molecular biology by providing a large-scale view of cellular activity. However, each type of data is noisy, contains many missing values and only measures a single aspect of cellular activity. Our computational focus is on methods for large scale data integration. We primarily rely on machine learning and statistical methods. Most of our work is carried out in close collaboration with experimentalists. Many computational tools we develop are available and widely used.
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I. Simon, Z. Siegfried, J. Ernst and Z. Bar-Joseph
(2005) Combined Static and Dynamic Analysis for Determining the Quality of Time-Series Expression Profiles
Nature Biotechnology 23, 1503-1508.
J. Ernst, Z. Bar-Joseph (2006)
STEM: a tool for the analysis of short time series gene expression data.
BMC Bioinformatics 7:191 |
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Jaime Carbonell, Ph.D.
Professor, Carnegie Mellon University, Dept. of Computer Science |
PhD, MPhil, MS, Computer Science, Yale University |
Artificial intelligence, natural-language processing, machine learning, machine translation |
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Liu Y, Xing E., Carbonell J
(2005) ‘‘Predicting Protein Folds with Structural Repeats Using a Chain Graph Model,’’ Proceedings of the Iinternational
Conference on Machine Learning (ICML05).
Liu Y, Carbonell J, Weigele P, Gopalakrishnan. V.
(2005) ‘‘Segmentation Conditional Random Fields (SCRFs): A New Approach for Protein Fold Recognition ,’’
Proceedings of the ACM International conference on Research in
Computational Molecular Biology (RECOMB05).
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Lillian T. Chong, Ph.D.
Assistant Professor, University
of Pittsburgh, Dept. of Chemistry |
Ph.D., University of California at San Francisco |
Protein structure and function, natively unfolded proteins, molecular sensors, molecular dynamics simulations, distributed computing.
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“Kinetic computational alanine scanning: application to p53 oligomerization,” L.T. Chong, W.C. Swope, J.W. Pitera, and V.S. Pande, J. Mol. Biol., 2006, 357, 1039-1049.
“Dimerization of the p53 oligomerization domain: identification of a folding nucleus by molecular dynamics simulations,” L.T. Chong, C.D. Snow, Y.M. Rhee, and V.S. Pande, J. Mol. Biol., 2005, 345, 869-878. |
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Rob Coalson, PhD.
Professor, University of Pittsburgh, Departments of Chemistry and Physics & Astronomy |
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Gregory F. Cooper, M.D., Ph.D.
Associate Professor of Medicine and of Intelligent Systems |
PhD, Medical Information Science, MD, Medicine, Stanford University |
His research interest is in the application of decision theory, probability theory, and artificial intelligence to address biomedical informatics research questions, with a focus on causal modeling and discovery in medicine and biology, data mining of medical databases, application of Bayesian statistics in medicine, and biosurveillance. |
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Yoo C, Cooper GF, Schmidt M. A controlled study to evaluate a computer-based microarray experiment-design-recommendation system for gene-regulation pathway discovery. Journal of Biomedical Informatics 39 (2006) 126-146.
Mani S, Cooper GF, Spirtes P. A theoretical study of Y structures for causal discovery. In: Proceedings of the Conference on Uncertainty in Artificial Intelligence (2006) 314-323.
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Billy W. Day, Ph.D.
Professor, University of Pittsburgh, Dept. of Pharmaceutical Sciences |
Ph.D., Medicinal Chemistry, University of Oklahoma |
Drug design, synthesis, biochemical and cell biological evaluation, proteomics
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Raccor BS, Vogt A, Sikorski RP, Madiraju C, Balachandran R, Montgomery K, Shin Y, Fukui Y, Jung W.-H, Curran DP, Day BW "Cell-based and biochemical structure-activity analyses of analogues of the microtubule stabilizer dictyostatin," Mol. Pharmacol. 2008, 73, 718-726.
Balachandran R, Hopkins TD, Thomas CA, Wipf P, Day BW "Tubulin-perturbing naphthoquinone spiroketals," Chem. Biol. Drug Des. 2008, 71, 117-124.
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David J. Earl, Ph.D.
