Faculty

Leadership
	Office: 835 MURD Lab Website	Ivet Bahar, PhD - Distinguished Professor and John K. Vries Chair, Department of Computational & Systems Biology Ph.D. in Chemistry, Istanbul Technical Institute; B.S. and M.S. in Chemical Engineering, Bogazici U. Biomolecular systems are not static: they constantly move, change shape, and interact with each other. Understanding the mechanisms of their interactions and their binding, catalytic and allosteric signaling effects is not possible without a molecular level modeling of their collective dynamics. A major research goal in my lab is to investigate the dynamics of molecular systems in the cellular environment cellular using fundamental principles of physical sciences and engineering. Another is the development of novel quantitative molecular and system pharmacology tools toward discovering novel drugs or repurposing existing drugs, with focus on neurosignaling disorders. Cheng MH, Bahar I (2019) Monoamine transporters: structure, intrinsic dynamics and allosteric regulation Nat Struct. & Molec. Biol. 26: 545–556 Zhang S, Li H, Krieger JM, Bahar I (2019) Shared signature dynamics tempered by local fluctuations enables fold adaptability and specificity Mol Biol Evol. 36: 2053-2068
	412-648-8171 faeder@pitt.edu Office: 837 MURD Lab Website	James R. Faeder, PhD - Associate Professor, Vice Chair for Educational Programs/Initiatives Ph.D. in Chemical Physics, University of Colorado at Boulder I am interested in developing mathematical models of biological regulatory processes that integrate specific knowledge about protein-protein interactions. My current research includes the development of specific models of signal transduction and the development of new stochastic simulation algorithms that will greatly broaden the scope of models that can be developed. Other research areas include model reduction, parameter estimation and uncertainty analysis, and automated model construction from databases of protein interactions. Singh M, Oltvai ZN, Warita K, Warita T, Faeder JR, Lee REC, Sant S (2018) Shift from Sto-chastic to spatially-ordered expression of serine-glycine synthesis enzymes in 3D microtumors Sci. Rep. 8:9388: Morel PA, Lee REC, Faeder JR (2017) Demystifying the cytokine network: Mathematical models point the way Cytokine. 98: 115-123
Faculty
	412-648-8646 jayoob@pitt.edu Office: 747 MURD Lab Website	Joseph C. Ayoob, PhD - Associate Professor Ph.D., Neuroscience, Johns Hopkins University School of Medicine Research: As an experimentalist, I use molecular-genetic approaches to study developmentally-regulated cell death and engulfment. Studying this process during the development of an organism will give us new insights into how this same process also eliminates pre-cancerous cells in the adult. Training and Outreach: To reach out to and train the next generation of scientists, we have initiated Tiered Mentoring and Training programs for undergraduates (TECBio REU @ Pitt) and high school students (DiSCoBio Summer Academy) to prepare them for careers in STEM (see Education page for more info). Delubac D, Highley CB, Witzberger-Krajcovic M, Ayoob JC, Furbee EC, Minden JS, Zappe S (2012) Microfluidic system with integrated microinjector for automated Drosophila embryo injection. Lab Chip. 12(22): 4911-9. [JIF=6.260] Ayoob JC, Chennubhotla CS (2012) First Steps: Tomorrow's Scientists International Innovation, North America - The Future of American Research. May: 30-32
