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Mosquito-borne viruses are the cause of diseases such as yellow fever, West Nile encephalitis, and dengue hemorrhagic fever, with millions of people affected worldwide each year. While there are vast numbers of diverse mosquito species throughout the world, only a few select species are responsible for the majority of this continued transmission. Research in my lab focuses on understanding the genetic basis as to why these few mosquito species are such good vectors of viral pathogens, and on using that knowledge to prevent transmission of these disease-causing viruses. Research projects include the identification of genes involved in antiviral defense pathways, the development of genetic drive systems that can increase the frequency of pathogen-resistance genes in mosquito populations; next-generation sequencing technologies and bioinformatics; and the development of novel genetic tools for the mosquito Aedes aegypti.

For more detailed information on my research program, here is a link to some recent publications:

http://www.ncbi.nlm.nih.gov/pubmed?term=adelman z

The Network Dynamics and Simulation Science Laboratory designs, develops and implements simulation tools to understand large biological, information, social, and technological systems. For many reasons, which range from practical difficulty to the possibility of great harm, simulations are a uniquely capable medium in which representation and analysis can be performed. The need for simulations is derived from questions posed by scientists, policy makers, and planners involved with very large complex systems. Extremely detailed, multi-scale computer simulations allow formal and experimental investigation of large-scale systems. By enabling individuals to explore the potential impact of different interventions or strategies on the course of a disease outbreak or a specific transportation scenario, for example, important information can be prioritized as to the potential merits of different interventions. The Network Dynamics and Simulation Science Laboratory is currently pursuing projects in the following programmatic areas: integrated high-performance simulation and data service architectures; human population dynamics and associated social networks in urban environments and at the national scale; epidemiology and the spread of infectious diseases; computational and behavioral economics and commodity markets; next generation computing and telecommunication systems; and computational systems biology. The group has developed Simfrastructure, a service- and grid computing-oriented modeling tool for socio-technical, biological, and information systems and Simdemics, a scalable high-performance computing-based service environment for general reaction diffusion systems. Other recent milestones include the successful development of scalable algorithms for simulating epidemics and other reaction diffusion systems. A synthetic population has been created consisting of 300 million individuals endowed with daily activity patterns where the activities are performed at real locations. The EpiSims tool is being used by the National Institutes of Health Models of Infectious Disease Agent Study to support preparedness for potential disease pandemics.

Modeling and simulation of molecular networks. During the last decade finite dynamical systems, that is, discrete dynamical systems with a finite phase space, have been used increasingly in systems biology to model a variety of biochemical networks, such as metabolic, gene regulatory and signal transduction networks. In many cases, the available data quantity and quality and not sufficient to build detailed quantitative models such as systems of ordinary differential equations, which require many parameters that are frequently unknown. In addition, discrete models tend to be more intuitive and more easily accessible to life scientists. Boolean networks and their generalization, the so-called multistates logical models, Petri nets, and agent-based models are the main types of finite dynamical systems that have been used in this context. Discrete models require less detailed information about the system to be modeled, so they can be used in cases where quantitative information is missing. They are also useful if qualitative predictions from the model are desired, such as whether a T cell becomes pro- or anti-inflammatory. Finally, discrete models are very intuitive compared to models based on differential equations or other more sophisticated formalisms. It is also easier to explore their dynamics, at least for reasonably small models. On the other hand, an important disadvantage of discrete models is that there are very few theoretical tools available for their analysis. Typically, discrete models are built by translating information from the literature into logical statements about the interactions of the different molecular species involved in the network. In many cases, the biological information about a particular network node might not be sufficient, however, to construct a logical function governing regulation. In the case of a continuous model, the remedy would be to insert a differential equation of specified form, e.g., mass action kinetics, with unspecified parameters. If experimental time course data are available one can then use one of several parameter estimation methods to determine those unspecified model parameters so that the model fits the given data. Data fit is determined by model simulation, using numerical integration of the equations in the model. We have developed a software package, Polynome , which carries out discrete analogs of parameter estimation for Boolean networks, a particular kind of discrete model. The software integrates several packages for network inference we have developed in recent years. In the case of missing information about a particular node in the network to be modeled, one can insert a general Boolean function, maybe of a specified type, e.g., a nested canalyzing function. If experimental time course data for the network is available, then one can use one of several existing inference methods to estimate a function that will result in a model that fits the data. This function in addition satisfies a specified optimality criterion, similar to the optimality criterion for the fitting of continuous parameters. It furthermore couples parameter estimation with extensive simulation capabilities. Cancer systems biology. There is increasing evidence that differences in iron metabolism play an important role in cancer risk, survival, and clinical prognosis. However, to date, why these differences in iron metabolism exist, how they contribute to malignant processes, and whether they can be successfully exploited to therapeutic advantage remains unknown. This is because the current approach to identifying whether these or other of the proteins in the complex network of iron metabolism should be targeted therapeutically is an empiric, protein-by-protein validation exercise. The intent of this project is to use the power of systems biology to identify the key regulatory points in this complex pathway, and how they change in malignancy. In work published in 2009, our group described the iron metabolic network in normal cells, including subnetworks for different cell types. We distilled this complex network into a system of feedback control loops that represent important points of potential control of the network. The goal of this project is to build on this network to create predictive models of iron metabolism in normal and cancer cells. This will help identify key nodal points in iron metabolism and how they change as cells progress to malignancy. Ultimately, we hope to be able to leverage knowledge gained to therapeutic or diagnostic advantage.
 

