Biomedical Informatics Faculty
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- Core Faculty are members of the Executive Committee and are advising faculty
- Advising Faculty may serve as primary advisor for BMI students
- Collaborating Faculty may serve as co-advisors for BMI students
Core Faculty (Executive Committee members)
- Russ B.
Altman. (Program Director). Research focuses on the creation of computational tools and resources to solve problems in biology and medicine. Current projects are focused on three areas: 1) creating a database for how genetic variation in humans is associated with differences in drug response (pharmacogenomics), with particular recent emphasis on the drug warfarin, (PharmGkb), 2) creating methods for identifying protein and RNA molecular function, in order to understand how we may engineer them to function differently (FEATURE) and 3) understanding how physics-based simulation of biological structures can be facilitated at scales ranging from molecules to intact humans (Simbios). Informatics methodologies include: supervised and unsupervised machine learning, natural language processing, molecular dynamics simulations, database design, knowledge representation.
- Atul
Butte. Translational bioinformatics has been defined as the development of analytic, storage, and interpretive methods to optimize the transformation of increasingly voluminous genomic and biological data into diagnostics and therapeutics for the clinician. The research goal is to develop translational bioinformatics methods to reason over many available genome-scale measurement and experimental modalities, and apply these methods to study complex disorders in genomic medicine, especially obesity and type 2 diabetes mellitus. The Butte Lab has four main directions in exploring integrative biology. First, we have developed bioinformatics methods to integrate genomic, genetic, phenotypic, clinical, and gene-knockout data from multiple sources and phenotypes and reason over these data. An example of this was our work in adipogenesis published in Nature Cell Biology (2005). Second, we have developed tools to automatically index and find genomic and proteomic data sets based on the phenotypic and contextual details of each experiment, published in Nature Methods (2007). We used these tools to create a comprehensive phenome-genome network published in Nature Biotechnology (2006). Third, we are building a novel gene-expression-based classification scheme for diseases across the entire field of medicine, as described in the New York Times and International Herald Tribune (2008). Fourth, we consider clinical measurements with molecular measurements to build multi-scale models of human health and disease, as published in Science (2008).
- Teri E. Klein. Research interests extend over the broad spectrum of pharmacogenetics, computational biology and bioinformatics. Applications include the development of a pharmacogenetics knowledge base, structure-function relationships, de novo modeling and the structural basis of disease.
- Mark Musen. Research interests involve construction of automated systems to assist biomedical decision making, focusing on areas where the decision making is impeded by difficulties in formalizing knowledge and in encoding that knowledge for use by the computer. Current work addresses mechanisms by which computers can assist communities of scientists in the development of large, electronic knowledge bases. The Protégé system provides an experimental framework for investigation of collaborative knowledge-base development, of mapping among knowledge bases, and of knowledge-base visualization. The National Center for Biomedical Ontology drives research on ontology-based access to biomedical data and knowledge, community-based peer review of electronic knowledge bases, and management of knowledge-base evolution. The laboratory studies architectures for intelligent systems in areas as diverse as protocol-based therapy and surveillance for possible bioterrorism.
- David Paik. Research interests lie at the intersection of radiology, molecular biology and informatics. We focus on developing and validating computational methodologies for extracting useful information content from anatomic, functional and molecular images, drawing upon image processing, computer vision, computer graphics, computational geometry, machine learning, biostatistics, modeling and simulation.
- Daniel Rubin. Research interests focus on biomedical and translational imaging informatics. We develop computational methods to identify and to extract information and meaning from images ("imaging phenotype") and to integrate and relate the image information to biological and clinical data ("molecular/clinical phenotype"). Our goal is to exploit images on a massive scale for discovery, similar to the data-driven approaches in modern bioinformatics, enabling us to discover image biomarkers of disease and to build predictive disease models from image data. We translate our methods into practice by creating computer applications (such as decision support) that will improve diagnostic accuracy and clinical effectiveness.
