Faculty & Staff
Faculty in the Division of Systems Biology at Virginia Tech are involved in the study of a wide range of biological problems that can be addressed with bioinformatics, computational and systems biology approaches. Through their research, they aim at addressing scientific questions related to how the cells divide, how cancer emerges as a disease, how the immune system works, or how infectious diseases can be stopped from propagating. Specific topics include:
- Cell cycle regulation and cell division
- Cancer etiology, diagnosis and treatment
- Circadian biology
- Drug development accelerated by modeling
- High throughput studies of genes, proteins and metabolites
- Immune system and infectious disease modeling
- Metabolic control
- Microbial ecology, diversity and communication
- Microbiome dynamics
- Molecular dynamics
- Network topology, dynamics and cell physiology
- Protein signaling networks
- Spatiotemporal dynamics and pattern formation
- Synthetic biology
My group uses approaches from molecular biology, bioinformatics, and ‘omics to study the ecology and evolution of microbial life. Microbes (bacteria, archaea, and microbial eukaryotes) represent an enormous reservoir of physiological and phylogenetic diversity in the biosphere, and their activities play fundamental roles in shaping global biogeochemical cycles. I am primarily interested in 1) understanding patterns and determinants of genomic diversity in uncultivated microbial groups in the biosphere, and 2) how microbial interactions and symbiosis shape higher-order phenomena in communities and ecosystems. Recent efforts have focused on marine microbial communities, though my interests span a variety of ecosystems.
My research interests are in building systems-level models to understand the dynamics of cellular processes involved in regulation, decision-making, and self-assembly. Currently, I am using deterministic and stochastic simulations to study the dynamics of two such processes: the spindle assembly checkpoint (SAC) and clathrin-mediated endocytosis.
SAC is a cellular surveillance mechanism that ensures that the duplicated chromosomes are partitioned equally between daughter cells during cell division. The network of molecular interactions underlying SAC seamlessly combine two seemingly opposing characteristics of dynamical systems – sensitivity and robustness to cellular noise. Using stochastic simulations, I am studying the properties of the SAC molecular network that allow these two characteristics to coexist.
Dr. Brown's research involves the application of computational molecular modeling and bioinformatic tools to relate the structure and dynamics of molecular systems to function. Currently, we study the amyloid β-peptide that is associated with Alzheimer's disease and peroxisome proliferator-activated receptor that is associated with inflammation, diabetes, and obesity. We also study drug targets involved in a variety of pathways (e.g. sphingosine kinases, ERKs, etc) to provide insight into drug discovery. Finally, we use bioinformatic tools to understand pathway relationships of proteins and assess motif presences in genomes.
Dr. Chen's research focuses on mathematical modeling of biological systems. She is particularly interested in studying the coupling between biological signaling and spatiotemporal regulation/mechanical interactions.
Biological systems self-assemble into highly heterogeneous and dynamic structures. Spatiotemporal regulation and mechanical interactions constitute critical aspects in biological signaling mechanisms, but in most cases their specific functional roles remain poorly understood. Modern experimental tools allow high resolution live-cell imaging and dynamic force measurement for these biological processes. Mathematical modeling fills the gaps in these data with physically viable assumptions, which optimizes the information obtained from the data and contributes to deeper understanding of the functional roles of spatiotemporal regulation and mechanical interactions. Previously, Dr. Chen built mathematical models for a wide range of biological mechanisms, including the spatiotemporal regulation of spindle assembly checkpoint mechanism, mechano-chemistry of bacterial motility mechanisms, skeletomuscular biomechanics, functioning neuronal networks, etc.
Dr. Chen's current interests lie in bacterial motility and intercellular coordination, spatiotemporal dynamics and mechanical dynamics in mitosis, and gene expression dynamics.
Dr. Pavel Kraikivski is a Collegiate Associate Professor in the Academy of Integrated Science, Division of Systems Biology at Virginia Tech with more than 14 years of experience applying mathematical models to study dynamic behavior of complex biological systems, especially to investigate the molecular mechanisms regulating intracellular transport, cell growth, division and cell death. Currently, he is applying innovative computational technology to develop a generic mathematical model of cell death decision network that determines how cell death mechanisms are controlled in sensitive and resistant cancer cells. Understanding how cell death decisions are avoided will lead to new approaches to treat drug-resistant cancers.
Cancer is a disease of the cell cycle that results in uncontrolled proliferation of cells. In our laboratory, we explore the molecular mechanisms of breast cancer cell cycle regulation by using holistic, mass spectrometry-based systems biology approaches.
