Aviv Bergman, Ph.D.
Harold and Muriel Block Chair in Systems & Computational Biology
Professor and Founding Chairman
Our research agenda involves multidisciplinary research into quantitative problems of evolutionary biology that can be approached using a combination of computational, mathematical and experimental tools. The focus of the research program is mainly on quantitative aspects of evolution and developmental biology, however, the relationship between these subjects and experimental molecular genetic studies of evolution and development are an integral part of the research efforts.
Andras Fiser, Ph.D.
The era of genomics and high throughput experimentation has opened up an opportunity for science and caused a paradigm shift in research. The opportunity is to look at a biological system as a whole. Meanwhile the challenge in research has shifted from data acquisition to the problem of developing computational techniques for systemic and systematic analysis of biological information.
Research topics in our lab include: the evolution of protein structures, modeling protein structures (incorporating experimental restraints, modeling loops and side chains, developing pairwise statistical energy functions, developing sequence-to-structure alignment optimization algorithms) and designing new protein structure topologies using fragments from existing structures. We are also interested in the question of exploring life stage specific virulence factors in apicomplexan pathogens by analyzing high throughput data from proteomics (mass spectrometry and cross-linking), epigenomics (ChIPChip and deep sequencing) and transcriptomics (mRNA expression) experiments and to computationally simulate how the network of their transcriptional regulation genes has to change to allow these pathogens to shuttle between the virulent and non-virulent stages characterized by the high throughput experiments.
Eduardo Fajardo, Ph.D.
Associate (PI: Andras Fiser)
Andrew Yates, Ph.D.
Our research relates to immunology, the within-host dynamics of pathogens, and connecting the epidemiology of disease (between-host dynamics) with immunological (within-host) processes. We like to collaborate with experimental biologists wherever possible.
Jessica Mar, Ph.D.
The focus of the Mar lab is to understand the functional impact of variability in biological signals, such as gene expression. We are interested in modeling how variable expression in certain genes affect the regulatory capacity and plasticity of gene networks, and the downstream consequences for cellular phenotypes. Analyzing variability gives us a window into the regulatory control of the genome, and we are exploring how changes in expression variance can better inform our understanding of disease processes. Our work has applications in single cell gene expression, stem and pluripotent cells, cell fate transitions, and cancer studies.
Yinghao Wu, Ph.D.
Cell adhesions are crucial for many biological phenomena such as tissue morphogenesis, immuneresponse and tumor invasion. The aggregations of membrane receptors on cellular interfaces during these physical processes initiate the elaborate intracellular networks of signaling pathways. Despite remarkable experimental achievements, there is still a long way to eventually form a mechanistic understanding of cell adhesion, and further decipher its intricate connection to signal transduction. By integrating computational analysis with experimental measurements, our lab focuses on developing multi-scale modeling frameworks to study the cross-talks between cell adhesion and cell signaling. We are particularly interested in asking the following questions: why and how different cells form contacts; when and where these contacts are formed at specific locations of our bodies; what are their functional impacts to the downstream signaling pathways, and further to our human health.
Bojana Gligorijevic, Ph.D.
Instructor (PI: Aviv Bergman)
I am interested in investigating the mechanisms of tumor cell intravasation by implementing established and developing new microscopy technologies, combined with advanced image analysis and modeling into a “systems microscopy” approach. My focus is on visualization and analysis of tumor cell behavior as it relates to tumor microenvironment and its spatial and temporal changes, including tissue remodeling by tumor cells themselves. Utilizing several novel microscopy-based techniques of my own combined with classical cell biology, I was led to interest in integrating interactions of tumor cells with extracellular matrix, macrophages, endothelial cells and pericytes into a systems-level network of biologically relevant in- and out- signals to decipher how these interactions lead to intravasation and subsequent metastasis. The goal I wish to achieve is to be able to predict tumor cell decisions, which are a dynamical, ever-changing outcome of multiple biomechanical and biochemical signals from different cell-based and matrix-based sources. I believe this approach will take me towards a detailed, mechanistic understanding of intravasation and metastasis, as well as direct me towards novel therapeutic targets within the tumor microenvironment. I think the ideal perspective is a result of the iteration between molecular and cell biology, imaging and computational/systems methods.
Eduardo Zaborowski, Ph.D.
Research Assistant Professor
I am interested in applying my knowledge of Physics, Chemistry and Biology to address challenging problems in Basic and Applied Research environments. More specifically, I want to make use of state-of-the-art Information Technology tools to facilitate the acquisition, storage, process and analysis of scientific data. That includes the evaluation, selection and implementation of new computing resources (both hardware and software). Where applicable, it might also involve programming, scripting and pipelining of algorithms, as well as the installations, configuration and customization of software to support scientific research.
Parsa Mirhaji, M.D., Ph.D.
Research Associate Professor
Libusha Kelly, Ph.D.
We study how ecosystem-level genetic variability influences adaptive responses to dynamic environments. Because microbial genomes of the same 'species' can be extremely diverse at the gene level, 16S characterization is not a reliable metric for the functional capacity of microbial populations. Metagenomics partially overcomes this issue by allowing the identification of functional genes in diverse community samples. Functional capacity, which can be selected for by environmental conditions, is accelerated by the ability of bacteria to exchange genes with each other and with viruses. A major question, therefore, is: How does variability at the gene level affect the functional capacity and interactions between members of microbial populations? Our interests are in 1) how environments select for different sets of microbial and viral genes, and 2) the influence of the commensal microbiota on human drug metabolism.