- Nigam Shah. My overarching interest is to make biomedical information actionable. I am interested in: (1) Annotation Analytics: Using methods for automated annotation and over 200 biomedical ontologies, we have created over 16 billion annotations on 22 public data sources. We are developing methods to mine such large annotation corpora for detecting hidden associations. (2) Data driven medicine: We are currently analyzing data from the electronic health record data warehouses of Stanford hospital comprising of over one million patients (~9.5 million notes) to identify statistically significant patterns of off-label drug use and for drug safety surveillance (http://tinyurl.com/SIG-emr-mining). (3) Socially Conscious Informatics: Clinical decision support tools traditionally focus on supporting a high trained individual—the doctor. Let's turn decision support on its head to aid the patient and provide support on a cell phone. We are semi-finalists in the Data Design Diabetes challenge (www.datadesigndiabetes.com/)
Advising Faculty
- Serafim Batzoglou. Our lab is interested in the applications of computer science to genomic research. Current research focuses on cancer genomics, disease associations, next generation genome sequencing and assembly, population ancestry inference, and other topics related to whole genome sequencing and population genomics.
- Mohsen Bayati. I have two main research interests: large-scale statistical data-mining, and applications of information technology in healthcare. In particular, I use tools from graph theory, machine learning, probability, and statistical physics in data-driven healthcare (predictive models, optimization, and decisions), high dimensional statistics, and networks.
- Gill Bejerano. Our lab seeks to understand the human genome through vertebrate comparative, functional, and paleo-genomics, including such topics as the functional landscape of the human genome and its evolution, with particular focus on vertebrate gene regulation and its contributions to morphological diversity, development, and human disease; functions, origins, and evolution of proximal and distal cis-acting regulatory elements; the paradoxical existence of ultraconserved elements; co-option of mobile DNA elements (repeats) as a driving force in vertebrate evolution; and the interpretation of ancestral genomes.
- Kwabena Boahen. Our group has two synergistic goals: To understand how brains work; this will advance treatment of neurological diseases. And to build computers that work like brains; this will increase computational power a million fold. To these two ends, we are building large-scale neural models to link cellular-level biophysical processes with the system-level functions that they enable (e.g., cognition), through an interdisciplinary effort that brings electronics and computer science in contact with neurobiology and medicine.
- Margaret Brandeau. Research focus is the application of mathematical and economic models to evaluate disease prevention and treatment programs. Current research focuses on HIV and drug abuse interventions, hepatitis B screening and vaccination, pandemic influenza preparedness, and bioterror response planning.
- Douglas Brutlag. Professor Emeritus of Biochemistry and Medicine (by courtesy), teaches courses in Genomics and Medicine (Biochem 118), Genomics, Bioinformatics and Medicine (Biochem 158/258 and HumBio 158G), Computational Molecular Biology (BIOC 218/BIOMEDIN 231) and Your Genes and your Health (Bio 84).
- Carlos Bustamante. Research focuses on analyzing genome wide patterns of variation within and between species to address fundamental questions in biology, anthropology, and medicine. My group works on a variety of organisms and model systems ranging from humans and other primates to domesticated plant and animals. Much of our research is at the interface of computational biology, mathematical genetics, and evolutionary genomics.
- J. Michael Cherry. Lab develops and maintains the Saccharomyces Genome Database (SGD). The SGD provides information and tools on budding yeast genome, its products and their interactions. Several computational tools have been developed to provide to allow the research community to explore the collected data sets. Tools for querying >50,000 full-text papers are also provided. SGD has become an essential research tool used daily by thousands of researchers around the globe. Dr. Cherry's second area of research is in the creation of ontologies to aid communication between biologists as well as biological database projects. His group is a founding member of the Gene Ontology (GO) Collaboration.
- Markus Covert. Research focus is on building computational models of complex biological processes and using these models to guide an experimental program. Such an approach leads to a relatively rapid identification and validation of previously unknown components and interactions. Biological systems of interest include metabolic, regulatory, and signaling networks as well as intercellular interactions. Current research involves the dynamic behavior of NF-kappa B, an important family of transcription factors whose aberrant activity has been linked to oncogenesis, tumor progression, and resistance to chemotherapy.
- Rhiju Das. The Das group strives to predict how sequence codes for structure in proteins, nucleic acids, and heteropolymers whose folds have yet to be explored. We use new computational and experimental tools to tackle the de novo modeling of protein and RNA folds, the high-throughput structure mapping of riboswitches and random RNAs, and the design of self-knotting and self-crystallizing nucleic acids.
