Computational Biology & Bioinformatics

PHD in Computational Biology & Bioinformatics

Program Principles & Goals

CBB students and faculty at the annual retreat

The Ph.D. Program in Computational Biology & Bioinformatics (CBB) is an integrative, multi-disciplinary training program that encompasses biology using computational and quantitative methods. In and out of the classroom, students learn to apply the tools of statistics, mathematics, computer science and informatics to biological problems. Vibrant and innovative research in these fields provides exciting interactions between biological and computational scientists. Because CBB is based in the Duke Center for Genomic and Computational Biology (GCB), it offers a unique opportunity for students to become tomorrow's leaders in genome sciences.

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Meet A Faculty Member

  • Anita Layton
    Robert R. & Katherine B. Penn Professor of Mathematics

    The focus of our research group is to use mathematical modeling techniques to better understand aspects of renal physiology. In particular, we are interested in the efficiency and synergy of various mechanisms of renal hemodynamics. We work with physiologists to formulate detailed models of the renal hemodynamics. Model simulations and predictions are then used to better understand hemodynamic mechanisms in physiological and pathophysiological conditions.

    Mathematical physiology. My main research interest is the application of mathematics to biological systems, specifically, mathematical modeling of renal physiology. Current projects involve (1) the development of mathematical models of the mammalian kidney and the application of these models to investigate the mechanism by which some mammals (and birds) can produce a urine that has a much higher osmolality than that of blood plasma; (2) the study of the origin of the irregular oscillations exhibited by the tubuloglomerular feedback (TGF) system, which regulates fluid delivery into renal tubules, in hypertensive rats; (3) the investigation of the interactions of the TGF system and the urine concentrating mechanism; (4) the development of a dynamic epithelial transport model of the proximal tubule and the incorporation of that model into a TGF framework.

    Multiscale numerical methods. I develop multiscale numerical methods---multi-implicit Picard integral deferred correction methods---for the integration of partial differential equations arising in physical systems with dynamics that involve two or more processes with widely-differing characteristic time scales (e.g., combustion, transport of air pollutants, etc.). These methods avoid the solution of nonlinear coupled equations, and allow processes to decoupled (like in operating-splitting methods) while generating arbitrarily high-order solutions.

    Numerical methods for immersed boundary problems. I develop numerical methods to simulate fluid motion driven by forces singularly supported along a boundary immersed in an incompressible fluid.

    Research Interests
    • Mathematical physiology
    • Scientific computing
    • Multiscale numerical methods
    • Fluid-structure interactions
Kai Fan

Kai Fan

5th year CBB Student Katherine Heller Lab
Mar 5
Neville Sanjana
New York Genome Center

CBB Seminar

Mar 19
Ziv Bar-Joseph
Carnegie Mellon University

CBB Seminar