We develop proteomic technologies for investigating the pathways that enable cancer cells to bypass tightly regulated molecular checkpoints, proliferate in an unrestrained manner, metastasize and hijack normal biological function. Further, we capitalize on the power of our proteomic data to identify novel therapeutic drug-targets, and to develop microfluidic architectures for targeted detection of biomarkers indicative of disease.
Work in the Allen lab is focused broadly on understanding the evolutionary, ecological, and biogeographic patterns that shape our world. To do this, the research falls under three goals. First, to collect and analyze data to uncover these patterns. Second, integrate different data types (e.g. morphology, genetics) to understand the mechanisms behind the patterns, and third to develop the bioinformatic tools necessary to do these analyses. This work focuses on data collected in the field, mined from online resources and even working with the general public (e.g. community science) to collect data.
Dr. Baumann's interests in system biology revolve around using mathematical modeling of biological systems as a way to integrate knowledge, gain enhanced understanding, and make useful predictions. His current interest is to model the effect of anti-estrogen therapy in breast cancer cells and create optimized therapeutic protocols that enhance cancer cell death while delaying the onset of resistance and limiting side effects. More generally, Dr. Baumann is interested in deterministic and stochastic modeling of biological systems, from ecological models to pharmacological models, to cell signaling networks.
Dr. Brazhnik’s interests are in using mathematical and computational approaches for studying biological systems. Currently, he is applying dynamical systems theory for analyzing cancer-related processes in cells. Prior to joining the AIS, Paul was a chief of the Bioinformatics and Computational Biology Branch in the Division of Biophysics, Biomedical Technology, and Computational Biosciences at NIGMS (NIH). He oversaw grants in the areas of bioinformatics, computational biology, systems biology, biostatistics and biological network modeling. In addition, he directed the Joint DMS/NIGMS Initiative to Support Research at the Interface of the Biological and Mathematical Sciences. Before joining NIGMS, for 13 years Dr. Brazhnik was a faculty in the Department of Biological Sciences and Virginia Bioinformatics Institute at Virginia Tech. He also was a consulting scientist for Rosa Pharmaceuticals and, prior to that, a scientist at Entelos, Inc.
Dr. Cao's research interests are in computational science and engineering. He has worked in the field of numerical solution of differential-algebraic equations (DAEs), sensitivity analysisfor DAEs and ordinary differential equations (ODEs), error estimation and control for linear systems and ODEs, and stochastic simulation for biochemical systems. He has made significant contribution in the stochastic algorithms related to multi-scale problems. The implicit tau-leapingmethod and the slow-scale SSA method proposed by him and his collaborators have become a hot topic in this area.
Dr. Carey's research integrates population, community, and ecosystem ecology to examine how natural and anthropogenic perturbations affect freshwater systems. A current research focus is on understanding how feedbacks between microbial and plankton taxa, food webs, and nutrient cycling can mediate ecosystem resilience to eutrophication and climate change. We work across lakes, reservoirs, and streams, and use models, field experiments, and long-term data analysis to determine how stressors affect both biological communities and ecosystem services.
Dr. Childs' research interests involve developing and analyzing mathematical models of biological systems as well as building innovative quantitative methods informed by experimental results. The biological focus of her work is the development of host immune responses to infectious disease and the resulting feedback on pathogen dynamics and transmission at the population level. She studies this from two perspectives: the acquisition of immunity to antigenically complex and varying pathogens and pathogen evolution as a response to adaptive immune control.
Dr. Cimini's research focuses on two major areas: (i) investigating how the mechanics and dynamics of mitotic apparatus components ensure accurate chromosome segregation during mitosis; (ii) how changes in chromosome numbers affects cell division and cell proliferation. For both areas of investigation, her lab collaborates with mathematical modelers. To study the mechanics and dynamics of mitosis, they use live-cell and high-resolution light microscopy data to build mathematical models that describe the forces acting within the mitotic apparatus at different stages of mitosis. Using these models, they make new predictions that they then test designing new experiments. To study the effects of changes in chromosome numbers, they use experimental data to build population dynamics models that they use to interpret their experimental data and design further experiments based on model predictions.
Dr. Ciupe's research focuses on development, analysis and validation of mathematical models that describe immune system responses to viral diseases such as human immunodeficiency virus, hepatitis B virus, dengue virus and equine infectious anemia virus. Besides modeling virus-host interactions, she is investigating possible homeostatic mechanisms that regulate lymphocyte population size and T cell receptor diversity, the immune deficiencies that lead to diabetes, and the use of dynamical systems to model archeological data.