- David Dill. Our lab is interested in Boolean modeling to gaining insight into cellular processes at a systems level. Our work includes analysis of Boolean circuit models using methods based on logic and automata theory, applied to understanding of the cell cycle, signal transduction networks, etc., and Boolean analysis of relationships in multiple large data sets, to understand regulation and global differences in gene expression among cell types.
- James Ferrell. Research lab has two main goals: to understand the regulation of entry into and progression through mitosis and meiosis, and to understand the basic logic of signaling cascades and loops.
- Hunter Fraser. We study the regulation and evolution of gene expression using a combination of experimental and computational approaches. Our work brings together quantitative genetics, genomics, epigenetics, and evolutionary biology to achieve a deeper understanding of how genetic variation within and between species affects genome-wide gene expression and ultimately shapes the phenotypic diversity of life. Some of our long-term goals are to better understand: 1) How new mutations affect gene expression, 2) What selective pressures act on these mutations, 3) How (and how often) changes in gene expression affect other phenotypes, including human disease
- Sam (Sanjiv) Gambhir. Research focuses on merging advances in molecular biology with those in biomedical imaging to advance the new field of molecular imaging. Strategies for imaging cellular/molecular events in small animals and humans are in development. These include studying gene expression, signal transduction, enzyme levels and receptor levels in vivo. Use of these technologies for better management of cancer patients are emphasized. Mathematical modeling of these processes to better quantitate imaging data are also being pursued.
- Mary Goldstein. Health services research in primary care and geriatrics. Ongoing work includes evaluation of methods of implementing clinical practice guidelines, for which she leads a multisite hypertension guidelines project using the ATHENA decision support system. Another research focus is evaluation of newly developed tools for automated guidelines, particularly for quality assessment.
- Leonidas Guibas. Heads the Geometric Computation group in the Computer Science Department of Stanford University and is a member of the Computer Graphics and Artificial Intelligence Laboratories. He works on algorithms for sensing, modeling, reasoning, rendering, and acting on the physical world. His interests span computational geometry, geometric modeling, computer graphics, computer vision, sensor networks, robotics, and discrete algorithms --- all areas in which he has published and lectured extensively.
- Trevor Hastie. Specializes in applied nonparametric regression and classification. His current research focuses on applied problems in biology and genomics, medicine and industry, in particular data mining, prediction and classification problems.
- Susan Holmes. Applications to Biology, in particular phylogenetic trees. Computational statistics, in particular, nonparametric computer intensive methods such as the bootstrap. Teaching using simulations and web-based tools. Image analysis. Immunology.
- Daphne Koller. Research focuses on applying machine learning and probabilistic methods to the analysis and reconstruction of cellular networks. Current projects include the extraction of regulatory networks from gene expression data; the analysis of the effect of individual genetic variation on regulation and phenotype; and understanding how different network structures manifest in terms of gene expression, phenotype, genetic interactions, and more.
- Michael Levitt. Research asks if it is possible to understand the molecular structure and function of proteins and nucleic acids in enough detail to make accurate predictions about structure and function. We are mounting a two-pronged attack on this problem using both molecular dynamics simulation and molecular modeling.
- Chris Longhurst. Clinical Informatics is the application of informatics and information technology to deliver healthcare services. In his administrative role as Chief Medical Information Officer (CMIO) at Lucile Packard Children's Hospital (LPCH), Dr. Longhurst help oversee the Clinical Informatics department at Packard Children’s (http://learnlinks.lpch.org/people/clinical-informatics.html), which is responsible for the implementation and optimization of a comprehensive electronic medical record (EMR) system including computerized physician order entry (CPOE) with clinical decision support (CDS), patient-engaging technologies like personal health records (PHR), and an analytics team building an enterprise data warehouse (EDW).
Together with colleagues including Jon Palma (http://med.stanford.edu/profiles/Jonathan_Palma/), Natalie Pageler (http://med.stanford.edu/profiles/Natalie_Pageler/), Scott Sutherland (http://med.stanford.edu/profiles/Scott_Sutherland/), and Jin Hahn(http://med.stanford.edu/profiles/Jin_Hahn/), our applied informatics research focuses on rigorously evaluating the best ways to implement and optimize health information technology to benefit the patients we serve at Packard Children’s. Results of this work have been published in the New England Journal of Medicine (2011), BMJ (2011), Pediatrics (2010, 2011), and Applied Clinical Informatics (2011, 2012).
