CURRICULUM

Prerequisites

MATH 381 - Discrete Math

BIOS 550 - Basic Elements of Probability AND Statistical Inference I

BMME 570 - From genes to tissues: Molecular Biology and genetics for biomedical engineers.

COMP 410 - Data Structures & Lab

Equivalent courses taken prior to enrollment can be substituted.


Core Courses

GNET 710 - BCB Colloquium - Required for First 4 Semesters

GNET 711 - Applications of Information Theory, Grammars, Genetic Programming, and Neural Networks to Sequence Analysis.
Instructor: Giddings (Microbiology & Immunology)

This module covers applications of several commonly-used methods to understand sequence structure and function at the DNA and RNA level. Topics covered include Information theory and Kolmogorov complexity, Hidden Markov Models, Stochastic Grammars, Genetic Programming, and Neural Networks, applied to problems such as gene finding, identification of regulatory elements, and RNA structure analysis.

GNET 712 - Databases, Metadata, Ontologies, Digital Libraries for Biological Sciences
Instructor: Hemminger (Information and Library Science)

This module covers the basic information science elements of methods for storage and retrieval of biological information. Instructors review starndard database types and their applicability to bioinformatics data generated in research laboratories. Students learn the role of metadata and ontologies as standardization mechanisms for providing interoperability between different information resource types such as genetic sequences, microarray maps, and journal articles.

GNET 716– Sequence Analysis
Instructor: Vision (Biology), Gupta (Biostatistics)

This course is designed to introduce students to the computational analysis of nucleic acids sequences, including sequence comparison, alignment, and assembly.

GNET 717 – Structural bioinformatics
Instructor: Tropsha (Pharmacy), Kuhlman (Biochemistry & Biophysics)

This course introduces methods and techniques for protein modeling including structure determination, protein architecture, approaches to folding simulations, structure prediction, and structure based drug design.

GNET 713 – Data mining and clustering of biological information
Instructor: Wang (Computer Science)

This course covers methods of knowledge extraction (association rules, pattern recognition, clustering, classification, prediction) from complex biological data sets.

GNET 714  – Biostatistics in bioinformatics and computational biology
Instructors: Wright, Zou, Gupta (Biostatistics)
This course is intended to introduce statistical concepts as commonly used and applied to problems in gene mapping and gene expression analysis.

GNET 715 - Mathematical and computational approaches to modeling signaling and regulatory pathways
Instructor: Elston (Pharmacology)

The course will provide an introduction to the basic mathematical techniques used to develop and analyze models of biological processes that occur at the cellular and molecular level. Both deterministic and stochastic models will be discussed.

Two graduate level electives in a related topic are also required.