Assistant Professor, University of Pittsburgh, Department of Chemistry |
PhD in Chemistry, University of Durham; BSc in Chemistry & Physics, University of Durham |
Modeling biological evolution and immune system dynamics using computer simulation and statistical mechanics. We are interested in how the mechanisms that facilitate evolution have evolved, how modularity, canalization, and robustness can evolve in biological systems, and how these properties influence the evolution and evolvability of populations.
We are also interested in understanding the physics behind low Reynolds number swimming (the regime at which bacteria and cells move), including how to design swimming motions that are efficent, and the physics of cooperative motion in this regime.
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D. J. Earl and M. W. Deem, "Evolvability is a selectable trait," Proc. Natl. Acad. Sci. USA, 101 (2004) 11531-11536.
D. J. Earl, C. M. Pooley, J. F. Ryder, I. Bredberg and J. M. Yeomans, "Modeling microscopic swimmers at low Reynolds number," J. Chem. Phys., 126 (2007) 064703. |
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G. Bard Ermentrout, Ph.D.
Professor, University of Pittsburgh, Dept. of Mathematics |
Ph.D., Biophysics, University of
Chicago
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Dr. Ermentrout's research program investigates models of neural and muscle physiology. His recent focus has been on the behavior of networks of cortical-like neurons. He is interested in the dynamics of wave propagation in cortical and thalamic slice models, the olfactory lobe of the Limax, and the synchronization of cortical networks,
in addition to spatial and temporal patterns in neuronal networks such as those observed during flicker stimulation and localized patterns of working memory. He also studies the effects of various ionic current and synaptic plasticity on the interactions between neural oscillators. He is the author of XPPAUT, a software platform for the simulation and analysis of nonlinear dynamical systems. |
Moldakarimov SB, McClelland JL, Ermentrout GB.
(2006) A homeostatic rule for inhibitory synapses promotes temporal sharpening and cortical reorganization.
Proc Natl Acad Sci U S A 31:16526-31.
Ermentrout B. (2006) Gap junctions destroy persistent states in excitatory networks.
Phys Rev E Stat Nonlin Soft Matter Phys. 74(3 Pt 1):031918. |
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Elodie Ghedin, Ph.D.,
Asst. Prof., U. of Pittsburgh School of Medicine, Dept of Medicine - Division of Infectious Diseases |
Ph.D., Molecular Parasitology, McGill University, Canada
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Research in our group is multidisciplinary and draws upon the tools of genomics, molecular virology, and computational biology.
Viral projects include a) determining the extent, structure and underlying mechanisms of genetic variation in influenza A viruses sampled across a population and within individual hosts; b) metagenomics approaches for the characterization from clinical samples of viruses believed to cause chronic diseases. Data collected in these projects is essential to understand the process of viral emergence. Parasite projects are presently focused on the filarial nematode Brugia malayi. Studies include the re-assembly of the whole genome and mapping of the interactome between the filaria worm and its endosymbiontic bacteria Wolbachia.
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Ghedin, E., Wang, S., Spiro, D.J., ... Scott, A.L. (2007) Draft Genome Sequence of the Filarial Nematode Parasite Brugia malayi Science 317: 1756-1760.
Ghedin, E., Sengamalay, N.A., Shumway, M., Zaborsky, ...Salzberg, S.L. (2005) Large-scale sequencing of human influenza reveals the dynamic nature of viral genome evolution. Nature Oct 20;437:1162-1166 |
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Vanathi Gopalakrishnan, PhD.
Asst Prof., U. of Pittsburgh, Dept of Biomedical Informatics and Intelligent Systems Program |
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Michael Grabe, Ph.D.,
Assistant Professor of Biological Sciences and Computational Biology |
PhD in Physics from the University of California, Berkeley |
Our lab uses computational methods to understand biological phenomena. We are primarily interested in ion transport across cellular membranes. We wish to understand the molecular workings of ion channels and transporters as well as how these proteins work together to regulate ion homeostasis in organelles such as the lysosome and Golgi. We are also interested in the role that membrane proteins have in controlling the intrinsic shape of organelles. |
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Structure prediction for the down state of a potassium channel voltage sensor. Grabe, M.*, H.C. Lai*, M. Jain, Y.N. Jan, and L.Y. Jan. Nature 445: 550
Electrostatic forces in the cavity regulate K+ channel selectivity via a kinetic switch.Grabe, M., D. Bichet, X. Qian, Y.N. Jan, and L.Y. Jan Proc. Natl. Acad. Sci. USA. 103: 14361
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Jonathan Rubin Ph.D.,
Associate Professor University of Pittsburgh Department of Mathematics |
Ph.D. in Applied Mathematics from Brown University |
Our lab uses computational methods to understand biological phenomena. We are primarily interested in ion transport across cellular membranes. We wish to understand the molecular workings of ion channels and transporters as well as how these proteins work together to regulate ion homeostasis in organelles such as the lysosome and Golgi. We are also interested in the role that membrane proteins have in controlling the intrinsic shape of organelles. |
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"Towards a model based medicine: a clinically meaningful approach to ill-posed inverse problems in quantitative physiology", S. Zenker, J. Rubin, and G. Clermont, PLoS Comp. Biol., 3: 2072-2086, 2007.