Research program in the Michalak laboratory focuses on comparative genomics and evolution. It employs an integrative approach with a variety of molecular and computational techniques to rigorous understanding of how genes produce biological diversity. Recent advances in genomic technologies and bioinformatics, including ‘next-gen’ sequencing of entire genomes and transcriptomes, provide a unique opportunity to investigate how morphological complexity elaborates from genomes through gene regulatory networks. Michalak is particularly interested in genome responses to such dramatic extrinsic and intrinsic challenges as interspecies hybridization and speciation, genome duplications (poliploidization), genomic conflicts, and cancer.

The Network Dynamics and Simulation Science Laboratory designs, develops and implements simulation tools to understand large biological, information, social, and technological systems. For many reasons, which range from practical difficulty to the possibility of great harm, simulations are a uniquely capable medium in which representation and analysis can be performed. The need for simulations is derived from questions posed by scientists, policy makers, and planners involved with very large complex systems. Extremely detailed, multi-scale computer simulations allow formal and experimental investigation of large-scale systems. By enabling individuals to explore the potential impact of different interventions or strategies on the course of a disease outbreak or a specific transportation scenario, for example, important information can be prioritized as to the potential merits of different interventions. The Network Dynamics and Simulation Science Laboratory is currently pursuing projects in the following programmatic areas: integrated high-performance simulation and data service architectures; human population dynamics and associated social networks in urban environments and at the national scale; epidemiology and the spread of infectious diseases; computational and behavioral economics and commodity markets; next generation computing and telecommunication systems; and computational systems biology. The group has developed Simfrastructure, a service- and grid computing-oriented modeling tool for socio-technical, biological, and information systems and Simdemics, a scalable high-performance computing-based service environment for general reaction diffusion systems. Other recent milestones include the successful development of scalable algorithms for simulating epidemics and other reaction diffusion systems. A synthetic population has been created consisting of 300 million individuals endowed with daily activity patterns where the activities are performed at real locations. The EpiSims tool is being used by the National Institutes of Health Models of Infectious Disease Agent Study to support preparedness for potential disease pandemics.

Currently, I am working on genomics projects related to alternative splicing, indels, hidden Markov models, genome compression, and genome IDs.  I am also interested in complex networks, especially biological (e.g., protein-protein interaction) networks.  I am looking for students to work on a project involving the topological and semantic comparison of biological networks.

My life science collaborators tend to be plant scientists, who work on stress, metabolism, seed development, and pathogen genomics.  One of my current projects is Beacon, which is a bioinformatics system we are building to capture, represent, and analyze signal transduction pathways in plants.  As part of this system, there is a drawing platform, a database of pathways, a simulation engine, and an inferrence engine.

Arthropod-borne viruses (arboviruses) continue to cause morbidity and mortality in humans and domesticated animals. My program seeks to develop new methods for the prevention, diagnosis, treatment, and management of arboviral diseases. Research in my laboratory is being conducted at the confluence of several different areas of study that include molecular virology, the mechanisms and biology of RNA silencing, next-generation sequencing, and bioinformatics. The underlying focus of this interdisciplinary approach is on understanding invertebrate antiviral immune responses.

