Computational Biology & Bioinformatics

PHD in Computational Biology & Bioinformatics

Program Principles & Goals

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

  • Professor of Biology

    I am an evolutionary biologist with a background in molecular and developmental biology. I'm broadly interested in the evolution of gene regulation, at the scales of single genes, networks, and genomes. A long-term project in my lab is aimed at identifying regulatory changes that contributed to the evolution of uniquely human traits, particularly with regard to diet and physiology as well as cognition and brain anatomy. Another major project is investigating the population genetics of a developmental gene regulatory network in sea urchins.

    I study the evolution of genes and genomes with the broad aim of understanding the origins of biological diversity. My approach focuses on changes in the expression of genes using both empirical and computational approaches and spans scales of biological organization from single nucleotides through gene networks to entire genomes. At the finer end of this spectrum of scale, I am focusing on understanding the functional consequences and fitness components of specific genetic variants within regulatory sequences of several genes associated with ecologically relevant traits. At the other end of the scale, I am developing molecular and analytical methods to detect changes in gene function throughout entire genomes, including statistical frameworks for detecting natural selection on regulatory elements and empirical approaches to identify functional variation in transcriptional regulation. At intermediate scales, I am investigating functional variation within a dense gene network in the context of wild populations and natural perturbations. My research leverages the advantages of several different model systems, but primarily focuses on sea urchins and primates (including humans).

    Research Interests
    • Human origins
    • Evolutionary genomics
    • Evolution of gene regulation
    • Gene network evolution

Dinesh Manandhar

5th year CBB Student Raluca Gordan Lab
Oct 31
Anshul Kundaje, PhD, Stanford University
CBB Seminar Series

Interpretable, integrative deep learning models for regulatory genomics and epigenomics

Nov 1
Florian Wagner, CBB PhD Student from the Dave Lab
The Edge (Bostock library, first floor)

Module 3: Supervised analysis of gene expression data

Nov 2
Firas Midani, CBB PhD Student from the David Lab
CBB Student Seminar

Predicting colonization of microbes in the human gut microbiota