"Giant squid - hidden canard: the 3D geometry of the Hodgkin-Huxley model", J. Rubin and M. Wechselberger, Biological Cybernetics, 97: 5-32, 2007.
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Naftali Kaminski, M.D.
Assoc Professor of
Medicine, Pathology and Human Genetics; Director, Dorothy P. &
Richard P. Simmons Center for Interstitial Lung Disease |
Medical School of Hadassah and the Hebrew University in Jerusalem |
The main research interests of my team are the basic mechanisms underlying chronic progressive lung disease such as pulmonary fibrosis and the way that molecular networks determine tissue and cellular phenotype. To study these mechanisms we apply a systems biology approach that incorporates a combination of traditional molecular biology methods, high-throughput genomic technologies such as expression and location microarrays, advanced computational approaches and targeted proteomic approaches. As a result of these studies we have identified key regulatory molecules as well as potential biomarkers for disease diagnosis and progression.
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Studer S, Kaminski N. Towards Systems Biology of Human Pulmonary Fibrosis
(2007) Proc Am Thorac Soc 4:85-91;
Segal E, Friedman N, Kaminski N, Regev A, Koller D
(2005) From Signatures to Models: Understanding Cancer using Microarrays.
Nat Genet 37 S38-45 |
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Judith Klein-Seetharaman, Ph.D.
Assistant Professor, U of
Pittsburgh, Dept. of Struct Biology |
Ph.D., Chemistry, Massachusetts Institute of Technology, Cambridge, MA |
Membrane receptors are the entry point to communication between the cell and its environment, and we are studying them using a combination of computational and experimental approaches. Our particular interests lie in understanding the mechanisms of membrane protein folding and misfolding, the discovery of novel interactions and functions of membrane receptors and in-depth quantification of their equilibrium dynamics and functional conformational changes.
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Rader, A.J., Anderson, G., Isin, B., Khorana H.G., Bahar, I. and Klein-Seetharaman, J. (2004) Identification of core amino acids stabilizing rhodopsin. Proc. Natl. Acad. Sci., 101(19), 7246–7251 Qi, Y., Bar-Joseph, Z. and Klein-Seetharaman, J. (2006) Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Proteins - Structure, Function and Bioinformatics, 63, 490-500. |
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Maria Kurnikova, PhD.
Assistant Professor, Carnegie Mellon University, Department of Chemistry |
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Christopher James Langmead B.M., M.A., Ph.D.,
Carnegie Mellon U, Assist Prof of Computer Science. |
Bachelor of Music, Oberlin Conservatory of Music, 1993 Masters of Computer Music, Dartmouth College, 1995 Ph.D. in Computer Science, Dartmouth College, 2003
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I am interested in the dynamics of complex biological processes. My research uses a combination of Machine Learning and Formal Methods to model and study the dynamics of a variety of phenomena including: molecular interactions, acute illness, and cancer.
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H. Kamisetty, E.P. Xing, C.J.
Langmead; "Free Energy Estimates of All-atom Protein Structures Using Generalized Belief Propagation", Proceedings of The Eleventh Annual International Conference on Computational Molecular Biology (RECOMB) , pp 366-380
C.J. Langmead, S.K. Jha, "Symbolic Approaches to Finding Control Strategies in Boolean Networks", Proceedings of The Sixth Asia-Pacific Bioinformatics Conference, (APBC) 2008. pp. 307-319 |
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Jeffry Madura, Ph.D.