	jpbarton@pitt.edu Office: 830 Murdoch Lab Website	John Barton, PhD - Associate Professor Ph.D., Physics, Rutgers University The evolution of pathogens like HIV, SARS-CoV-2, and influenza presents a major threat to public health. My group works to combat this threat by studying how pathogens evolve and how they interact with the immune system. We’re particularly interested in questions related to the predictability of evolution and coevolution of hosts and pathogens. For example, which strains of a pathogen will become dominant in the near future? Can we guide evolution to prevent pathogens from escaping immune control or developing resistance to drugs? Our work combines mathematical modeling, simulation, and data analysis to pursue these questions. Lee B, Sohail MS, Finney E, Ahmed SF, Quadeer AA, McKay MR, Barton JP (2022) Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data medRxiv. Sohail MS, Louie RHY, McKay MR, Barton JP (2021) MPL resolves genetic linkage in fitness inference from complex evolutionary histories Nature Biotechnology. 39 (4):: 472-479
	412-624-1223 jberg@pitt.edu Office: 825 MURD Lab Website	Jeremy M. Berg, PhD - Professor Ph.D. in Chemistry, Harvard University Specific interactions between macromolecules are key to essentially all biological processes. Our research program has two related goals. The first is to understand the structural and chemical bases by which these specific interactions occur. The second is to understand why, biologically and evolutionarily, particular interactions have the strengths that they do. Systems of particular interest involve peroxisomal protein targeting and protein and nucleic acid interactions involving zinc-binding domains. Jeremy M. Berg is Director of the Institute of Personalized Medicine, Associate Vice Chancellor for Science Strategy and Planning in the Health Sciences, and Professor of Computational and Systems Biology at the University of Pittsburgh. Berg JM, Berg WA (2016) No myth: Benefits of breast screening Nature. 529(7586): 283 Geskin A, Legowski E, Chakka A, Chandran UR, Barmada MM, LaFramboise WA, Berg JM, Jacobson RS (2015) Needs Assessment for Research Use of High-Throughput Sequencing at a Large Academic Medical Center PloS. 10: e0131166
	412-648-7776 ccamacho@pitt.edu Office: 745 MURD Lab Website	Carlos J. Camacho, PhD - Associate Professor Ph.D. in Physics, University of Maryland, College Park A striking set of specific and non-specific interactions encoded in the protein structure tolerates binding only to a unique substrate. My main research interests focus on modeling the physical interactions responsible for molecular recognition, and in the development of new technologies for structural prediction, their substrates and supramolecular assemblies. Any progress in these fundamental problems is bound to bring about a better understanding of how proteins work cooperatively in a cell, promoting breakthroughs in every aspect of the biological sciences. Pabon NA, Zhang Q, Cruz JA, Schipper DL, Camacho CJ, Lee REC (2019) A network-centric approach to drugging TNF-induced NF-κB signaling Nature Communications. 10: 860 Ye Z, Needham PG, Estabrooks SK, Whitaker SK, Garcia BL, Misra S, Brodsky JL, Camacho CJ (2017) Symmetry breaking during homodimeric assembly activates an E3 ubiquitin ligase. Sci Rep. 7(1): 1789
	412-648-3335 anc201@pitt.edu Office: 9053A -5 BST3 Lab Website	Anne Ruxandra Carvunis, PhD - Assistant Professor Ph.D., Bioinformatics, Université Joseph Fourier, Grenoble, France What makes each species unique? Why is it that drugs that cure rats in the lab are often powerless against human disease? A major goal of my research is to work out the molecular mechanisms of change and innovation in biological systems in order to define the genetic and network-level determinants of species-specificity. No recent publications authored or co-authored by Carvunis, A found. Try searching pubmed.gov.
