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Project Team

Reinhard Laubenbacher | PI | Center for Quantitative Medicine at University of Connecticut
Laubenbacher Dr. Laubenbacher is a professor at the Department of Cell Biology, professor at the Jackson Laboratory for Genomic Medicine, and the director of the Center for Quantitative Medicine at the University of Connecticut since 2013. Before he joined UConn in 2013, he has been a Professor at the Virginia Bioinformatics Institute and a Professor in the Department of Mathematics at Virginia Tech from 2001 to 2013. He was also an Adjunct Professor in the Department of Cancer Biology at Wake Forest University in Winston-Salem (NC) and Affiliate Faculty in the Virginia Tech Wake Forest University School of Biomedical Engineering and Sciences. Current interests in Dr. Laubenbacher's research group include the development of mathematical algorithms and their application to problems in systems biology, in particular the modeling and simulation of molecular networks. An application area of particular interest is cancer systems biology, especially the role of iron metabolism in breast cancer.

Stefan Hoops | Virginia Bioinformatics Institute at Virginia Tech
Hoops Dr. Stefan Hoops is a senior project associate at the Virginia Bioinformatics Institute. He earned his Ph.D. in Mathematical Physics from the Norwegian Institute of Technology in 1993. Before joining VBI in 2000, Hoops served as a software designer/developer for Schumann Consulting Corporation in Germany from 1995-2000. He was elected in 2006 as one of the 5 editors of the Systems Biology Markup Language (SBML), serving for the years 2007-2009.
At VBI, Hoops works with Dr. Pedro Mendes in the Biochemical Networks Modeling Group. The research group is interested in understanding how cells work at the biochemical level. Their approach can be labeled as Systems Biology, as they derive quantitative dynamic models from integrative functional genomic data. They have ongoing projects in development of methodologies and software for the complete process of going from functional genomic data to computational models: biochemical simulators (Gepasi and COPASI), database design (DOME and B-Net), data analysis, network inference, parameter estimation for nonlinear models, and theoretical aspects of biochemical regulation (such as Metabolic Control Analysis).

Sook Ha | Virginia Bioinformatics Institute at Virginia Tech
Ha Dr. Sook Ha is a post doctoral associate at the the Virginia Bioinformatics Institute (VBI). She earned her Ph.D. in Computer Engineering from Virginia Tech in 2012. Her research interest was dimensionality reduction, pathway analysis, statistical learning, and informational visualization of biological data. The title of her dissertation was "Dimensionality Reduction, Feature Selection, and Visualization of Biological Data." She also earned her Bachelor and Master from Virginia Tech in Computer Science and in Information Technology respectively. Before starting her Ph.D. study, she worked as a software engineer for six years in Center for Advanced Aviation System Development at MITRE, a federal government R&D in nation's capital area. After finishing her Ph.D. degree, she continued her research as a visiting scholar to Dr. Ina Hoeschele's lab at the VBI for a few months. Then in April 2013, she joined Dr. Reinhard Laubenbacher's research group at the VBI as a post-doctoral associate, and has been working on the PlantSimLab project since.

Jane Glazebrook | University of Minnesota
Glazebrook Pathogen pressure exerts a major drag on crop yields. The outcomes of plant-pathogen interactions are determined by a fascinating complex interplay between pathogen efforts to extract nutrients from plant hosts and host efforts to prevent this. Major host and pathogen genes that play roles in these interactions are subject to ongoing selection as both pathogens and hosts evolve improved mechanisms for attack and defense.
We use a genetic approach to understanding plant defenses against pathogen attack. Plant mutants with defects in disease resistance are isolated and characterized, revealing the mechanisms underlying effective defense. Most of our work involves the reference plant Arabidopsis thaliana, which enables us to take advantage of powerful genetic and genomic resources available for this organism. In much of our work we use the bacterial pathogen Pseudomonas syringae. Growth of this hemi-biotrophic pathogen is limited by two main sectors of the defense network. One sector triggers defenses by host recognition of pathogen-associated molecular patterns (PAMPs), while the other triggers defenses through the salicylic acid (SA)-dependent network sector. We also study defenses against the necrotrophic fungal pathogen Alternaria brassicicola. Effective defense against his pathogen depends on production of the small anti-microbial molecule camalexin, as well as on the jasmonic acid-dependent network sector. By studying responses to both of these pathogens, we hope to obtain a broad view of the plant defense network.

Fumi Katagiri | University of Minnesota
Katagiri A major type of plant defense against pathogen is inducible defense: i.e., defense mechanisms are turned on upon recognition of pathogen attack. Research in my group is directed towards understanding (1) how plants recognize pathogen attack and (2) how this recognition leads to induction of coordinated responses in plants. We use Arabidopsis thaliana and its bacterial pathogen Pseudomonas syringae as a model to study these problems. We are interested in a network of molecules that enables inducible defense: how are the components and connections of the network organized?; how is the behavior of the network controlled? Pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) are two well defined modes of inducible defense. PTI is initiated by recognition of molecular patterns common among related microbes (microbe-associated molecular patterns, MAMPs) by pattern recognition receptors (PRRs). It is necessary for a potential pathogen to negate PTI sufficiently to be a real pathogen. For this purpose, real pathogens deliver effectors into the plant cell that interfere with PTI signaling. Plants may have receptors, resistance (R) proteins, which recognize some of the pathogen effectors and trigger ETI. The plant immune signaling network that controls inducible defense is different from other plant signaling networks because pathogens not only initiate signaling events but also interfere with plant signaling. Microbial pathogens also evolve much faster than plants. Therefore, the plant immune signaling network must have properties that allow it to withstand perturbations from a wide variety of pathogens without heavily relying on evolutionary adaptation. Unnecessary immune responses carry negative impacts on plant fitness, further constraining possible network properties.

John McDowell | Virginia Tech
McDowell Pathogens have evolved sophisticated molecular weapons to exploit plants as sources of food and shelter. For example, many pathogens have evolved the capacity to export their own proteins to the interior of plant cells. Once they have gained entry to the interior of plant cells, these pathogen "effector" proteins manipulate specific plant regulatory proteins to make the plant more susceptible to infection. Plants, in turn, have evolved large collections of surveillance proteins that recognize specific pathogen molecules (including some effector proteins) as signals of invasion. This molecular recognition can trigger potent immune responses in the plant, including programmed plant cell suicide at the site of invasion.

My group investigates the molecular interplay and co-evolution between pathogens effector proteins, their targets inside plant cells, and the plant immune surveillance system. Most of our effort is focused on the interaction between the model plant Arabidopsis thaliana and the oomycete pathogen Hyaloperonospora arabidopsidis (downy mildew disease). H. arabidopsidis is a natural pathogen of Arabidopsis, and is related to downy mildew pathogens of crops as well as notorious pathogens in the Phytophthora genus (e.g., late blight of potato).

João Setubal | University of São Paulo and Virginia Bioinformatics Institute at Virginia Tech
Setubal João C. Setubal is Full Professor at the University of São Paulo, Brazil and Adjunct Faculty at the Virginia Bioinformatics Institute. He has a PhD in Computer Science from the University of Washington (1992). Setubal's main work is in the development and use of computational tools for microbial genome, transcriptome, and metagenome analysis.

Brett Tyler | Oregon State University
Tyler Much of the Tyler group's research is focused on the systems biology of microbe-host interactions, with a focus on the interactions of oomycete pathogens with their hosts. This research spans molecular biology, structural and functional genomics, bioinformatics, data mining and mathematical modeling.

   PlantSimLab: A Simulation Laboratory for Plant Biology is funded by NSF Award Number 1146819.