The Nutritional Immunology and Molecular Medicine Laboratory (NIMML, www.nimml.org) conducts translational research aimed at developing novel therapeutic and prophylactic approaches for modulating immune and inflammatory responses. The Laboratory combines computational modeling, bioinformatics approaches, immunology experimentation, and pre-clinical and clinical studies to better understand the mechanisms of immune regulation at mucosal surfaces and ultimately accelerate the development of novel treatments for inflammatory, infectious, immune-mediated and chronic inflammatory diseases. The NIMML is co-led by Drs. Bassaganya-Riera and Hontecillas and its research program has received support from the National Center for Complementary and Alternative Medicine, the National Institute of Allergy and Infectious Disease at the National Institutes of Health, Bristol Myers Squibb, Cognis Nutrition and Health GmbH, Lipid Nutrition, and commodity groups. Focus Areas • Identifying novel, naturally occurring and safer anti-inflammatory compounds and analyzing the relation between therapeutic targets and disease-gene products • Investigating the mechanisms of immune modulation by pharmacological regulators of ligand-activated transcription factors • Characterizing the mechanisms of obesity-related inflammation at the cellular and molecular levels and discovering novel therapies for uncoupling obesity from its co-morbidities (i.e., diabetes, heart disease, stroke) • Developing safer therapies against gut immunopathologies such as inflammatory bowel disease and colon cancer • Characterizing mucosal immune responses to gut and respiratory pathogens for accelerating the development of mucosal vaccines and broad-based immune therapeutics • Studying host-microbiota interactions • Modeling immune responses to enteric and respiratory pathogens • Apply high-performance computing to large-scale high-throughput immunological data analyses (miRNA, transcriptomic sequencing, large-scale proteomics, Chip-Seq). Center for Modeling Immunity to Enteric Pathogens (MIEP) The MIEP is one of four NIAID-funded National Centers in Modeling Immunity for Biodefense. The MIEP is a collaborative $10.6M program Directed by Dr. Bassaganya-Riera with the mission of understanding the mechanisms of action underlying immune responses to gut pathogens. To achieve this goal MIEP will develop and disseminate new and improved computational and mathematical models of the mucosal immune system to generate new knowledge from immunology and infectious disease datasets. To engage the immunological research community, MIEP will provide a user-friendly web-based immunological experimentation environment that incorporates the models, is programmatically tied to the Immunology Database and Analyses Portal (ImmPort), and offer traning on modeling mucosal immunity. See press release for additional details https://www.vbi.vt.edu/public_relations/press_releases/vbi_awarded_10_million_from_nih_to_model_immune_responses_to_gut_pathogens Nutritional Immunology NIMM research characterized the modulatory effects of conjugated linoleic acid (CLA) on inflammation and immunity. This research was recognized by the American Society of Nutritional Sciences from which Dr. Bassaganya-Riera was awarded the 2005 Bio-Serv Award. Dr. Bassaganya-Riera was awarded US$ 263,439 by Mead Johnson Nutritionals-Bristol Myers Squibb for the project entitled "Assessment of a docosahexaenoic acid (DHA) and arachidonic acid (ARA)-enriched infant formula on immune responses in neonatal piglets." Dr. Bassaganya-Riera and colleagues found that when DHA and ARA are fed in combination (0.63/0.34%) to neonatal piglets they elicit significant immunosuppressive effects. These results raise questions about the safety of adding these two fatty acids to infant formulas. In 2007, NIMM Research group was awarded a $1.2 million RO1 grant by the National Center for Complementary and Alternative Medicine designed to determine whether the phytohormone abscisic acid (ABA) elicits its immunomodulatory actions through peroxisome proliferator-activated receptor (PPAR) gamma-dependent or gamma-independent mechanism(s). The group has elucidated the mechanism of action underlying the immunoregulatory actions of ABA and discovered a novel class of ABA-like compounds that can be develop as therapeutics for inflammatory diseases. Gastrointestinal health Peroxisome proliferator-activated receptors (PPARs) are novel members of the nuclear receptor superfamily with three isoforms (alpha, gamma, and delta) that translate nutritional or pharmacologic stimuli into changes in gene expression, including a down-regulation of cytokine and chemokine expression. The two clinical manifestations of