	cheny25@upmc.edu Lab Website	Yu-Chih Chen, PhD - Assistant Professor Ph.D., Electrical Engineering and Computer Science, University of Michigan, Ann Arbor Due to genomic and epigenetic instability of cancer cells, inter-patient and intra-patient heterogeneity in tumors creates formidable challenges in identifying optimal treatments. To address the challenges, I aim to establish comprehensive high-throughput multi-omics single-cell analysis including genome, epigenome, transcriptome, proteome, functional, and morphological methods. With large amounts of data collected from high-throughput single-cell multi-omics analysis, machine learning techniques can predict patient prognosis and suggest treatments for precision medicine. The integrated approach will change how we understand and treat cancer and ultimately improve outcomes for patients. Chen YC, Gonzalez ME, Burman B, Zhao X, Anwar T, Tran M, Medhora N, Hiziroglu AB, Lee W, Cheng YH, Choi Y, Yoon E, Kleer CG (2019) Mesenchymal stem/stromal cell engulfment reveals metastatic advantage in breast cancer Cell Reports. 27: 3916–3926 Chen YC, Allen SG, Ingram PN, Buckanovich R, Merajver SD, Yoon E (2015) Single-Cell Migration Chip for Motility-based Microfluidic Selection of Heterogeneous Cell Populations Scientific Reports. 5: 9980
	412-648-7794 chakracs@pitt.edu Office: 746 MURD Lab Website	Chakra Chennubhotla, PhD - Associate Professor Ph.D. in Computer Science, University of Toronto Developing computational models and methods to improve the understanding of major interactions and allosteric mechanisms that underlie the proper functioning of biomolecular systems. In particular (i) developing information-theoretic concepts for determining the probabilistic rates, pathways, and sequences of information flow in multicomponent and cellular biomolecular systems, (ii) designing and interpreting FRET based experiments to explore and assess functional implications of molecular interactions and correlations, and (iii) developing novel computer vision methods for analyzing, refining and interpreting structure, dynamics, and function in biomolecular images and movies. Benos PV, Tosun BA, Manatakis DV, Vukmirovic M, Nguyen L, Yan X, Hu B, Deluliis G, Woolard T, Maya JD, Homer R, Kaminski N, Chennubhotla CS (2017) Towards Understanding Spatial Lung Tissue Heterogeneity In Idiopathic Pulmonary Fibrosis (IPF) A72. Mechanisms Driving Fibrosis. Narayanan C, Bernard DN, Bafna K, Choudhary OP, Chennubhotla CS, Agarwal PK, Doucet N (2017) Conformational Motions Impacting Function in an Enzyme Superfamily The FASEB Journal. 31(1 supplement): 762.6
	412-648-3338 mchikina@pitt.edu Office: 833 MURD Lab Website	Maria Chikina, PhD - Assistant Professor Ph.D. in Molecular Biology, Princeton University The rise of genome-scale experimental methods has greatly accelerated the speed of biological data accumulation. However, as datasets increase in size, it becomes easier to find patterns and correlations, but harder to distinguish true biological insight from technological and statistical artifacts. Consequently, exploiting large-scale datasets to inform our understanding of biological systems remains a challenge. My work has focused on bridging the gap between statistically rigorous computational techniques and knowledge of underlying biological and experimental processes to develop methods that overcome the biases and artifacts inherent in the structure of large-scale datasets and transform noisy data into concrete biological knowledge. Buschur KL, Chikina M, Benos PV (2019) Causal network perturbations for instance-specific analysis of single cell and disease samples Bioinformatics. Overacre-Delgoffe AE, Chikina M, Dadey RE, Yano H, Brunazzi EA, Shayan G, Horne W, Moskovitz JM, Kolls JK, Sander C, Shuai Y, Normolle DP, Kirkwood JM, Ferris RL, Delgoffe GM, Bruno TC, Workman CJ, Vignali DAA (2017) Interferon-γ Drives Treg Fragility to Promote Anti-tumor Immunity. Cell. 169(6): 1130-1141
	ishihara@pitt.edu Office: BST3 Lab Website	Keisuke Ishihara, PhD - Assistant Professor (starting December 2022) Ph.D. in Systems Biology, Harvard University My group takes a synthetic approach to study how cells form tissues. “Synthetic” embodies the experimental creation of states of physical organization and gene expression that push a multicellular system to all possible extremes. The synthetic approach allows us to discover novel regulatory molecules, dormant genetic programs, and general physical principles, which we will critically evaluate as next generation strategies for organ engineering. My group will quantitatively capture 3D tissue morphogenesis through imaging, computation, and molecular profiling. We will use this knowledge to develop genetic and chemical tools to engineer in vitro tissues such as human brain organoids and cardiac organoids. Ishihara K, Mukherjee A, Gromberg E, Brugues J, Tanaka E, Jülicher F (2021) Topological morphogenesis of neuroepithelial organoids bioRxiv. Ishihara K, Decker F, Caldas P, Pelletier JF, Loose M, Brugues J, Mitchison TJ (2021) Spatial Variation of Microtubule Depolymerization in Large Asters Mol. Biol. Cell.
