Joint Biology and Computer Science Track in Bioinformatics



Bioinformatics is an emerging discipline that integrates molecuar biology, genetics, biochemistry and computer science. It provides tools that allow scientists to make use of the vast amount of biological data generated in recent years and to answer questions unimaginable just a decade ago. Bioinformatics has revolutionized gene discovery and analysis, and offers excellent career opportunities for those who master it.

Link to companies and institutions in the Bay Area.

The departments of Biological Sciences and Computer Science at San Jose State University designed a new BSCS Track in Bioinformatics.

Prerequisite Courses

Molecular Biology for Computer Scientists
Biology 23
Offered in Spring 2008
Tuesdays and Thursday: 2:30 - 3:45, DH 415. Instructor: Dr. Brandon White
The aim of this course is to help computer science students become familiar with the principles of molecular biology as they relate to bioinformatics. The emphasis will be on the molecular workings of a cell, including the fundamental processes by which coded genetic information is decoded during the production of useful macromolecular structures and machines. The course is a prerequisite for CS students who want to take Bioinformatics I, Bio/CS 123A, to be offered in Fall 2008.
Prerequisites: CS 46A and CS 46B; No college biology required.


Introduction to Computer Science for Biologists and Chemists
CS 23
Not offered in Spring 2008
This course is an introduction to computer science topics needed to enter the field of bioinformatics and is open to Biology/Chemistry students. Simple C and Perl programming in a Unix environment and basic database access techniques will be covered. The course is an in-lab course and serves as the prerequisite for a joint Bio/CS 123A course.
Prerequisites: Bio 5 or Chem 55, or instructor approval; no prior knowledge of computer programming required.

Required Courses

Bio/CS 123A
Next time offered: Fall 2008
Instructor: Dr. Sami Khuri
This practical course, cross-listed with the biology department, provides an introduction to the main public domain tools, databases and methods in bioinformatics, including DNA and protein databases, such as Genbank and PDB, software tools such as BLAST, FASTA, Smith-Waterman; and methods including those for aligning sequences. More precisely, the course analyzes the algorithms behind the most successful tools, such as the local and global sequence alignment packages mentioned above; and the underlying methods used in fragment assembly packages. This course is intended both for biology and computer science students, to work together to solve complex biological questions requiring modification of standard code.
Prerequisites: Bio 23 for Computer Science students or instructor approval.


Bio/CS 123B
Offered in Spring 2008.
Tuesdays and Thursday: 9:00 - 10:15, DH 450.
Instructor: Dr. Sami Khuri
This practical course, cross-listed with biology, continues to cover the computational methods used for searching, classifying, analyzing, and modeling protein sequences. The course also continues to cover tools for analyzing DNA and RNA sequences. More advance topics, such as genetic algorithms and simulated annealing which can be used to address folding problems, are covered.
Prerequisites: Bio/CS 123A or instructor approval.

Overview of the program

Additional Courses of Interest

Advanced Programming with PERL
CS 122
Offered in Spring 2008
Tuesdays and Thursday: 10:30 - 11:45, DH 450. Instructor: Natasha Khuri
This course introduces the Perl programming language with an emphasis on data manipulation, file processing and database access. The course will deal with real life applications in various fields such as system administration, networking and bioinformatics.
Prerequisites: CS146 or by permission of the instructor.


Statistics for Bioinformatics
Math 162
Offered in Spring 2008
Tuesdays and Thursday: 7:00 - 8:15 pm, MH 320. Instructor: Dr. Martina Bremer
Introduction to data analysis methods which are widely used in bioinformatics. Methods deal with prediction, classification, optimization, and clustering. The methods are placed into the context of principles and models of statistical science, with emphasis on Bayesian methods.
Prerequisite: Math 161A or instructor consent.

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