The mathematical questions associated with Dr. Ciupe's research range from the study of existence and positivity of solutions of systems of ordinary and delayed differential equations, to conducting local and global stability analysis of steady state solutions, to determining conditions for the emergence of bi-stable solutions, to optimization techniques involved in data fitting, to analysis of the sensitivity of models to changes in parameters and errors in data measurements, and finally, to questions regarding model validation. Biologically, she aims to determine the individual and combined contributions of cellular and antibody immune responses in protection against viral infections, the host-virus mechanisms responsible for transitions from acute to chronic disease, and the determination of the values of unknown parameters.
Our research focuses on cell division machinery. Successful cell division requires distributive segregation of genome copies into daughter cells. Early in division microtubules form a bipolar spindle that aligns replicated chromosomes along the metaphase plate. As sister chromatids begin to segregate apart, a large contractile structure made up of f-actin and non-muscle myosin II assembles along the equatorial cortex of the cell aligned with the division plane. This actomyosin contractile ring is a highly dynamic structure that constricts to physically drive membrane ingression and cell division. We combine cell biology techniques, such as quantitative fluorescence microscopy and genetics, with computational techniques, such as agent-based modeling, to characterize the molecular mechanisms of cell division. We are particularly interested in investigating how chromosome segregation defects in anaphase alter contractilering composition and dynamics during cytokinesis and abscission.
Dr. Finkielstein is Associate Professor of Cell and Molecular Biology in the Department of Biological Sciences at Virginia Tech, Director of the Integrated Cellular Responses Laboratory, and Member of the Board of Directors of the Virginia Breast Cancer Foundation.
She has more than fifteen years experience as an accomplished scholar, teacher, and researcher. She founded the Integrated Cellular Responses Laboratory (ICRL) at Virginia Tech where her group works to understand the contribution of environmental factors on breast cancer initiation and progression.
Dr. Finkielstein's laboratory has produced over 50 publications and book chapters in the field, including articles in top journals such as Nature and Cell. In addition, She has filled and commercialized patents, trained over 120 undergraduate students that continued their graduate education in top and Ivy League Universities, and graduated numerous MSc and PhDs in the last years. Furthermore, Dr. Finkielstein has been running an international high school exchange program that has facilitated a new cultural and scientific experience to many Virginia and Argentinean students.
As a general rule, the more complex a machine is, the more likely it is to break - simply because there are more potential breaking points. Yet, cells are highly complex entities and are extremely robust, i.e. they perform their functions reliably even in variable intra- and extracellular milieus. We want to understand the underlying basis: what makes biological systems robust?
To study this question, we look at cell division, a process that is essential for life and whose deregulation is common in cancer. When cells divide, a multitude of changes need to happen in a very short time, and any error can be fatal. Hence, reliability and robustness are crucial. To understand the underlying principles, we combine perturbations with high-end, quantitative microscopy, which allows us to obtain single-cell, time-resolved and spatial information.
Computational modeling, for which we collaborate with experts in this field, helps us to interpret these experiments and to develop new hypotheses. We work with fission yeast, which is an excellent model for eukaryotic cells, and where we can easily introduce perturbations using CRISPR/Cas9 and other state-of-the-art technologies.
Lenwood S. Heath is a professor in the Department of Computer Science at Virginia Tech. His research interests include theoretical computer science, algorithms, graph theory, computational biology, and bioinformatics. Dr. Heath completed a Ph.D. in computer science at the University of North Carolina, Chapel Hill, an M.S. in mathematics at the University of Chicago, and a B.S. in mathematics at the University of North Carolina, Chapel Hill. Before joining the faculty at Virginia Tech in 1987, he was an instructor of applied mathematics and member of the Laboratory of Computer Science at MIT.
Dr. Heath is a member of SIAM, a member of the International Society for Computational Biology (ISCB), and a senior member of the Institute of Electrical and Electronics Engineers (IEEE). He is an editor of the Journal of Interconnection Networks (JOIN).
Dr. Hoeschele has been a Professor at Biocomplexity Institute of Virginia Tech and in the Department of Statistics at Virginia Tech since 2002. Prior to this appointment, she was Assistant and Associate Professor and Professor of Statistical Genetics in the Department of Dairy Science at Virginia Tech. Dr. Hoeschele was a Visiting Professor at the University of New England (Australia) in 1993, at Wageningen Agricultural University (The Netherlands) in 1995, and in the Statistics Department at North Carolina State University in 1999.