Professor, Duquesne U, Dept. of Chemistry and Biochemistry |
PhD, Physical Chemistry, Purdue University
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Research in our laboratory consists of the development and application of computational methods to diverse systems of interest. The research projects are interdisciplinary in nature, including subjects from organic chemistry, physical chemistry, analytical chemistry, inorganic chemistry and biochemistry. The group utilizes various computational tools such as Gaussian 98/03, GAMESS, CHARMM, AMBER, MOE, CAChe, UHBD, NAMD, and DL_POLY. Students in the group interact with faculty members within the Department as well as faculty from Pitt and PSC. Students also work with and visit scientists at IBM Almaden and the Pittsburgh National Energy Technology Laboratory. |
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Dalal, P.; Zanotti, K. Wierzbicki, A; Madura, J. D. Lipid bilyar simulations, Biophysics J.(2005) (in press). Zhou, Zhigang; Madura, Jeffry D Relative free energy of binding and binding mode calculations of HIV-1 RT inhibitors based on dock-MM-PB/GS Proteins (2004), 57(3), 493-503 |
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Zoltan Nagy Oltvai, M.D.,
Associate Professor of Pathology and Computational Biology, University of Pittsburgh, School of Medicine |
M.D., Semmelweiss Medical School, Budapest, Hungary
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Biomedical research today is conducted largely in the context of an
established paradigm that is fundamentally reductionist in nature. Yet,
despite the enormous success of this approach, it is increasingly clear that
discrete biological function and the astounding complexity of living systems
cannot be understood by studying individual molecules. Instead, most
biological characteristics arise from complex interactions among the cell's
numerous molecular constituents. Thus, a key challenge for 21st century
biology is to understand the structure and the dynamics of the complex
intracellular web of interactions among the various types of molecules that
contribute to the function and the physical entity of a living cell.
Together with our collaborators, our laboratory focuses on the understanding
of the system-level organization of cellular metabolism, and how
environmental cues are processed through regulatory pathways leading to
rearranged metabolic activities. In the long run, we are also interested in
applying this knowledge to improved disease diagnosis and treatment.
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A.-L. Barabási and Z. N. Oltvai, Network Biology: Understanding the Cell's Functional Organization, Nature Reviews Genetics 5, 101-113 (2004)
Q. K. Beg, A. Vazquez, J. Ernst, M. A. de Menezes, Z. Bar-Joseph, A.-L. Barabási, and Z. N. Oltvai, Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proceedings of the Nat'l Academy of Sciences 104: 12663-12668 (2007) |
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John Rosenberg, PhD
Professor, University of Pittsburgh, Department of Biological Sciences |
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Roni Rosenfeld, Ph.D.
Professor, Carnegie Mellon U, School of Computer Science |
Ph.D., Computer Science, and M.Sc., Computer Science, Carnegie Mellon University
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My research interests are in computational molecular virology and vaccine design. Retroviruses like HIV and RNA viruses evolve at a much higher rate than DNA life forms. This is a formidable challenge to vaccine design, but is also an opportunity to observe evolution as it happens. We use the fast growing databases of viral sequences to build descriptive and generative models of viral molecular evolution
and to suggest potential antigenic targets that cannot easily mutate away. In collaboration with virologists
and immunologists, we try to correlate isolate sequence composition to important biological properties of the isolate, such as pathogenicity, infectivity and neutralizability. Along the way we design and develop visualization tools for multiple sequence alignments (MSAs) and other biological sequence data.
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Rose Hoberman, Judith Klein-Seetharaman, and Roni Rosenfeld. Inferring Property Selection Pressure from Positional Residue Conservation.
Appl. Bioinformatics, 2004; 3: 167-179.
Madhavi Ganapathiraju, Judith Klein-Seetharaman, Roni Rosenfeld, Jaime Carbonell and Raj Reddy. Rare and frequent amino acid n-grams in whole-genome protein sequences.
Proc. RECOMB'02.