- Henry Lowe. Primary research interests are in the areas of clinical and research information systems design, development and evaluation including multimedia clinical systems, integration of data to support patient care and clinical research, biomedical terminologies, automated indexing of biomedical documents, cancer information systems and biomedical data security.
- Parag Mallick. Research in the Mallick lab centers on developing and applying multi-scale systems approaches to enable personalized, predictive medicine in cancer. Specifically, we are developing computational methods and experimental techniques to identify diagnostic and prognostic circulating biomarkers. Biomarker-based approaches to detect cancers as early as possible and to personalize treatment are envisioned to radically improve patient outcomes and reduce healthcare costs. Within our multi-scale framework, one can consider biomarkers to be host-scale variables that inform tumor and cell-scale phenomena. Our approach to marker discovery begins with the development of molecular/cellular-scale models that attempt to describe how cells are likely to behave in response to endogenous (mutation) or exogenous perturbation (therapeutics). At the tumor-scale, we are investigating tumor heterogeneity and evolution. Recently, we have been interrogating the role of tumor-microenvironment in directing tumor evolution. At the host-scale, we are attempting to model the relationship between the tumor and the circulating proteomes to help inform biomarker candidate selection. Together, these inquiries will enable us to better understand cancer and to enable rational, model-driven approaches to biomarker discovery.
- Stephen Montgomery. Identifying the molecular causes of phenotypic diversity will be enhanced by our ability to decipher individual genomes. However, the complexity of life, our resolution and subsequent ability to define individual traits and the vast information encoded within each genome has made the direct translation of genotype to phenotype elusive. The Montgomery Lab aims to uncover and define how a specific class of genetic variation, namely those variants which effect the expression of genes, first impact cellular state and behavior and then ultimately play a role in defining human traits and disease. For more information, visit http://montgomerylab.stanford.edu/
- Sandy Napel. Research focuses on CT and other medical imaging modalities. Our lab is currently interested in efficient and reproducible methods of extracting and visualizing medical information from the thousands of images typically generated by one or more radiological exams performed for each patient.
- Richard Olshen. Dr. Olshen's interests regarding research are in statistics and mathematics and
their applications to medicine and biology. Many efforts have concerned binary tree-structured algorithms for classification, regression, survival analysis, and clustering. Those for classification and survival analysis have been used with success in computer-aided diagnosis and prognosis, especially in cardiology, oncology, and toxicology. With the late Leo Breiman, Jerome Friedman (of Stanford), and Charles Stone (of the University of California, Berkeley) he coauthored the book Classification and Regression Trees, that gives motivation, algorithms, various examples, and mathematical theory for what have come to be known as CART algorithms.
The approaches to tree-structured clustering have been applied to lossy data compression, especially in digital radiography (with Robert Gray of the Department of Electrical Engineering at Stanford and others), and also to HIV genetics. Recent research concerns applying information on SNPs (single nucleotide polymorphisms) and other features as together they predispose to hypertension in a population of white women.
Much of his work concerns analyses of longitudinal data. Some that was of interest concerned the pharmacokinetics of intracavitary chemotherapy with systemic rescue. Related efforts have also been to the development of mature walking, longitudinal studies of cholesterol, and many aspects of glomerular filtration in patients with nephrotic disorders. With the late David Sutherland, Edmund Biden, and Marilynn Wyatt I coauthored The Development of Mature Walking.
He was one of the founders of the NCI-designated UCSD Clinical Cancer
Center, which is now the University of California, San Diego Medical Center Moores Cancer Center. He was a Statistical Editor of the Journal of the National Cancer Institute.
Some research has involved more mathematical problems, including those
that arise concerning exchangeable probabilities, conditional levels of particular test statistics, topological category regarding CART-like estimators in regression, and successive standardization of rectangular arrays of numbers. Dr. Olshen is a
Professor of Health Research and Policy (Biostatistics) and (by Courtesy) Professor of Electrical Engineering and of Statistics and Chief, Division of Biostatistics. - Douglas K. Owens. Research concerns health policy, clinical policy, and the development of analytic methods for evaluating policy questions. Particular interest in technology assessment and the application of decision theory to clinical/health policy problems. Secial interest in questions related to disease caused by the human immunodeficiency virus (HIV) and cardiovascular disease.