Dr. Hoeschele has served and continues to serve as the Principal Investigator on smaller statistical methodology grants and as statistics/statistical genetics co-Investigator on large, collaborative grants in genomics and Genetical Systems Biology. Her current collaborative research focuses on the genetical genomics and genetical epigenomics of common and complex human diseases such as cardiovascular disease, diabetes and obesity, via collaborations with investigators at Wake Forest University Medical School. Dr. Hoeschele's current statistical methodology research focuses on multivariate statistical methods for genome-wide asssociation (and linkage) analyses of high-dimensional phenotypes such as genome-wide gene expression, and the utilization of the SNP-phenotype associations for causal inference.
Associate Professor, Biological Sciences
Fellow and Assistant Professor, Biocomplexity Institute
The rotation of the earth creates a daily fluctuation of environmental cues, and organisms have evolved internal timing systems, called circadian clocks, to coordinate their daily activities to anticipate and prepare for these environmental changes. Because circadian rhythmicity is a fundamental aspect of temporal organization in essentially every cell in the body, and governs many biological processes ranging from molecular and biochemical pathways to physiological and behavioral rhythms, disruption of the circadian clock can have a severe influence on human health, ranging from psychiatric disorders, obesity, cardiovascular diseases, to certain types of cancer.
The mission of the Kojima laboratory is to understand how the molecular clock machinery in each cell controls circadian biochemistry, physiology and ultimately behavior at a molecular level. We specifically focus on rhythmic gene expression in various mouse tissues, with a special emphasis on transcription-independent gene regulatory mechanisms. We use various approaches such as neuroscience, molecular/cellular biology, genomics, bioinformatics and computational biology to open up an exciting new avenue and address unforeseen questions.
Research in the Lemkul Lab focuses on applying molecular dynamics simulations to biomolecules, including proteins, nucleic acids, and phospholipid membranes. We are primarily interested in understanding how proteins and nucleic acids fold and interact with each other, with specific emphasis on amyloid-forming peptides and DNA and RNA G-quadruplexes. We leverage this information for computer-aided drug design against conditions such as Alzheimer's disease and different types of cancer.
Professor, Biological Sciences
Dr. Li studies innate immune memory underlying both acute and chronic inflammation. His group has defined mechanisms governing the novel paradigms such as differential polarization and skewing of immune environment as well as priming and tolerance of macrophages. These dynamic paradigms play key roles during both the pathogenesis and resolution of human inflammatory diseases such as atherosclerosis, sepsis, wound healing, and cancer.
Dr. Mukhopadhyay's group studies the biochemical mechanisms used by microorganisms to survive under extreme conditions with specific focus on the methanogenic archaea, natural gas production and tuberculosis. They also examine the structure-function relationships of two CO2-fixing enzymes (PEPCK and PEPC) with relevance in type 2 diabetes and production of chemicals and food crop.
Professor, Computer Science
The overall goal of Murali's research in computational systems biology is to build phenomenological and predictive models of the intricate interaction networks that govern the functioning of a living cell. He designs algorithms and computational tools based on graph theory, data mining, and machine learning to obtain network-oriented system-level insights into fundamental questions on cellular phenomena.
Current projects include signaling pathway reconstruction and crosstalk, synthesis of top-down and bottom-up approaches in systems biology, systems biology of bioengineered livers, and host-pathogen protein interactions.
Dr. Onufriev's research group develops and uses computational methods to understand dynamics and function of large biomolecular systems such as proteins, DNA, and their complexes.
Associate Dean for Undergraduate Programs
Pleimling’s research, which is focused on Statistical Physics and Condensed Matter Physics, has resulted in more than 140 peer-reviewed publications. He is the author of a textbook on aging and non-equilibrium phase transitions and the editor of two books on the same topic. He has been the research advisor of 17 Ph.D. students and of 32 undergraduate students. His research has been funded by the National Science Foundation, the Department of Energy, the Army Research Office, the Deutsche Forschungsgemeinschaft and the European Commission. In 2015 he was elected Fellow of the American Physical Society “For seminal and sustained contributions to computational statistical physics, specifically his investigations of complex systems far from thermal equilibrium, and in-depth understanding of non-equilibrium relaxation and physical aging phenomena.”
My primary interest in systems biology is in the tools and visualizations that support the process by which modelers develop biochemical reaction models. The principle goal is to support the modeler in the process of developing larger scale models. This includes visualizations and automated optimization tools, as well as model design and editing tools. I see many parallels between the process of developing, maintaining, and analyzing a model based on a collection of chemical reaction equations and the software development process.
