The required coursework is designed with three tiers of formal training: foundational (introductory) courses, core modules, and electives. Since incoming students come from a broad range of disciplines (e.g., math, computer science, biology, genetics, statistics), 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.
Incoming students will consult with their first-year advisors in the BBSP first year group as well as the BCB Student Progression Director to select one or two introductory-level courses that address any specific deficiencies in their backgrounds. For students who do not require any foundational classes, these credit hours can be taken as additional electives to provide more in-depth training.
During their first year, most BCB students will also take six core modules, two in the first semester and four in the second semester. Students will be expected to demonstrate competence in all six subject areas covered by the modules for the written qualifying exam. Each module is a short course designed to address a specific topic in bioinformatics and computational biology. Names and brief descriptions of each module are listed below.
Since bioinformatics and computational biology encompass diverse fields of study, students are required to take two electives to receive specialized, in-depth training in mathematical, computational, physical, or biological sciences to enhance their specific research interests. All electives must be approved by the student’s thesis advisor and the BCB Student Progression Director.
Core BCB modules (1 to 3 credit hours each)
BCB 712 – Databases, metadata, ontologies, and digital libraries for biological sciences
Course Description: This module introduces the basic information-science methods for storage and retrieval of biological information. Instructors review standard 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.
Instructor: Bradley M. Hemminger
BCB 715 – Mathematical and computational approaches to modeling signaling and regulatory pathways
This module provides an introduction to the basic mathematical techniques used to develop and analyze models of biochemical networks. Both deterministic and stochastic models are discussed.
Instructor: Jeremy Purvis or Tim Elston*
BCB 716 – Sequence Analysis
This module is designed to introduce students to concepts and methods in the comparative analysis of nucleic acid and protein sequences, including sequence alignment, homology search, phylogenetics and genome assembly.
Instructors: Todd Vision and Zefeng Wang
BCB 717 – Structural bioinformatics
This module introduces methods and techniques for protein modeling including structure determination, protein architecture, approaches to folding simulations, structure prediction, and structure based drug design.
Instructor: Brian Kuhlman
BCB 720 – Introduction to Statistical Modeling
The module will introduce foundational statistical concepts and models that motivate a wide range of analytic methods in bioinformatics, statistical genetics, statistical genomics, and related fields.
Instructors: William Valdar and Ethan Lange
BCB 722 – Foundations of Population Genomics
This course will cover the fundamental principles of population genomics. We will address such questions as: What are the evolutionary forces that have shaped the genetic diversity we see today? Can we distinguish one such force from another? Can we estimate their relative strengths? Is any of this relevant for studying the role of genetics in complex disease? The goal of the course is to equip students with foundational knowledge in the theory of evolution, which serves as the bedrock of modern biology, and to demonstrate its relevance to modern--day genetic applications in biomedicine.
Instructor: Praveen Sethupathy