- Art Owen. My research interests include analysis of high throughput biological data, for instance finding age-related genes in multiple species and tissues with the Kim lab. I am generally interested in settings where both rows and columns of the data matrix correspond to entities of interest, that is, neither are IID. Special interests include adjusting for the effects of latent variables, finding ways to bootstrap and cross-validate non-IID data, and making extensions to three-way and higher order data arrays. I also work on Monte Carlo methods.
- Jon Palma. Administrative role in Clinical Informatics/Analytics at Lucile Packard Children's Hospital. Research focus in clinical informatics includes optimization of commercial EMRs to support complex clinical workflows in newborn intensive care; clinical decision support; real-time clinical dashboards; electronic sign-out tools; and IT-supported patient/family communication. Analytics roles and research interests include support of LPCH's enterprise data warehouse; enabling reporting of business and clinical metrics; and generating new knowledge from clinical data.
- Vijay Pande. My research interests lie at the intersection of machine learning, Bayesian statistics, atomistic simulation, bioinformatics, and cheminformatics methods and its application to problems of linking drug efficacy and side effects to geneomics and systems biology. My group also has expertise in related synergistic areas, such as theoretical physical chemistry, structural biology, computer science, and large-scale distributed computing. By combining our methods with the Folding@home distributed computing project (currently the most powerful supercomputer in the world, with almost 10 petaflops of performace), we have a unique opportunity to push the state of the art in these and related areas. Finally, via collaborations with biotechs, pharmaceutical companies, and experimental groups interested in drug design, we can directly test our predictions, thus strengthening our methods as well as the direct impact of our results.
- Dmitri Petrov. Three main topics are studied in the lab: 1) mutation and evolution of global genomic properties, 2) evolution and population dynamic of transposable elements in eukaryotes, and 3) evolution and population dynamic of transposable elements in eukaryotes.
- Sylvia Plevritis. Research program focuses on computational modeling of cancer biology and cancer outcomes. We develop stochastic models of the natural history of cancer based on clinical research data. We predict population-level cancer outcomes under different screening and treatment interventions. We also analyze genomic and proteomic data in order to identify molecular networks that are perturbed in cancer initiation and progression and relate these perturbations to patient outcomes.
- Ingmar H. Riedel-Kruse . Our lab is focused at two topics: (1) Engineering (and programming) biological games and proving their utility for education and large scale science. (2) Quantitative and modeling approaches to decipher the biophysics and genetic network dynamics underlying vertebrate development and pattern formation - with a longer term interest in tissue engineering. We have a variety of rotation projects; and based on your specific interest you can use and learn a number of techniques, such as zebrafish, micro-fluidics, programming, theory, molecular and cell biology, imaging - or any combination thereof.
- Chiara Sabatti. My research focus is on developing statistical methods for the analysis of high throughput genomics data. Areas of particular interests at the moment are: association mapping of multiple related phenotypes, DNA copy number variant detection, analysis of rare variants in population isolates and reconstruction of gene regulatory networks.
- Arend Sidow. Current projects are in developmental genomics (mouse), gene regulation and chromatin function (mouse and human), cancer genomics (human), and inherited rare disorders (human).
- Gavin Sherlock. The Sherlock lab uses genomic approaches to shed light on biological systems, particularly employing high throughput sequencing and rigorous analyses of the resulting data. We are characterizing adaptive evolution at the molecular level to understand the adaptive landscape that yeast populations traverse when evolving under a selective pressure. Specifically, we want to know what are the mutational and fitness trajectories taken as a population explores the landscape. We have sequenced the genomes of many adaptive clones and identified the genes and pathways that are the targets of adaptive mutation under a particular selective pressure. We are currently sequencing DNA from entire populations, developing rigorous statistical models to identify low frequency mutations and distinguish them from sequence errors. In a second project, we are using high throughput sequencing to sequence the transcriptome of the human fungal pathogen, Candida albicans. Candida albicans is an obligate diploid, and in many ways, different alleles of the same gene can be thought of a paralogs, as they never go through a haploid phase where deleterious alleles would be exposed. This should result in a relaxed evolutionary constraint, and possible lead to allele specific transcription. We are focusing our efforts on understanding the RNA sequencing data to look for allele specific events, which requires significant bioinformatics expertise. We have recently phased SNPs in the diploid genome, which gives us greater power to detect such events. Finally, we are also looking at changes that occur in cancer. We have profiled DNA methylation changes in prostate cancer, and found that there are large scale genome wide difference between normal and tumor prostate tissue. We are currently working to understand the origins of these changes.