inflammatory bowel disease (IBD) -- Crohn's disease (CD) and ulcerative colitis (UC) -- afflict over 1 million people in North America and 4 million people worldwide. Current treatments against IBD have improved, but they are modestly successful for the long-term management of the disease and result in significant side effects. Dr. Bassaganya-Riera first reported the efficacy of CLA in ameliorating gut inflammation in a pig model and proposed that some of the beneficial effects of CLA on mucosal immune responses could be mediated by epithelial and immune cell PPAR gamma. Recent results fulfilled the prediction of the group's previous hypothesis, as the deletion of the PPAR gamma gene in immune and epithelial cells abrogated CLA's anti-inflammatory actions in the gut. Dr. Bassaganya-Riera and colleagues also found that PPAR gamma is required for Treg's anti-inflammatory function. Consistent with the concept from bench to bedside, Dr. Bassaganya-Riera is translating the basic scientific understanding of the role of PPAR gamma in the pathophysiology of gut inflammation into the clinic through extramurally funded research collaborations with University of North Carolina at Chapel Hill. Our group also led the efforts to sequence the whole genome of an Amerindian strain of H. pylori. These ongoing efforts will not be combined with MIEP. https://www.vbi.vt.edu/public_relations/press_releases/helicobacter_pylori_genome_sequence Type 2 diabetes and obesity According to recent estimates from the Centers for Disease Control and Prevention, 30% of the United States population is obese and 65% is overweight. One of the major consequences of these high rates is manifested by the increased prevalence of type 2 diabetes mellitus (T2D), a chronic disease characterized by systemic insulin resistance and inflammation. In 2004, it was estimated that 20.8 million Americans had T2D and over 40.1% of middle-aged adults have prediabetes, with healthcare costs for diabetes treatment over US$ 132 billion and US$ 394 billion for treating cardiovascular disease and stroke (Center for Disease Control and Prevention). Current antidiabetic drugs elicit important insulin-sensitizing and anti-inflammatory effects, but side effects associated with using these medications are significant. Dr. Bassaganya-Riera's laboratory is actively screening and discovering novel, naturally occurring, orally active nutraceuticals against diabetes and cardiovascular disease that activate nuclear receptors. Of note is the discovery of a PPAR gamma-activating and anti-inflammatory phytohormone, abscisic acid (ABA), which is also a potent antidiabetic agent. Following an in vitro screening of its PPAR gamma agonistic activity, Dr. Bassaganya-Riera and coworkers generated molecular evidence in vivo showing that ABA improves insulin sensitivity and obesity-related inflammation by inhibiting monocyte chemoattractant-1 (MCP-1) expression and macrophage infiltration through a PPAR gamma-dependent mechanism. They plan to use ABA as a proof-of-concept to establish a solid innovation pipeline of immune modulatory compounds for chronic disease prevention. Drs Bassaganya-Riera, Hontecillas and Guri are co-inventors in a Patent (US 7,741,367) with claims protecting the anti-diabetic actions of ABA. The NIMM is pursuing commercilization of this class of compounds through BioTherapeutics https://www.vbi.vt.edu/public_relations/press_releases/biotherapeutics_inc_launched NIMML Team Members Asturias, Salvador Visiting Student ACDIL (540) 231-1209 svento@vbi.vt.edu Bassaganya-Riera, Josep, Ph.D. Associate Professor, Virginia Bioinformatics Institute; Associate Professor, Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences; Director, Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute ACDIL (540) 231-7421 jbassaga@vbi.vt.edu Carbo, Adria Graduate Research Assistant, NIMML ACDIL (540) 231-1209 acarbo@vbi.vt.edu Hontecillas-Magarzo, Raquel, PhD Assistant Professor, Virginia Bioinformatics Institute ACDIL (540) 231-7276 rmagarzo@vbi.vt.edu Kronsteiner-Dobramysl, Barbara Postdoctoral Associate ACDIL bkrondo@vbi.vt.edu Lewis, Nikki Graduate Research Assistant, NIMML ACDIL lewissn@vbi.vt.edu Lu, Pinyi Graduate Research Assistant ACDIL (540) 231-3052 lyp0526@vbi.vt.edu Marin, Mireia Graduate Research Assisant, NIMML ACDIL (540) 231-1209 mireia88@vbi.vt.edu Mei, Yongguo Research Software Engineer ACDIL 231-1705 ymei@vbi.vt.edu Nguyen, Matthew USP Student ACDIL (540) 231-1209 mattn5@vbi.vt.edu Pujol, Monica Graduate Research Assistant, NIMML ACDIL (540) 231-1209 monica@vbi.vt.edu Walke, James, MFA Program Coordinator ACDIL (540) 231-3014 jwalke@vbi.vt.edu Washington, Cassandra Undergraduate Lab Assistant ACDIL (540) 231-1209 cwash@vbi.vt.edu

