In exploring the rich expanse of Network Biology, COMBINE takes full advantage of UMD’s strengths in three different fields: (1) quantitative biology, (2) physical/mathematical modeling, and (3) computational science. COMBINE builds upon existing highly successful interdisciplinary initiatives at the University of Maryland (UMD), including:
- The Center for Bioinformatics and Computational Biology (CBCB), which combines quantitative biology and computer science
- The Biophysics Program, which combines quantitative biology and physical/mathematical modeling
- The Applied Math and Scientific Computing program (AMSC), which promotes the application and development of mathematical and computational tools for interdisciplinary research
Core COMBINE Faculty:
- Girvan, Michelle (PI; Physics/IPST/Biophysics/AMSC) Expertise: Network science, modeling, biological systems. Prof. Girvan’s research focuses on complex networks, operating at the intersection of statistical physics, nonlinear dynamics, and computer science and has applications to social, biological, and technological systems. While some of the research is purely theoretical, Girvan has become increasingly involved in using empirical data to inform and validate mathematical models.
- Butts, Daniel (Co-PI; Biology/AMSC/NACS) Expertise: Computational neuroscience. Research in Prof. Butts’ NeuroTheory Lab is concerned both with developing larger theories of system-level function in the visual and other sensory systems, as well as working closely with neurophysiologists to design and perform experiments that can guide and/or validate these theories. The Butts’ Lab also develops new analytical tools to facilitate these new experiments, as well as increase what can be learned from existing experiments.
- Corrada Bravo, Hector (Co-PI; CS/CBCB/AMSC) Expertise: High-throughput genomics, data mining, machine learning. Prof. Corrada Bravo’s research focuses on statistical and machine learning methods for high-throughput genomic data analysis. This includes pre-processing of measurements from high-throughput assays, disease risk models that integrate high-throughput genomic and other data, and cancer epigenetics and biomarker discovery. Corrada Bravo’s research interests also include the development of new methods and tools from multiple areas in the computational and statistical sciences: basic bioinformatics/biostatistics, statistical and machine learning, data management, and numerical optimization.
- Fagan, William (Co-PI; Biology/AMSC) Expertise: Ecological networks, eco-informatics. Prof. Fagan’s research involves meshing field biology with theoretical models to address critical questions in community ecology and conservation biology. Ongoing research falls in several areas that illustrate this melding of theory and problem-solving, including 1) spatial ecological dynamics, 2) ecoinformatics, biodiversity databases, and conservation planning, and 3) biological stoichiometry and paleoecostoichioproteomics.
- Varshney, Amitabh (Co-PI, UMIACS/CS) Expertise: Visual informatics for biological applications. Prof. Varshney’s research focus is on exploring the applications of graphics and visualization in engineering, science, and medicine. He has developed new algorithms for automatically generating multiresolution object hierarchies, image-based rendering, parallel computation and simplification of radiosity meshes, and fine gesture recognition for virtual environments. He is currently exploring applications in general-purpose high-performance parallel computing using clusters of CPUs and Graphics Processing Units (GPUs).
- Hannenhalli, Sridhar (CBMB/CBCB) Expertise: Computational biology. Within the broad field of computational biology, Prof. Hannenhalli’s lab focuses on eukaryotic gene regulation and its evolution. The lab develops computational approaches to harness the huge amount of biological data (genomes, epigenomes, transcriptomes, proteomes, etc.) to answer specific biological questions pertaining to these domains. Hannenhalli is also involved in exploiting massive amounts to clinical and molecular data in cancer to identify genetic interactions and dysregulations relevant to cancer.
- JaJa, Joseph (UMIACS/ECE) Expertise: Machine learning, visualization. Prof. Jaja’s current research interests are in high performance computing, long-term management and preservation of digital information, and large-scale data management, analysis, and visualization.
- Losert, Wolfgang (Physics/IPST/Biophysics) Expertise: Cell dynamics, integration of disparate data types. Prof. Losert’s research is centered on dynamical properties of Complex Systems at the convergence of physics and biology. The main thrust of his work on living systems is to assess how cell motion and collective behavior are affected by physical cues, in particular the topography of the surface, surface adhesivity, and cell-cell adhesion.
- Ott, Edward (Physics/ECE/AMSC) Expertise: Nonlinear dynamics, networks. Prof. Ott’s research is on the basic theory and applications of nonlinear dynamics. Some of his research current projects are in wave chaos, dynamics on large interconnected networks, chaotic dynamics of fluids, and weather prediction.
- Gili Marbach-Ad, CMNS Teaching and Learning Center.
- Hailey Marr, School of Public Health.
- Mark Connolly, Wisconsin Center for Education Research, University of Wisconsin-Madison.
Affiliated COMBINE Faculty and Partners:
- Mihai Pop (CS/CBCB)
- Eytan Ruppin (CBCB/CS)
- Jonathan Simon (ECE/NACS)
- Jayanth Banavar (CMNS/Physics)
- Rajarshi Roy (Physics/IPST)
- Jim Yorke (Math/IPST)
- Patrick Kanold (ECE/Biology)
- Amy Sapkota (SPH)
- Gerald Wilkinson (Biology)
- Karen Lips (Biology)
- Dan Larson (NIH)
- Dietmar Plenz (NIH)
- Doug Erwin (Smithsonian Institution)
- Pete Marra (Smithsonian Institution)
- Rao Gullapalli (University of Maryland Medical School)
- Owen White (University of Maryland Medical School)