- Hua Tang. Genetic variation does not only underlie phenotypic diversity among individuals, but also documents the evolutionary history of a species. Research in our laboratory aims to uncover the evolutionary forces that have shaped the patterns of genetic variation in humans, to elucidate the genetics basis of complex traits, and to shed light on the mechanisms that lead to diverse phenotypes and disparate disease risk among populations. We approach these questions by developing statistical and computational approaches, by analyzing large-scale genomic data, and by collaborating with experts in a variety of fields.
- Robert Tibshirani. Research is in applied statistics and biostatistics. Our lab specializes in computer-intensive methods for regression and classification, bootstrap, cross-validation and statistical inference, and signal and image analysis for medical diagnosis.
- Terry Winograd. Research is on human-computer interaction design, with a focus on the theoretical background and conceptual models. He directs the teaching programs and HCI research in the Stanford Human-Computer Interaction Group. He is also a founding faculty member of the Hasso Plattner Institute of Design at Stanford (the "d.school") and on the faculty of the Center on Democracy, Development, and the Rule of Law (CDDRL)
- Lei Xing. Medical imaging informatics, image reconstruction, Image-guided intervention, CT, MRI and radionuclide imaging (PET/CT, SPECT/CT), intensity modulated radiation therapy (IMRT), treatment planning and plan optimization, image segmentation and deformable registration, tele-radiology/treatment planning, radiobiology modeling, biologically conformable radiation therapy (BCR), application of molecular imaging to radiation oncology.
Collaborating Faculty
- Euan Ashley. The Ashley lab is focused on the application of whole genome sequencing to the medical care of individuals and families. We lead the Stanford Center for Inherited Cardiovascular Disease, one of the few medical centers in the country where patient genome sequences can be readily incorporated into clinical care. In 2010, we led the team of BMI faculty that completed the first clinical interpretation of a human genome. We extended this to a pipeline that would handle families in 2011. We are also fascinated by network biology. Part of the Stanford heart transplant team, we are focused on understanding the heart’s response to disease or exercise stress. We are part of a team of three major transplant centers that was recently awarded $9m to explore the genetic control of cardiac transcriptional activity via RNA sequencing and network modeling. Finally, although many of our questions can be answered in silico, to establish causality, we turn to the wet lab to explore the biology of key genes and signaling modules.
- Catherine Blish. The Blish lab is focused on using a systems immunology approach to develop new methods to prevent and control infectious diseases. Our studies are highly translational in nature, bringing comprehensive immune profiling techniques such as mass cytometry to clinical and epidemiologic studies of HIV transmission and influenza vaccination. We are particularly interested in the role of NK cells in viral immunity, the etiology behind the susceptibility of pregnant women to viruses, and the impact of viral diversity and escape in the interplay between the virus and the host immune response.
- Stanley N. Cohen. The collection and interpretation of large amounts of data obtained from DNA and protein microarrays has become an important approach toward understanding the biological regulatory circuits that control gene expression. In the prevailing paradigm, clusters of genes that show common patterns of expression on microarrays are identified computationally and relationships among these genes are inferred by the experimenter in part by using his/her prior knowledge.
- Ronald Davis. Our lab is using Saccharomyces cerevisiae and Human to conduct whole genome analysis projects. The yeast genome sequence has approximately 6,000 genes. We have made a set of haploid and diploid strains (21,000) containing a complete deletion of each gene. In order to facilitate whole genome analysis each deletion is molecularly tagged with a unique 20-mer DNA sequence. This sequence acts as a molecular bar code and makes it easy to identify the presence of each deletion.
- Joshua E. Elias. Developing new mass spectrometry-based experimental and computational tools that advance the field of proteomics, and applying them to a variety of important biomedical paradigms, including cancer, aging, and stem cell biology.