The Synthetic Biology Group at VBI streamlines the design and fabrication of artificial gene networks. Computer-assisted design of genetic systems is poised to bring significant benefits to the biomedical community and the biotechnology industry. However, the lack of calibrated genetic parts remains a major limitation. The Synthetic Biology Group develops software, computational tools and high throughput imaging systems that allow researchers to take full advantage of calibrated genetic components and the potential of synthetic biology.

Dr. Hoeschele has substantial experience in the basic statistical design and analysis of ‘omics experiments (genomics, transcriptomics, proteomics, epigenomics), in linkage and association mapping of quantitative trait loci (including epistasis and copy number variation), and in the design and analysis of Genetical Systems Biology experiments and studies Dr. Hoeschele's current statistical methodology research focuses on high-dimensional QTL mapping, multi-omics data integration and causal network inference in Genetical Systems Biology. Dr. Hoeschele's current collaborative research focuses on the epigenomics, in combination with transcriptomics and DNA variation, of atherosclerosis, in collaboration with Dr. Yongmei Liu of Wake Forest University. Most recently, she has become involved in a genomics approach to study brain cancer in dogs as a model for humans in collaboration with Dr. John Robertson of the VA-MD Regional College of Veterinary Medicine.

The Network Dynamics and Simulation Science Laboratory designs, develops and implements simulation tools to understand large biological, information, social, and technological systems. For many reasons, which range from practical difficulty to the possibility of great harm, simulations are a uniquely capable medium in which representation and analysis can be performed. The need for simulations is derived from questions posed by scientists, policy makers, and planners involved with very large complex systems. Extremely detailed, multi-scale computer simulations allow formal and experimental investigation of large-scale systems. By enabling individuals to explore the potential impact of different interventions or strategies on the course of a disease outbreak or a specific transportation scenario, for example, important information can be prioritized as to the potential merits of different interventions. The Network Dynamics and Simulation Science Laboratory is currently pursuing projects in the following programmatic areas: integrated high-performance simulation and data service architectures; human population dynamics and associated social networks in urban environments and at the national scale; epidemiology and the spread of infectious diseases; computational and behavioral economics and commodity markets; next generation computing and telecommunication systems; and computational systems biology. The group has developed Simfrastructure, a service- and grid computing-oriented modeling tool for socio-technical, biological, and information systems and Simdemics, a scalable high-performance computing-based service environment for general reaction diffusion systems. Other recent milestones include the successful development of scalable algorithms for simulating epidemics and other reaction diffusion systems. A synthetic population has been created consisting of 300 million individuals endowed with daily activity patterns where the activities are performed at real locations. The EpiSims tool is being used by the National Institutes of Health Models of Infectious Disease Agent Study to support preparedness for potential disease pandemics.

Evolutionary change, whether in populations of organisms or malignant tumor cells, is contingent on the availability of inherited variation for natural selection to act upon. Our lab explores the molecular basis for this variation using a combination of traditional biochemistry, cell biology, and genetic techniques; coupled with population genetics simulations, genomics, and other computational methods. We are particularly interested in high frequency heritable variations, genetic and epigenetic in nature, that can facilitate adaption to rapidly changing environments, and in other instances, underlie clinical disorders such as cancer. The translational component of our research program seeks to exploit pathways that modulate DNA repair to engineer directed changes to the genome for research and therapeutic benefit.

The Network Dynamics and Simulation Science Laboratory designs, develops and implements simulation tools to understand large biological, information, social, and technological systems. For many reasons, which range from practical difficulty to the possibility of great harm, simulations are a uniquely capable medium in which representation and analysis can be performed. The need for simulations is derived from questions posed by scientists, policy makers, and planners involved with very large complex systems. Extremely detailed, multi-scale computer simulations allow formal and experimental investigation of large-scale systems. By enabling individuals to explore the potential impact of different interventions or strategies on the course of a disease outbreak or a specific transportation scenario, for example, important information can be prioritized as to the potential merits of different interventions. The Network Dynamics and Simulation Science Laboratory is currently pursuing projects in the following programmatic areas: integrated high-performance simulation and data service architectures; human population dynamics and associated social networks in urban environments and at the national scale; epidemiology and the spread of infectious diseases; computational and behavioral economics and commodity markets; next generation computing and telecommunication systems; and computational systems biology. The group has developed Simfrastructure, a service- and grid computing-oriented modeling tool for socio-technical, biological, and information systems and Simdemics, a scalable high-performance computing-based service environment for general reaction diffusion systems. Other recent milestones include the successful development of scalable algorithms for simulating epidemics and other reaction diffusion systems. A synthetic population has been created consisting of 300 million individuals endowed with daily activity patterns where the activities are performed at real locations. The EpiSims tool is being used by the National Institutes of Health Models of Infectious Disease Agent Study to support preparedness for potential disease pandemics.