I am broadly interested in understanding of how a eukaryotic genome is organized and how it changes over time. More specifically I am looking at if and how the linear and three-dimensional organization of chromosomes affect the function and evolution of insect genomes.
We use light microscopy, molecular techniques, and bioinformatics to delve into the genome at three different levels: the 3D structure of the cell nucleus, epigenetic modifications of chromosomes, and organization of DNA sequence. We investigate the effect of chromosomal attachments to the nuclear envelope on chromosome territories, gene-gene contacts, and genome rearrangements. We found that computational models of a fruit fly nucleus with more numerous attachments form more distinct chromosome territories, the frequency of intra-chromosomal gene-gene contacts increases, but the frequency of inter-chromosomal contacts decreases. By analyzing mapped genome assemblies of Anopheles gambiae, An. stephensi, An. funestus, An. atroparvus, and An. albimanus, we found that rearrangements of the X chromosome occur 3 times faster than autosomal rearrangements pointing to a special role of sex chromosomes in evolution of malaria mosquitoes. We characterized a major epigenetic component of the An. gambiae germ-line – small non-coding Piwi-interacting RNA (piRNA) sequences and their euchromatic and heterochromatic clusters. We also identified a subset of the piRNA-enriched genes that have functions related to reproduction and embryonic development.
Overall, my research helps to understand the mechanisms of mosquito evolution, adaptation, and reproduction. This knowledge can facilitate the development of innovative genome-based approaches for mosquito-borne disease control.
My research is primarily focused on phylogenetic inference, including the development of scalable computational methods using machine learning for reconstructing the evolutionary relationships among species, and on carrying out phylogenomic analyses in insect systems. Phylogenetic trees delineate relationships between populations or species and serve as the cornerstone of almost any basic research leveraging evolutionary information. That is to say, in addition to helping to reconstruct the Tree of Life, phylogenetic analyses can help researchers characterize the spread of pathogens, reconstruct ancestral sequences and morphologies, conduct “phylogenetically-aware” drug design and vaccine development, study the evolution of cancer cells (PhyloOncology), trace the evolution of human spoken languages, and numerous other basic and practical applications.
My laboratory employs systems biology or functional genomics approaches to study the basic biology of sex-determination and embryonic development in mosquitoes. On the basis of such fundamental information, we are developing novel genetic applications to control mosquito-borne infectious diseases. Such applications include a synthetic gene drive system for efficient and safe spread of refractory genes in mosquito populations and genetic manipulation of mosquito sex ratios and fertility.
We have experience with transgenic Aedes and Anopheles mosquitoes and next-generation sequencing of RNA and genomic samples. We have recently developed novel genomic and bioinformatics approaches to study Anopheles Y chromosome genes. We have recently discovered a male-determining factor in Aedes aegypti. We have also clearly demonstrated complete dosage compensation in An. stephensi by RNA-seq analysis of genes on different chromosomes of both sexes. Our research has the potential to bridge a major gap in our understanding of mosquito biology and lead to novel control strategies based on manipulation of mosquito sex ratios and fertility.
My expertise is building deterministic and stochastic models of the molecular control systems that underlie various aspects of cell physiology, including the cell cycle control system in yeast, drug sensitivity and resistance in breast cancer cells, circadian rhythms, cell differentiation in the immune system, stem cell dynamics in the shoot apical meristem of plants, and the growth, division and differentiation of alphaproteobacteria.
My group builds comprehensive, accurate, predictive models of these control systems and uses these models to understand the observations and data collected by our experimental colleagues. Deterministic models are formulated in terms of nonlinear differential equations describing the temporal dynamics of the reaction network, and, when appropriate, including spatial transport and diffusion of molecules. Stochastic models are formulated in terms of elementary chemical reactions and transport processes, simulated by Gillespie’s stochastic simulation algorithm, or, for more complex networks, by chemical Langevin equations.
My research interest has been related to systems biology mainly through our analyses of various omics analyses.
The point of systems biology is to consider the system as a whole, rather than isolating them into separate pieces, which has been the traditional approach used in biology. Examples of relevant projects include understanding the disease relationship through a systems biology approach, integrated analyses of various genomic, transcriptomic, proteomic, exome data in lung cancer, and identification/prediction of protein complex using network properties. Also relevant is the most recent collaboration with colleagues from the dairy science department, crop science department, and Civil Engineering department to understand the dynamics of antibiotic resistance genes/bacteria in a farming system using a systems approach.