- Will Greenleaf. My lab is interested in leveraging the power of high-throughput sequencing methods to 1) understand rare genomic and epi-genomic heterogeneity at the level of cellular subpopulations and even single cells 2) investigate chromatin structure at the level of the 30-nm fiber, and 3) probe the relationship between primary sequence and functionality of macromolecules (RNA and protein). All of these research endeavors lie at the intersection of physics, engineering, and biology, and require the analysis of large, novel data sets.
- Mark A. Hlatky. Main research work is in "outcomes research", especially examining the field of cardiovascular medicine. Particular areas of interest are the integration of economic and quality of life data into randomized clinical trials, evidence-based medicine, decision models, and cost-effectiveness analysis. I am also interested in the application of novel genetic, biomarker, and imaging tests to assess risk and guide clinical management of coronary artery disease.
- Hanlee Ji. Research group is focused on the genomic analysis of cancer with several general goals. 1) Developing novel, cost-effective genomic technologies involving next generation DNA sequencing for application in cancer mutation characterization and genomic biomarker discovery. My group is focused on integrating genomic analysis with clinical issues in oncology and integration into oncology clinical trials. 2) Developing computational analytical methods for next generation sequencing and application in genomic diagnostic technologies. 3) Applying functional comparative genomic approaches to identify genes involved in genomic instability in colorectal carcinoma and understanding these genes role in biological networks
- Peter Karp. Dr. Karp's research interests include metabolic-pathway bioinformatics, biological databases and ontologies, genome annotation, and metabolic engineering. He is the bioinformatics architect of the BioCyc collection of pathway/genome databases that includes EcoCyc, HumanCyc, and MetaCyc. EcoCyc is a pathway/genome database for E. coli that integrates information about its full metabolic-pathway complement and its genome. HumanCyc contains a computational prediction of the metabolic pathways of humans. MetaCyc is a multi-species metabolic-pathway database. He has developed algorithms for prediction of metabolic pathways from sequenced genomes, for visualization of individual pathways and entire metabolic maps, and for generation of metabolic models from pathway databases. He is interested in extending these modeling techniques from individual organisms to communities of interacting microorganisms. He has also worked in the area of bioinformatics database integration, and has developed BioWarehouse, a system for building relational database warehouses that integrate multiple bioinformatics databases.
- Karla Kirkegaard. Lab investigates the cell biology, genetics and biochemistry of RNA viral propagation, using poliovirus as a model system. For many subcellular viruses and parasites, RNA, not DNA, is the carrier of genetic information. Poliovirus serves as a model to increase our understanding of positive-strand RNA viruses for which no vaccine is available and which remain a significant health hazard: examples include other picornaviruses, such as rhinoviruses, coxsackieviruses and the deadly enterovirus 71, as well as more distantly related positive-strand RNA viruses such as hepatitis C and Dengue fever.
- Thomas Krummel.Surgical Innovation, Simulation and Virtual Reality in Surgical Education, Fetal Healing-Cellular and Biochemical Mechanisms.
- Jin Billy Li. The landscape of RNA editing in the transcriptomes The main interest of Jin Billy Li's lab is to identify and interpret the RNA editing sites using a variety of approaches including genomics, technology development, and computational biology. RNA editing is a phenomenon where genomically encoded information is changed in the RNA. Adenosine-to-Inosine (A-to-I) editing is the most common type of editing, and is achieved by enzymes called Adenosine deaminase acting on RNA (ADAR). RNA editing is critical because ADAR knockout mice die before or shortly after birth. Despite the fact that RNA editing was first discovered over twenty years ago, it has been surprisingly under appreciated and under explored. Very few RNA editing sites had been discovered in humans, mainly due to technological barriers. We recently expanded the RNA "editome" to about 400 sites by computational prediction followed by targeted next generation sequencing (Li et al., Science 2009, 324:1210-1213). This, however, is probably just tip of the iceberg. Our lab will continue the discovery of the RNA editing sites in the transcriptomes of human and may model organisms, as well as various disorders such as autism and cancers. Our main approach is next generation sequencing and computational data analysis. Bioinformatics skills are also needed in a genome-wide association study to link genetic variations with the RNA editing level of a nearby editing site. In a longer term, we aim to perform functional genomic screening of these newly identified RNA editing sites.
- Vinod Menon. Experimental and theoretical systems neuroscience: Cognitive neuroscience; Cognitive development; Psychiatric neuroscience; Functional brain imaging; Dynamical basis of brain function; Nonlinear dynamics of neural systems .