Cyberinfrastructure group overview The approach used for research in the Cyberinfrastructure (CI) Division is transdisciplinary, uniting diverse initiatives to address some of key challenges in the biomedical, environmental and agricultural sciences. Cyberinfrastructure, which underpins infectious disease research in Dr. Sobral¿s group, refers to new research environments that support advanced data acquisition, storage, management, integration, mining, visualization and other computing and information processing services via computing infrastructure. As such, cyberinfrastructure is a technological solution to the problem of efficiently connecting data, computers, and people with the goal of generating new scientific theories and knowledge. The bioinformatics resources developed by the CI Group include tools for the curation of the genomes and pathogen systems of a wide range of infectious organisms, database systems for acquiring, storing, and disseminating high-throughput data generated from the study of pathosystems biology, and software systems for analysis and visualization of the data. Integrated into this effort are education and outreach activities that include the training of current and future generations of scientists as well as collaborative research activities. Some of the projects of the CI Group include: o PATRIC (patricbrc.org) o Pathogen Portal (pathogenportal.org) o Mid-Atlantic Regional Center of Excellence in Biodefense and Emerging Infectious Disease (marce.vbi.vt.edu) Some of the resources offered by the CI Division include: o Comprehensive Genome Curation and Annotation Infrastructure and Analysis Servers o Document information systems that integrate and disseminate published information on pathogens in a machine-readable format o Database systems, web visualization and bioinformatic tools for microarray and proteomic applications o Text mining focused on deep semantic parsing o Portals leveraging distributed resources through programmatic access o Usability Engineering of Interfaces o A Molecular Genetics Laboratory geared toward validation of informatics-based predictions o The Nutritional Immunology Group led by Dr. Bassaganya-Riera

