Curriculum in Bioinformatics and Computational Biology
TIMOTHY ELSTON, Director
Max Berkowitz, Theoretical and Computational Chemistry
Charles Carter, Protein Crystallography, Structural Polymorphism and Function
Jeff Dangl, Plant Genetics and Cellular Biology, Plant Disease Resistance and Cell Death Control
Henrik Dohlman, Regulators of G Protein Signaling
Nikolay Dokholyan, Protein Folding, Design, and Evolution
Timothy Elston, Mathematical Modeling of Biological Networks
Gregory Forest, Mathematical Modeling of Mucociliary Transport Processes
Klaus Hahn, Spatio-temporal Dynamics of Signaling in Living Cells
Joseph Ibrahim, Bayesian Model Selection, Prior Elicitation, Bayesian Computational Methods, Bioinformatics, Missing Data Problems, Survival Analysis, Longitudinal Data, Generalized Linear Models
Jason Lieb, Regulation Chromosomal Functions such as Transcription, DNA Replication and Repair, Recombination and Chromosome Segregation
Terry Magnuson, Mammalian Genetics/Genomics/Development/Mouse Models of Human Disease
Steve Marron, Analyzing Data That Lie in Non-Standard Spaces
William Marzluff, Regulation of RNA Metabolism in Animal Cells
Fernando Pardo-Manuel de Villena, Evolution, Mouse Genetics, Epigenetics, Female Meiosis, Chromosome Segregation, Meiotic Drive
Charles Perou, Genomic and Molecular Classification of Human Tumors to Guide Therapy
Jan Prins, High-Performance Computing, Algorithms, Programming Languages, Scientific Computing
Matthew Redinbo, Structural Studies of Dynamic Cellular Processes
Ivan Rusyn, Molecular, Biochemical and Genomics Approaches toward Understanding the Mechanisms of Chemical-Induced Carcinogenesis
Jack Snoeyink, Discrete and Computational Geometry Applications to Molecular Biology
John Sondek, Structural Biology of Signal Transduction
Alex Tropsha, Computational Analysis of Protein Structure and Drug Design
Wei Wang, Data Mining, Classification and Clustering Analysis of Gene-Expression Data and Protein Structures
Kirk Wilhelmsen, Genetic Mapping of Susceptibility Loci for Complex Neurological Diseases
Fred Wright, Statistical Genetics, Computational Genome Analysis
Bradley Hemminger, Bioinformatics, Medical Informatics, User Interface Design
Corbin Jones, Evolution and Underlying Genetics of Species-Specific Adaptations
Brian Kuhlman, Protein Design/Modeling, Protein Interactions
Ethan Lange, Statistical Genetics of Human Disease
Yufeng Liu, Statistical Learning and Genomic Analysis
Karen Mohlke, Complex Traits, Genetics of Type 2 Diabetes
Maria Servedio, Mathematical Models Integrating Evolutionary Theories with Behavioral and Ecological Phenomena
Todd Vision, Evolution of Genome Organization, Architecture of Complex Traits
Fei Zou, Statistical Genetics of Complex Traits, Empirical Likelihood
Derek Chiang, Predicting Genetic Vulnerabilities of Cancer
Flavio Frohlich, Cortical Neurophysiology, Computational Neuroscience, Brain Stimulation, Epilepsy
Terry Furey, Chromatin and Gene Regulation, Cancer Genomics, High-Throughput Sequencing
Shawn Gomez, Systems Biology, Mathematical Modeling of Protein Interaction Networks
Alain Laederach, RNA Folding Bioinformatics
Yun Li, Statistical Genetics
Laura Miller, Mathematical Biology, Computational Fluid Dynamics, Biomechanics
Praveen Sethupathy, Genomics of Gene Regulation, microRNAs, Epigenomics, Computational Biology, Metabolic Disease
Brenda Temple, Structural Bioinformatics
William Valdar, Mapping of Complex Disease Loci in Animal Models, Statistical Genetics
Zefeng Wang, Splicing Regulation and Modulation
Modern biology, in this post-genome age, is being greatly enriched by an infusion of ideas from a variety of computational fields, including computer science, information science, mathematics, operations research, and statistics. In turn, biological problems are motivating innovations in these computational sciences. There is a high demand for scientists who can bridge these disciplines. The goal of the Curriculum in Bioinformatics and Computational Biology (BCB) is to train such scientists through a rigorous and balanced curriculum that transcends traditional departmental boundaries.
Incoming students are expected to matriculate from a broad range of disciplines; thus, it is important to ensure that all students have a common foundation on which to build their BCB training. The first year is dedicated to establishing this foundation and training all students with a common set of core BCB courses. BCB students will also participate in three laboratory research rotations their first year and ultimately join a lab at the end of those rotations. Research work is done in the laboratory facilities of the individual faculty member and is supported primarily by faculty research grants.