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Joel Stiles, M.D., Ph.D. Director, Center for Quantitative Biological Simulations
Associate Professor, Mellon College of Science, Carnegie Mellon
U |
Ph.D., Physiology; M.D., University of Kansas School of Medicine
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Dr. Stiles is a computational physiologist with research interests in synaptic and cellular microphysiology. His work has helped create and distribute research and teaching software for spatially realistic simulations of cellular function, and has illustrated counter-intuitive structure-function relationships at the nerve-muscle synapse and in specific instances of neuromuscular disease. He is a principal co-author of MCell, a Monte Carlo simulator of cellular microphysiology, and is also the principal architect of DReAMM (Design, Render, and Animate MCell Models). |
Stiles, JR, et al. (1996). Miniature endplate current rise times <100 μs from improved dual recordings can be modeled with passive acetylcholine diffusion from a synaptic vesicle. Proc. Natl. Acad. Sci. USA 93:5747-5752.
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David Swigon, Ph.D.Assistant Professor, U of Pittsburgh, Dept. of Mathematics |
Ph. D.and M.S., Theoretical Mechanics, Rutgers University, Piscataway, NJ.
Mgr., Applied Mathematics(equivalent of M.S.), Charles University, Prague, Czech Republic |
My research interests lie in the area of application of mathematical tools in molecular biology, with a focus on quantification of the relation between the sequence, mechanical properties, and biological function of intracellular components. I have developed micromechanical models of DNA and protein elasticity that combine atomic-scale and continuum mechanics approaches with recent advances in computational chemistry and employ information obtained by X-ray crystallography, single-molecule manipulation, and other experimental techniques. My research program is oriented towards modeling of macromolecular assemblies and the application of mathematical principles in the study of molecular biological processes.
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D. Keller, D. Swigon, & C. Bustamante, Relating single molecule measurements to thermodynamics, Biophys. J, 84,733–738 (2003).
D. Swigon, B.D. Coleman, & W.K. Olson, Modeling the Lac repressor-operator assembly. I. The influence of DNA looping on Lac repressor conformation, Proc. Natl. Acad. Sci. USA, 103, 9879–9884 (2006).
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Pei Tang, Ph.D. Associate Professor, U of Pittsburgh, Dept. of Anesthesiology |
PhD, Physical Chemistry, SUNY at Stony Brook |
Determination of high-resolution domain structures of neuronal ion channels, such as nicotinic acetylcholine and glycine receptors Characterization at the molecular level of how low-affinity drugs, particularly general anesthetics and alcohols, affect the functions of transmembrane ion channels. The approaches used in Dr. Tang's laboratory include high-resolution NMR and large-scale molecular dynamic simulations.
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Liu Z, Xu Y, Tang P: MD simulations of C2F6 effects on gramicidin A: Implication of mechanisms of general anesthesia, Biophysical J, 88, 3784-3791, (2005)
Saladino AC, Xu Y, Tang P (2005) Homology Modeling and Molecular Dynamics Simulations of Transmembrane Domain Structure of Human Neuronal Nicotinic Acetylcholine Receptor,
Biophys J 88, 1009
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George C. Tseng, Sc.D.
Assistant Professor, U of Pittsburgh, Dept. of Biostatistics |
ScD, Biostatistics, Harvard University;
MS, Mathematics, National Taiwan University |
We are a statistical group with major applications on genomics and bioinformatics. Currently we mainly focus on data mining of high-throughput genomic and proteomic data and methods for biomarker detection including supervised (classification) and unsupervised (clustering) machine learning and detection of differentially expressed genes. Related research also include statistical modelling, statistical computing and graphical visualization of data. Collaboration with biology labs plays an important role where most of our projects and methodological ideas come from.
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Tseng GC and Wong WH. Tight Clustering: A Resampling-based Approach for Identifying Stable and Tight Patterns in Data. Biometrics.61:10-16, (2005)Thalamuthu A, Mukhopadhyay I, Zheng X and Tseng GC. Evaluation and comparison of gene clustering methods in microarray analysis. Bioinformatics. 22:2405-2412. (2006) |
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Yoram Vodovotz, Ph.D.