Dr. Biswarup Mukhopadhyay's research group studies the biochemical mechanisms used by microorganisms to survive under extreme conditions. Research in the laboratory focuses on the physiology, enzymology and evolutionary biology of methanogenic archaea, mycobacterial metabolism and tuberculosis, and microbiological conversion of coal to methane in deep subsurface coalbeds. Methanocaldococcus jannaschii is a methane-producing organism found in submarine hydrothermal vents. The Mukhopadhyay research group has discovered that M. jannaschii possesses a new type of enzyme, a sulfite reductase, which represents an ancient detoxification system. This is the first time methane production and sulfite reduction have been shown not to be mutually exclusive processes. M. jannaschii can carry out both processes because it contains a system to detoxify the otherwise toxic sulfite. It is possible that at one time methanogenesis and sulfate reduction existed in one organism, which performed both methanogenesis and sulfate-dependent anaerobic oxidation of methane. Mycobacterial metabolism and tuberculosis: Coenzyme F420, a deazaflavin that is present in every methanogenic archaea and rare in bacteria, is found in every mycobacteria. Purwantini and Mukhopadhyay are investigating the role of this coenzyme in mycobacteria. Purwantini and colleagues have shown that the mycobacteria carry an unusual glucose-6-phosphate dehydrogenase (G6PD) that reduces F420 to F420H2; previously known G6PDs utilize NAD or NADP and not F420. Recently, Purwantini and Mukhopadhyay have shown that the F420H2 generated by the unusual G6PD (called Fgd) could help Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), to combat nitrosative attack by the macrophages and thereby to cause tuberculosis. Further focus is on elucidating the role of F420 in the synthesis of mycobacterial cell and the use this information to identify cellular targets for the development of therapeutics for TB. Also, in collaboration with the Johns Hopkins University, Rotinsulu Pulmonary Hospital (Bandung, Indonesia), and Institut Teknolgi (Bandung, Indonesia) they are analyzing the genomic and immunological characteristics of clinical isolates of M. tuberculosis with the goal of developing diagnostics, vaccines and therapeutics for tuberculosis. The Mukhopadhyay research group is also exploring the use of microorganisms to convert coal to methane and to reduce the risk of methane-induced mine explosion. This work is being carried out in collaboration with the Altuda Energy Corporation in San Antonio, Texas. Studies of human phosphoenolpyruvate carboxykinase and an archaeal-type phosphoenolpyruvate carboxylase are allowing new avenues to be explored for designing drugs for treating type 2 diabetes and infections caused by Clostridium perfringens as well as improving CO2 fixation efficiencies in plants. Current laboratory members: Name Position / Title Contact Information Johnson, Eric F. Microbial Technologist ejohnson@vbi.vt.edu (540) 231-1274 (office); (540) 231-1219 (lab) Purwantini, Endang, PhD Collaborator, Biochemical Pathways Scientist epurwant@vt.edu (540) 231-4395 (office); (540) 231-1219 (lab) Dharmarajan, Lakshmi PhD Student, GBCB laxo82@vbi.vt.edu (540) 231-2993 (office); (540) 231-1219 (lab) Susanti, Dwi PhD Student, GBCB dsusanti@vbi.vt.edu (540) 231-2993 (office); (540) 231-1219 (lab) Loganathan, Usha Laboratory Technician Trainee urlogan@vbi.vt.edu (540) 231-2993 (office); (540) 231-1219 (lab) Rodriguez, Jason PhD Student, Biochemistry/Microbiology, Virginia Tech jrodri@vt.edu (540) 231-3661 (office); (540)231-1219 (lab) Martin, Lindsay Undergraduate Research Student, Biochemistry (540) 231-1219 (lab) Piedl, Karla Undergraduate Research Student, Biochemistry (540) 231-1219 (lab) Dayton, Taylor Undergraduate Research Student, Biochemistry (540) 231-1219 (lab) Cecil, Michael Undergraduate Research Student, Biochemistry (540) 231-1219 (lab) Bleull, Samantha Undergraduate Research Student, Biochemistry (540) 231-1219 (lab) Tu, Emilee High School Research Student, Blacksburg High School (540) 231-1219 (lab) Past laboratory members: Name Position / Title Current location Boswell, Kristin L. Biochemistry Undergraduate Research Student Biochemistry, Post-Doctoral Associate, University of Wisconsin at Madison Case, Christopher Undergraduate Research Student, Biochemistry (2003-2005); Enzymologist and Microbial Physiologist (2005-2006) Biological and Biomedical Sciences, PhD Student, Yale University Cephas, Ryan HHMI Summer Research Student Oakwood University Colton, Deanna L. Undergraduate Research Student, Biology (2004-2007) PhD Student, Microbiology, University of Georgia Criss, Caitlyn L. Undergraduate Research Student, Biochemistry (2005-2006) Undergraduate Student, Biochemistry, Virginia Tech Dalton, Justin B. Biochemistry Undergraduate Research Student (2002-2003) Medical Student, West Virginia University Downs, Jennifer Medical Research Student Virginia Commonwealth University Fanning, Sean Undergraduate Research Student, Biochemistry - Glasson, Hannah Blacksburg High School Research Student (2004-2006) Undergraduate Student, Harvard University Haynie, Kimberly R. Undergraduate Research Student, Biology (2004-2005) PhD Student, Human Nutrition, Food and Excercise, Virginia Tech Hicks, Jeremiah MAOP Summer Research Student Undergraduate Student, University of Maryland (Eastern Shore) Hoffman, Ashley M. Biology Undergraduate Research Student (2004-06) Charlotte, NC Kale, Shiv D. Undergraduate Research Student, Biochemistry (2004-2006) Genetics, Bioinformatics and Computational Biology, PhD Student, Virginia Tech Kohnke, Philip Undergraduate Student Texas A and M University Kraszewski, Jessica Undergraduate Research Student, Biochemistry (2002-2003); Biochemist, DuPont Central Research Lahar, Nitu Undergraduate Research Student, Biochemistry Virginia Tech Lai, Haufang (Lilly) Senior Research Specialist (2002-2004) Senior Research Specialist, Bio-Design Institute, Arizona State University Le Gall, Claire Undergraduate Research Student, Biochemistry (2003-2004) France Nebus, Bernadette Undergraduate Research Student, Biology (2003) - Pagano, Allison Undergraduate Research Student, Biochemistry Undergraduate Research Student, Virginia Tech Perevalova, Anna American Society of Microbiology International Fellow Winogradsky Institute of Microbiology, Russian Academy of Science Piedl, Karla Research Student, Blacksburg High School Undergraduate Research Student, Virginia Tech Pochyla, Margaret I. Undergraduate Research Student, Biology (2002-2004) - Rolfe, Bradley Undergraduate Research Student, Biochemistry Virginia Tech Shifflett, Ashley Administrative Assistant (2004-2006) New Haven, CT Smith, Matthew Phillip Undergraduate Student, Dept. Mathematics Virginia Tech Solberg, Robert Undergraduate Research Student, Biology (2004-2006) - Stieber, Jennifer P. Undergraduate Research Student, Biochemistry (2004-2007) Merck Research Laboratories, Ralway, NJ Susanti, Dwi Visiting Scientist from the Institut Teknologi Bandung, Indonesia (2005-06) Genetics, Bioinformatics and Computational Biology, PhD Student, Virginia Tech Tanoerahardjo, Francisca, MD Visiting Scientist from the Rotisulu Pulmonary Hospital, Bandung, Indonesia (2005) Head, Clinical Pathology, Rotinsulu Pulmonary Hospital, Bandung, Indonesia VonHerbulis, S. Lindsay Undergraduate Research Student, Biochemistry (2002-2004) Forestry Services, Inc., Charlottesville, VA Wang, Ban Undergraduate Research Student John Hopkins University