- Garry Nolan. Dr. Nolan’s laboratory focuses on the analysis of biological events at the single cell level using novel genetic and FACS-based approaches at the intersection of immunology, autoimmunity, biochemistry, and cancer. The laboratory studies phospho-protein immune cell and cancer signaling, and other metabolic parameters by analysis of biochemical functions at the single cell level in primary cell populations. This includes interrogation of cancer (Cell, 2004) and immune signaling networks in complex cell populations (Science, 2005), drug screening approaches (Nature Methods, 2005, (cover article), Nature Chemistry and Biology, 2007a, Nature Chemistry and Biology, 2007b (cover article)) and using multiparameter data to stratify signaling maps from patient samples, (Cancer Cell, 2008, cover article). Other major interest areas of the laboratory include mapping of signaling networks within complex populations of immune cells, developing systems biology approaches to develop an atlas of immune cell differentiation (Nature Biotechnology, In Press, 2011), the development of mechanism-based diagnostics for use in clinical trial studies. The data generated at the single cell level ranges from 10-15 parameters per cell (hundreds of thousands of cells per sample, and dozens of samples per experiment) to up to 50-100 parameters per cell using a new mass spectrometer flow cytometer we have co-developing and recently published upon (Bendall et al, Science, 2011). To analyze these datasets and infer signaling networks within each cell subpopulation, we have developed novel hardware (Field Programmable Gate Arrays and GPUs tethered to standard CPUs with novel compiler/distributor architecture) to implement the more computationally intensive algorithms we are using for our Bayesian inference and other bioinformatics approaches. The combination of hardware/software/biology applied in the laboratory to clinical samples sits at the edge of the translational arena in that we focus on developing techniques that can provide mechanistically relevant answers to clinicians while simultaneously helping biologist answer questions of basic importance to biology.
- Ross D. Schacter. Dr. Schacter's early work developed a method for purchasing an expert's forecast that encourages accurate revelation of the expert's beliefs as probabilities. His interest in medical decision analysis led to joint work on scheduling patients for follow-up bladder cancer therapy. In recent years, his research has focused on the representation, manipulation, and analysis of uncertainty and probabilistic reasoning in decision systems. As part of this work, he developed the DAVID influence diagram processing system for the Macintosh. He has worked closely with many students in Bioinformatics, where he holds a courtesy appointment.
- Robert Shafer. Research is on the mechanisms and consequences of HIV evolution with an emphasis on HIV drug resistance. Maintains an online database (HIV Drug Resistance Database ) designed to provide a publicly available resource for those performing HIV drug resistance surveillance, interpreting HIV drug resistance tests, and developing new antiretroviral drugs.
- Michael P. Snyder. Snyder laboratory the first to perform a large-scale functional genomics project in any organism, and currently carries out a variety of projects in the areas of genomics and proteomics both in yeast and humans. These include the large-scale analysis of proteins using protein microarrays and the global mapping of the binding sites of chromosomal proteins. His laboratory built the first proteome chip for any organism and the first high resolution tiling array for the entire human genome.
- Julie Theriot. Research concentrates on interactions between infectious bacteria and the human host cell actin cytoskeleton. Listeria monocytogenes and Shigella flexneri are unrelated food-borne bacterial pathogens that share a common mechanism of invasion and actin-dependent intercellular spread in epithelial cells. Our studies fall into three broad areas: the biochemical basis of actin-based motility by these bacteria, the biophysical mechanism of force generation, and the evolutionary origin of pathogenesis.
- Samson Tu. Modeling of biomedical ontologies and clinical guidelines and protocols, development of knowledge-based systems, knowledge representation, databases, temporal database and temporal reasoning, protocol-based health care.
- Paul (PJ) Utz. Research goal is to develop a better understanding of the pathogenic mechanisms underlying systemic lupus erythematosus (SLE) and other autoimmune diseases by exploring signaling pathways in blood cells, autoantibody production by B lymphocytes, and novel therapeutics targeting these and related pathways.
- Michael G. Walker. Research interests include the genetics of disease, intelligence, and aging. He also provides statistics consulting to biotechnology and medical device companies and consults to venture capital companies evaluating investments in these areas.