	412-383-5745 dkoes@pitt.edu Office: 748 MURD Lab Website	David R. Koes, PhD - Associate Professor Ph.D. in Computer Science, Carnegie Mellon University Removing barriers to computational drug discovery bit by bit. I create novel computational methods for accelerating the pace of discovery and enhancing the accuracy of virtual screening. Sunseri J, Koes DR (2020) libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications J Chem Inf Model. 60(3): 1079-1084 Gau D, Lewis T, McDermott L, Wipf P, Koes DR, Roy P (2018) Structure-based virtual screening identifies a small-molecule inhibitor of the profilin 1-actin interaction J Biol Chem. 293(7): 2606-2616
	412-648-8607 robinlee@pitt.edu Office: 9053A -3 BST3 Lab Website	Robin E.C. Lee, PhD - Associate Professor Ph.D. in Cellular and Molecular Medicine, University of Ottawa To decide between irreversible cell fates such as growth, differentiation or death, cells process information about their environment through a network of molecular circuits. Our research combines principles of systems and synthetic biology with large-scale data to understand how information flows through these circuits. By observing input-output relationships in the same cell using microfluidics, live-cell dynamics and single-molecule microscopy, we aim to decode the ‘language’ of signaling dynamics and develop mathematical models of information flow with single-cell resolution. Our ultimate goal is to understand how population-level responses emerge from single-cell heterogeneity and to rationally manipulate cell fate decisions in disease. Cruz JA, Mokashi CS, Kowalczyk GJ, Guo Y, Zhang Q, Gupta S, Schippper DL, Lee REC (2021) A variable-gain stochastic pooling motif mediates information transfer from receptor assemblies into NF-κB Science Advances. 7: Mokashi CS, Schippper DL, Qasaimeh MA, Lee REC (2019) A System for Analog Control of Cell Culture Dynamics to Reveal Capabilities of Signaling Networks iScience. 19: 586-596
	ndlord@pitt.edu Office: 10019 BST3 Lab Website	Nate Lord, PhD - Assistant Professor Ph.D., Systems Biology, Harvard University Developing embryos must orchestrate the fates and movements of their cells with precision. However, precise control is no easy feat; genetic mutations, unexpected environmental perturbations and noisy signaling all threaten to scramble communication. Despite these challenges, development is remarkably robust. How do developing systems ensure precise pattern formation? How are mistakes corrected when they occur? Can we learn to engineer synthetic systems to have the reliability of developing embryos? Answers to these questions must span multiple scales, from signaling responses in individual cells to collective cell movement and morphogenesis. Our lab will tackle these questions with a combination of optogenetic manipulation, quantitative microscopy, computational modeling and classical embryology. Over the long run, we hope to learn the mechanistic principles that enable embryos to avoid and correct errors in development. Lord ND, Carte AN, Abitua PB, Schier AF (2019) Co-receptor expression shapes the Nodal signaling gradient bioRxiv. Lord ND, Norman TM, Yuan R, Bakshi S, Losick R, Paulsson J (2019) Stochastic antagonism between two proteins governs a bacterial cell fate switch Science. 366: 116-120
	stallaert@pitt.edu Office: The Assembly Lab Website	Wayne Stallaert, PhD - Assistant Professor Ph.D in Biochemistry, Université de Montréal I am fascinated by the plasticity of the cell cycle and its extraordinary capacity to adapt to changes in the environment or genome. Resolving this diversity in cell cycle regulation is key to fully understanding biological problems ranging from development and regeneration to inflammation, aging and cancer, where the decisions of when and where to trigger cell division are of paramount importance. My lab combines advanced single-cell imaging and computational approaches to study cell cycle plasticity in its natural habitat: embedded within a complex, multicellular environment. Using patient-derived tissues and organoids, we investigate the contribution of cell cycle plasticity in tumorigenesis and cancer drug resistance, and how the spatial organization of a tumor shapes a cancer cell’s decision to proliferate or arrest. Stallaert W, Taylor SR, Kedziora KM, Taylor CD, Sobon HS, Young CL, Limas JC, Holloway JV, Cook JG, Purvis JE (2022) The molecular architecture of cell cycle arrest Molecular Systems Biology. Stallaert W, Kedziora KM, Taylor CD, Zikry TM, Ranek JS, Sobon HS, Taylor SR, Young CL, Cook JG, Purvis JE (2022) The structure of the human cell cycle Cell Systems. 13(3): 230-240
	412-648-3338 dltaylor@pitt.edu Office: 10045 BST3 Lab Website	D. Lansing Taylor, PhD - Distinguished Professor; Director, University of Pittsburgh Drug Discovery Institute Ph.D. in Cell Biology, State University of New York at Albany My research interests have been rooted in understanding the temporal-spatial dynamics of signaling molecules and proteins in living cells, coupled to defining the mechanisms of fundamental cell functions such as cell division and cell migration. I have always integrated the development of new technologies in fluorescence-based reagents and light microscope imaging in order to improve the ability to define molecular events in cells and tissue models. My interests have evolved from single cell activities to understanding cellular population dynamics, including the biological basis for heterogeneity in response to perturbagens such as drug treatments. Uttam S, Stern AM, Furman S, Pullara F, Spagnolo D, Nguyen L, Gough AH, Sevinsky C, Ginty F, Taylor DL, Chennubhotla SC (2020) Spatial domain analysis predicts risk of colorectal cancer recurrence and infers associated tumor microenvironment networks Nature Communications. 11: 3515 Tosun AB, Nguyen L, Ong N, Navolotskaia O, Carter G, Fine JL, Taylor DL, Chennubhotla SC (2017) Histological detection of high-risk benign breast lesions from whole slide images, Medical Image Computing and Computer Assisted Intervention (MICCAI ‘17), Quebec, Canada, September 10-14, 2017, Proceedings, part 2, pp. 1444-152 .
	412-383-7475 shf28@pitt.edu Office: 817 MURD Lab Website	Shikhar Uttam, PhD - Assistant Professor Ph.D. Electrical Engineering (Minor: Mathematics) The University of Arizona, Tucson Primary focus: Computational imaging and optics, signal and systems, machine learning and imaging science applied to cancer systems biology, cancer epigenetics, early cancer detection, and precision medicine. Secondary focus: Computational biology and bioinformatics. Other interests: Radar imaging and quantum information theory. Uttam S, Hashash JG, LaFace J, Binion D, Regueiro M, Hartman DJ, Brand RE, Liu Y (2020) Three-Dimensional Nanoscale Nuclear Architecture Mapping of Rectal Biopsies Detects Colorectal Neoplasia in Patients with Inflammatory Bowel Disease Cancer Prevention Research. 12(8): 527-538 Uttam S, Stern AM, Furman S, Pullara F, Spagnolo D, Nguyen L, Gough AH, Sevinsky C, Ginty F, Taylor DL, Chennubhotla SC (2020) Spatial domain analysis predicts risk of colorectal cancer recurrence and infers associated tumor microenvironment networks Nature Communications. 11: 3515
	412-383-5856 avogt@pitt.edu Office: W 948 BST Lab Website	Andreas Vogt, PhD - Associate Professor Ph.D. in Pharmaceutical Chemistry, University of Hamburg My major research interest is the discovery of new therapeutic agents for diseases related to cell proliferation and intracellular signaling. Specific targets of interest are the mitogen-activated protein kinase phosphatases (MKPs), cellular enzymes involved in cancer, immune response, and heart disease and development that have largely eluded discovery efforts. An important part of my research is the development of analysis tools to increase information content of biological assays and to enable small molecule drug discovery in whole multicellular organisms such as zebrafish. Chan L, Murakami M, Caeser R, Hurtz C, Kume K, Sadras T, Shojaee S, Hong C, Pölönen P, Nix M, Ugale A, Z Che, Lee J-W, Cosgun N, Geng H, Chen C, Chen J, Vogt A, Heinäniemi M, Lohi O, Wiita A, Izraeli S, Graeber T, Weinstock D, and Mü (2020) Signaling input from divergent pathways subverts malignant B-cell transformation Nature. Saydmohammed M, Vollmer LL, Onuoha EO, Maskrey TS, Gibson G, Watkins SC, Wipf P, Vogt A, Tsang M (2018) A High-Content Screen Reveals New Small-Molecule Enhancers of Ras/Mapk Signaling as Probes for Zebrafish Heart Development Molecules. 23: 7