The Network Dynamics and Simulation Science Laboratory designs, develops and implements simulation tools to understand large biological, information, social, and technological systems. For many reasons, which range from practical difficulty to the possibility of great harm, simulations are a uniquely capable medium in which representation and analysis can be performed. The need for simulations is derived from questions posed by scientists, policy makers, and planners involved with very large complex systems. Extremely detailed, multi-scale computer simulations allow formal and experimental investigation of large-scale systems. By enabling individuals to explore the potential impact of different interventions or strategies on the course of a disease outbreak or a specific transportation scenario, for example, important information can be prioritized as to the potential merits of different interventions. The Network Dynamics and Simulation Science Laboratory is currently pursuing projects in the following programmatic areas: integrated high-performance simulation and data service architectures; human population dynamics and associated social networks in urban environments and at the national scale; epidemiology and the spread of infectious diseases; computational and behavioral economics and commodity markets; next generation computing and telecommunication systems; and computational systems biology. The group has developed Simfrastructure, a service- and grid computing-oriented modeling tool for socio-technical, biological, and information systems and Simdemics, a scalable high-performance computing-based service environment for general reaction diffusion systems. Other recent milestones include the successful development of scalable algorithms for simulating epidemics and other reaction diffusion systems. A synthetic population has been created consisting of 300 million individuals endowed with daily activity patterns where the activities are performed at real locations. The EpiSims tool is being used by the National Institutes of Health Models of Infectious Disease Agent Study to support preparedness for potential disease pandemics.

Phylogenomics involves the study of evolutionary relatedness among various groups of organisms. The idea that a common ancestry links all living organisms has been an integral part of biological research long before it became possible to compare sequence information. Full gene sequences of many organisms have now been completed, providing researchers with opportunities to identify more specific ancestral connections using genes, chromosomes and whole genome sequences. Phylogenomics is thus enabling the analysis of the similarities and differences of many species in an evolutionary context. By using phylogenetic models, researchers can identify patterns of diversification in gene sequences that relate to changes in function. A major focus of Dr. Allan Dickerman's Research Group is the creation of analysis tools needed to construct the history of common ancestry for all of the components of genomes within a particular area of interest. For this purpose, the GeneTrees database focuses on the alignment of protein sequences in an effort to obtain evolutionary histories. This project has created external collaborations with scientists interested in a wide range of organisms and their classification. In addition, the group's SeedGenes project is near completion. This project focuses on the bioinformatic and functional analysis of genes active in the early development of plant seeds. More specifically, SeedGenes was developed to examine essential gene functions of the model organism Arabidopsis thaliana and includes a web interface that offers information on all the proven early developmental lethal mutations in Arabidopsis. This coordinated effort has resulted in the collection and analysis of information related to the function of A. thaliana's genes and the results have been synthesized for efficient use by the scientific community.

Our laboratories are active with the work of undergraduate and graduate students, medical students, post doctoral fellows and staff who work collaboratively with many other groups. The group and its research are inherently multidisciplinary, with research focusing on two areas: o applied computational biology and bioinformatics - text mining and hypothesis generation o genetics, genomics and proteomics research that works in tandem with our software findings and instrumentation capabilities, especially to improve human health, quality-of-life and security. We pursue independent research, work collaboratively with other faculty across Virginia Tech and researchers off-campus using the software and instrumentation being developed in our laboratory. The group attempts to make the results of our research available to the world to accelerate biomedical discovery and application via commercialization, having founded Light Biology (which became Nimblegen, now Roche), BioAutomation, and Heliotext. Additional information and our on-line computational resources can be found at http://innovation.vbi.vt.edu/. The currently active projects include: o Genomic analysis software and laboratory validation for gene discovery (polymorphism prediction, genomics features extraction/correlation) o Expression and proteomics software (structure/function/variation correlation and structural databases) o Data mining (grammar induction) on terabyte-sized biomedical text datasets o Duplicate article detection as a component to the study of publication ethics as presented on the Déjà Vu web site o Computational modeling and experimental verification of quantitative traits in association with repeat polymorphisms in canines and humans o The development of new approaches to clinical decision making and support.