Director, Center for Inflammation and Regenerative Modeling, Professor of Surgery, Immunology, Communication Sci and Disorders, and Comp Biology U of Pittsburgh |
PhD, Immunology, Cornell University Graduate School of Medical Sciences
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Inflammation is a complex, multi-scale biological response to threats, both internal and external to the body, which is also required for proper healing of injured tissue. Dr. Vodovotz established the first institution dedicated to studying inflammation through biomarker analysis, modeling/simulation, and experiments: the Center for Inflammation and Regenerative Modeling (CIRM, www.mirm.pitt.edu/cirm). Dr. Vodovotz has helped develop mathematical models of inflammation and its effects on organ function. These models have translational utility at the pre-clinical and clinical levels. Dr. Vodovotz is funded by the NIGMS, NIAID, NHLBI, NIDRR, the Commonwealth of Pennsylvania, and the Pittsburgh Tissue Engineering Initiative. |
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Vodovotz, Y., Csete, M.; Bartels, J.; Chang, S.; An, G. Translational systems biology of inflammation. PLoS Comput. Biol. 2008. (In Press).
Chow, C. C.; Clermont, G.; Kumar, R.; Lagoa, C.; Tawadrous, Z.; Gallo, D.; Betten, B.; Bartels, J.; Constantine, G.; Fink, M. P.; Billiar, T. R.; Vodovotz, Y. The acute inflammatory response in diverse shock states. Shock 2005. 24:74-84.
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Michael Widom, Ph.D.
Professor of Physics, Carnegie Mellon University |
PhD in Physics, University of Chicago |
My research applies methods of theoretical physics to problems of biological and physical interest. One project considers normal modes of model elastic networks representing proteins and protein assemblies. The maturation of viral capsids is modeled as a soft-mode buckling transition in icosahedral networks. Other interests include the conformations and dynamics of biopolymers such as DNA and RNA.
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M. Widom, J. Lidmar and D. R. Nelson, "Soft modes near the buckling transition of icosahedral shells" (preprint, 2007)
M. Widom and I. Al-Lehyani, "Repton Model of Gel Electrophoresis in the long chain limit",
Physica A 244 (1997) 510-521 |
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Xiang-Qun (Sean) Xie, Ph.D., MBA Professor, U of Pittsburgh, Dept of Pharmaceutical Sciences
and Drug Discovery Institute |
Ph.D., Medicinal Chemistry, School of Pharmacy, University of Connecticut, CT |
Our research is to develop a combined approach of NMR structure, computer pharmacophore-generation,
in-silico screening, and in-vitro bioassay. Our goal is to discover and design highly potent CB2-specific leads and develop them for therapeutic immune-treatments.
Ongoing projects are: I) GPCR CB2 structural proteomics studies by developing recombinant membrane protein engineering and 3D NMR biophysics approaches. II)
Virtual screening and in-silico CB2 drug design method development by integrating NMR and computer modeling into QSAR pharmacophore studies and 3D database query. III) Building Web-interfaced molecular information repository. It also involves building in-house virtual compound databases and constructing structure-diverse or target-specific sub-libraries for drug screening.
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Xie, X.-Q. Book chapter 3: “Molecular Modeling and In-silico Drug Design” in Foye’s Principles of Medicinal Chemistry (editor: D.A. Williams and T. L. Lemke), Lippincott Williams & Wilkins, 2007 (in press)
Wang, J.m.; Krudy, G.; Xie, X-Q.; Wu, C.; Holland, G (2006) "Genetic Algorithm-Optimized QSPR Models for Bioavailability, Protein Binding, and Urinary Excretion." J. of Chem. Info. and Modeling 46, 2674-2683
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Eric Xing, Ph.D.
Assistant Professor, Carnegie Mellon University, Department of Computer Science |
PhD,Computer Science, U.C. Berkeley;
PhD, Molecular Biology, Rutgers University, NJ |
Currently the following major themes are studied in my group: 1) graphical models, Bayesian approaches, inference algorithms, and learning theories for analyzing and mining high-dimensional, longitudinal, and relational data; 2) computational and comparative genomic analysis of biological sequences, systems biology investigation of gene regulation, and statistical analysis of genetic variation, demography and linkage (to diseases);
and 3) application of statistical learning in text/image mining, vision, and machine translation. |
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T. Lin, E.W. Myers and E.P. Xing, Interpreting Anonymous DNA Samples From Mass Disasters --- probabilistic forensic inference using genetic markers, Bioinformatics 22(14):e298-e306. (special issue for The Fourteenth International Conference on Intelligence Systems for Molecular Biology
K. Sohn and E. P. Xing, Hidden Markov Dirichlet Process: Modeling Genetic Recombination in Open Ancestral Space, Advances in Neural Information Processing Systems 19 (NIPS2006). |
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