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Bioinformatics and Computational Biology, M.S.

Students sit at long rows of desks in a classroom with laptops in front of them while an instructor points to a presentation screen.
  1. Graduates will be able to design and implement in silico experiments for biological problems.
  2. Graduates will be able to apply and combine existing tools for processing and analysis of biological data sets.
  3. Graduates will be able to use small- and large-scale quantitative data sets to model complex biological systems.
  4. Graduates will be able to work as part of multidisciplinary teams in corporate or academic environments.
  5. Graduates will be able to effectively communicate research approaches and findings.
Required Courses
BCB 5200Introduction Bioinformatics I3
BCB 5250Introduction Bioinformatics II3
BCB 5300Algorithms in Computational Biology3
BCB 5810Bioinformatics Colloquium1
BIOL 5030Genomics3
Internship/Research Experience1-3
Select one of the following:
BCB 5910
Bioinformatics Internship
BCB 5970
Research Topics
BIOL 5970
Research Topics
CHEM 5970
Research Topics
CSCI 5970
Research Topics
Bioinformatics & Computational Biology Electives14-16
Select remaining courses to reach 30 credits:
BIOL 5050
Molecular Techniques Lab
BIOL 5070
Advanced Biological Chemistry
BIOL 5080
Advanced Cell Biology
BIOL 5090
Biometry
BIOL 5190
Geographic Information Systems in Biology
BIOL 5430
Advanced Principles of Virology
BIOL 5520
Biochemical Pharmacology
BIOL 5700
Advanced Molecular Biology
BIOL 5780
Molecular Phylogenetic Analysis
BME 5130
Medical Imaging
BME 5150
Brain Computer Interface
CHEM 5370
Computational Chemistry
CHEM 5470
Medicinal Chemistry
CHEM 5610
Biochemistry 1
CHEM 5615
Biochemistry 2
CHEM 5620
Biophysical Chemistry
CSCI 5030
Principles of Software Development
CSCI 5300
Software Engineering
CSCI 5360
Web Technologies
CSCI 5610
Concurrent and Parallel Programming
CSCI 5620
Distributed Computing
CSCI 5710
Databases
CSCI 5730
Evolutionary Computation
CSCI 5740
Introduction to Artificial Intelligence
CSCI 5750
Introduction to Machine Learning
CSCI 5760
Deep Learning
CSCI 5830
Computer Vision
HDS 5310
Analytics and Statistical Programming
HDS 5330
Predictive Modeling and Machine Learning
MATH 5021
Introduction to Analysis
MATH 5023
Multivariable Analysis
MATH 5080
Probability Theory
STAT 5084
Time Series
STAT 5085
Mathematical Statistics
STAT 5087
Applied Regression
STAT 5088
Bayesian Statistics and Statistical Computing
Total Credits30

Continuation Standards

Students must maintain a cumulative grade point average (GPA) of 3.00 in all graduate/professional courses.

Prerequisite Courses

The following courses may be required to fill in missing prerequisite coursework, such as data structures. These prerequisite courses do not count toward the 30 credits needed for graduation.

  • General Biology: Information Flow and Evolution (BIOL 1240)/Principles of Biology I Laboratory (BIOL 1245)
  • General Biology: Transformations of Energy and Matter (BIOL 1260)/Principles of Biology II Laboratory (BIOL 1265))
  • General Chemistry 1 (CHEM 1110)/General Chemistry 1 Laboratory (CHEM 1115)
  • General Chemistry 2 (CHEM 1120)/General Chemistry 2 Laboratory (CHEM 1125))
  • Biochemistry and Molecular Biology (BIOL 3020) or Cell Structure & Function (BIOL 3040)
  • Principles of Genetics (BIOL 3030)
  • Introduction to Object-Oriented Programming (CSCI 1300)
  • Data Structures (CSCI 2100)
  • Calculus I (MATH 1510)
  • MATH 1300X Elementary Statistics with Computers (3 cr)Foundation of Statistics (MATH 3850) or Mathematical Statistics (MATH 4850)

Students may complete these prerequisites as part of the program, but the courses will not count toward the 30 credits required for the degree.

Roadmaps are recommended semester-by-semester plans of study for programs and assume full-time enrollment unless otherwise noted.  

Courses and milestones designated as critical (marked with !) must be completed in the semester listed to ensure a timely graduation. Transfer credit may change the roadmap.

This roadmap should not be used in the place of regular academic advising appointments. All students are encouraged to meet with their advisor/mentor each semester. Requirements, course availability and sequencing are subject to change.

Plan of Study Grid
Year One
FallCredits
BCB 5200 Introduction Bioinformatics I 3
BIOL 5030 Genomics 3
 Credits6
Spring
BCB 5250 Introduction Bioinformatics II 3
BCB Electives 6
 Credits9
Summer
BCB 5910 Bioinformatics Internship 2
 Credits2
Year Two
Fall
BCB 5300 Algorithms in Computational Biology 3
BCB 5810 Bioinformatics Colloquium 1
BCB Elective 3
 Credits7
Spring
BCB Electives 6
 Credits6
 Total Credits30

Apply Now

For questions about the program or application process, please contact:

Maureen J. Donlin, Ph.D.
Program Director
maureen.donlin@health.slu.edu

Graduate Admission
graduate@slu.edu