This page provides some thoughts and ideas on how to prepare for a career in computational biology. Because computational biology is a new and rapidly evolving field, it is to a significant extent whatever you decide to make of it, and consequently the ideas presented here may or may not be directly applicable to your own interests and ambitions.
Computational biology is a very broad field that lies at the intersection of two large and rapidly changing disciplines, biology and computer science. Thus work in computational biology might range from analysis of genomic sequences to visualizing the activity of an animal's nervous system or modeling the responses of plants to changing environmental conditions. Because of the great success of genome-sequencing projects, the quantity of DNA sequence data that are now available greatly exceeds the tools that are available to process those data. Consequently the analysis of those data presents one of today's great scientific challenges.
Bioinformatics is a subdiscipline of computational biology that focuses on the information-processing associated with biological data, particularly the DNA sequences acquired in genome sequencing projects. This field involves developing and using tools that permit the semi-automatic analysis of very large datasets such as those that are acquired in the process of sequencing complete genomes and other high-throughput sequencing studies. Computational methods that are used in the interpretation of data from high-throughput hybridization (DNA array) and protein-analysis (proteomic) studies are also considered to be a part of bioinformatics, in part because they involve the analysis of large quantities of data, and in part because of their close relationship to genomic studies.
A great source on education in computational biology (particularly for graduate and postdoctoral education) is the International Society for Computational Biology (ISCB) web site.
Undergraduates wishing to explore carreers in computational biology might want to consider the following classes:
Biological Sciences
Life Sciences - Fundamental
BSCI 105 (4) Principles of Biology I
BSCI 106 (4) Principles of Biology II
BSCI 222 (4) Principles of Genetics
BSCI 230 (4) Cell Biology and Physiology
One of: BSCI 223-227 (4) Biological Diversity
CHEM 103 (4) General Chemistry I
CHEM 113 (4) General Chemistry II
CHEM 233 (4) Organic Chemistry I
CHEM 243 (4) Organic Chemistry II
Life Sciences - Upper Division (One or more of the following)
BSCI 370 (3) Principles of Evolution
BSCI 410 (3) Molecular Genetics
BSCI 411 (3) Plant Genetics and Molecular Biology
BSCI 412 (4) Microbial Genetics
BSCI 413 (3) Recombinant DNA
BSCI 414 (3) Recombinant DNA laboratory
BSCI 415 (2) Plant Biotechnology
BSCI 416 (3) Biology of the Human Genome
BCHM 461 (3) Biochemistry III
Computational Biology
Math and Computer Science
Math and CS - Fundamental
Math 140 (4) Calculus I
Math 141 (4) Calculus II
Math 240 (4) Introduction to Linear Algebra
(or Math 461 (3) Linear Algebra for Scientists and Engineers)
STAT 400 (3) Applied Probability and Statistics
CMSC 106 (4) Introduction to C programming
CMSC 114 (4) Computer Science I
CMSC 214 (4) Computer Science II
CMSC 250 (3) Discrete Structures
CMSC 251 (3) Algorithms
Math and CS - Upper Division
Math 240 (4) Linear Algebra
Math 461 (3) Linear Algebra for Scientists and EngineersCMSC 420 (3) Data Structures
CMSC 424 (3) Intro to Web Databases [Proposed Course]
Also of Interest
PHYS 141 (4)
PHYS 142 (4)PBIO 699K (3) Molecular Systematics
ENTM 622 (3) Principles of Systematic Entomology
Graduate students and practicing scientists wishing to improve their skills in genome bioinformatics might want to consider the intensive course offered at Cold Spring Harbor Labs: http://stein.cshl.org/genome_informatics/