Curriculum faculty have appointments in 18 departments in the School of Medicine, School of Dentistry, School of Public Health, School of Pharmacy, School of Information and Library Science, and the College of Arts and Sciences. This level of diversity allows students a broad range of research opportunities.
Requirements for Admission for Graduate Work
Ideal BCB candidates should have an undergraduate degree in a biological, physical, mathematical, or computational science. They must apply to the program through a unified application program known as the Biological and Biomedical Sciences Program (BBSP). Students apply for graduate study in the biological or biomedical sciences at UNC–Chapel Hill. Students interested in any of the BBSP research areas apply to BBSP and those whose application portfolio places them highest on the admission list are asked to visit Chapel Hill for interviews. Students who are ultimately admitted to UNC make no formal commitment to a Ph.D. program. After completing their first year of study students leave BBSP, join a thesis lab, and matriculate into one of 14 participating Ph.D. programs. During their first year BBSP students are part of small, interest-based groups led by several faculty members. These groups meet frequently and provide a research community for students until they join a degree-granting program. Students are encouraged to apply as early as possible, preferably before December 1. (Applicants seeking a master’s degree are not considered for admission.)
Requirements for the Ph.D. Degree
In addition to the dissertation requirements of The Graduate School (four full semesters of credit including at least six hours of doctoral dissertation, a written preliminary examination, an oral examination and a dissertation), students in the Curriculum in Bioinformatics and Computational Biology must meet the following requirements: complete one or two foundational courses (as needed), complete all six of the BCB core courses, complete two elective courses (as determined by thesis advisor); participate in the BCB Colloquium as attendees the first and second years and as presenters in later years, act as teaching assistants for one of the BCB modules, attend the monthly seminar series sponsored by the Carolina Center for Genome Sciences, and participate in the yearly BCB mini-symposium in the fall. Students are required to rotate through at least three laboratories before choosing a thesis advisor. It is strongly recommended that students attend national meetings in order to better understand how their research fits with progress in their field.
Stipends for predoctoral students are available from an NIH predoctoral training grant and from the University. Tuition, student fees, and graduate student health insurance are also covered by the training grant and the University.
Courses for Graduate Students
701 Genome Sciences Seminar Series (1). Open to bioinformatics students only. Diverse but current topics in all aspects of bioinformatics. Relates new techniques and current research of notables in the field of bioinformatics and computational biology.
702 Genome Sciences Seminar Series (1). Open to bioinformatics students only. Diverse but current topics in all aspects of bioinformatics. Relates new techniques and current research of notables in the field of bioinformatics.
710 Bioinformatics Colloquium (1). The goal of this course is to expose students to the research interests of BCB faculty and to provide an opportunity for students to present their own work and receive input from their peers and faculty.
711 Applications of Information Theory, Genetic Programming, and Neural Networks to Sequence Analysis (1). Course covers applications of several commonly used methods to understand sequence structure and function at the DNA and RNA level.
712 Databases, Metadata, Ontologies, and Digital Libraries for Biological Sciences (1). Course introduces the basic information-science methods for storage and retrieval of biological information.
713 Data Mining and Clustering of Biological Information (1). Course covers methods of knowledge extraction.
714 Biostatistics in Bioinformatics and Computational Biology (1). Course covers statistical concepts as commonly used and applied to problems in gene mapping and gene expression analysis.
715 Mathematical and Computational Approaches to Modeling Signaling and Regulatory Pathways (1). The course provides an introduction to the basic mathematical techniques used to develop and analyze models of biochemical networks. Both deterministic and stochastic models are discussed.
716 Sequence Analysis (1). Course designed to introduce students to the computational analysis of nucleic acids sequences, including sequence comparison, alignment, and assembly.
717 Structural Bioinformatics (1). Course introduces methods and techniques for protein modeling.
725 Introduction to Statistical Genetics (3). Covers statistical methods for the analysis of family and population-based genetic data. Topics include classical linkage analysis, population-based and family-based association analysis, haplotype analysis, genome-wide association studies, basic principles in population genetics, imputation-based analysis, pathway-based analysis, admixture mapping, analysis of copy number variations, and analysis of massively parallel sequencing data.
850 Training in Bioinformatics and Computational Biology Teaching (3). Principles of bioinformatic and computational biology pedagogy. Students are responsible for assistance in teaching BCB and work under the supervision of the faculty, with whom they have regular discussion of methods, content, and evaluation of performance.
905 Research in Bioinformatics and Computational Biology (1–8). Credit awarded to students for research in bioinformatics and computational biology.
993 Master’s Thesis (3). Students are not accepted for master’s program.
994 Doctoral Dissertation (3–8). Credit for work done towards doctorate.