	412-383-5743 xing1@pitt.edu Office: 9053A -4 BST3 Lab Website	Jianhua Xing, PhD - Professor Ph.D., Theoretical Chemistry, University of California, Berkeley, 2002 The Xing lab is interested in the following fundamental questions. How do thousands of molecules species orchestrate temporally and spatially to determine a cell phenotype? How can one regulate and direct cell phenotype? Specifically, the lab currently focuses on Epithelial-to-Mesenchymal Transition (EMT), characterized by loss of cell-cell adhesion and increased cell motility. EMT plays important roles in embryonic development, tissue regeneration, wound healing and pathological processes such as fibrosis in lung, liver, and kidney, and cancer metastasis. The lab studies the coupled gene expression and epigenetic dynamics of EMT. Zhang H, Tian X, Kim KS, Xing JH (2014) Statistical mechanics model for the dynamics of collective epigenetic histone modification, Physical Review Letters. 112: 068101 Wang P, Song C, Zhang H, Wu Z, Tian XJ, Xing JH (2014) Epigenetic state network approach for describing cell phenotypic transitions Interface Focus. 4(3): 20130068
Emeritus Faculty
	412-648-3333 hagaim@pitt.edu Lab Website	Hagai Meirovitch, PhD - Professor Emeritus Ph.D. in Statistical Mechanics, The Weizmann Institute of Science Structure and function of proteins by the energetic and statistical approaches. Development of modeling of solvation, methods for calculating the entropy and the free energy of macromolecules and fluids (water), and simulation and conformational search techniques for protein systems. These methods are components of a new statistical mechanics methodology for treating flexibility applied to loops, peptides, and active sites to understand protein-protein and protein-ligand recognition processes (e.g., antibody-antigen interactions) and to analyze NMR and x-ray data of flexible molecules. General IJ, Dragomirova R, Meirovitch H (2012) Absolute free energy of binding of avidin/biotin, revisited J Phys Chem B. 116: 6628-36 Meirovitch H (2010) Methods for calculating the absolute entropy and free energy of biological systems based on ideas from polymer physics. J Mol Recognit. 23: 153-72
	412-383-9146 vriesjk@pitt.edu Office: 3061 BST3 Lab Website	John K. Vries, MD - Professor Emeritus M.D., University of California San Francisco Asymmetry in the distribution of attributes along biological sequences generates signals with characteristic frequency and phase spectra. Asymmetry in the distribution of contacts in 3-dimensional models also generates signals with characteristic spectra. In some cases, these spectra are correlated. My research attempts to predict tertiary structure from these correlations. The long term goal is go develop an alignment-independent method for protein classification. The methodologies employed include n-gram analysis, Fourier analysis, eigenfunction decomposition and all poles spectral density estimation. In related research, correlations between the periodicity of pairwise relationships in molecular dynamics simulations and the results of Gaussian network analysis are compared. Koes DR, Vries JK (2017) Evaluating Molecular Mechanics Force Fields with a Quantum Chemical Approach Biophysical Journal. 112 (3): 289a Koes DR, Vries JK (2017) Error assessment in molecular dynamics trajectories using computed NMR chemical shifts Computational and Theoretical Chemistry. 1099: 152-166