- Graduates will be able to design and implement in silico experiments for biological problems.
- Graduates will be able to apply and combine existing tools for processing and analysis of biological data sets.
- Graduates will be able to use small- and large-scale quantitative data sets to model complex biological systems.
- Graduates will be able to work as part of multidisciplinary teams in corporate or academic environments.
- Graduates will be able to effectively communicate research approaches and findings.
Code | Title | Credits |
---|---|---|
Required Courses | ||
BCB 5200 | Introduction Bioinformatics I | 3 |
BCB 5250 | Introduction Bioinformatics II | 3 |
BCB 5300 | Algorithms in Computational Biology | 3 |
BCB 5810 | Bioinformatics Colloquium | 1 |
BIOL 5030 | Genomics | 3 |
Internship/Research Experience | 1-3 | |
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 Electives | 14-16 | |
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 Credits | 30 |
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.
Year One | ||
---|---|---|
Fall | Credits | |
BCB 5200 | Introduction Bioinformatics I | 3 |
BIOL 5030 | Genomics | 3 |
Credits | 6 | |
Spring | ||
BCB 5250 | Introduction Bioinformatics II | 3 |
BCB Electives | 6 | |
Credits | 9 | |
Summer | ||
BCB 5910 | Bioinformatics Internship | 2 |
Credits | 2 | |
Year Two | ||
Fall | ||
BCB 5300 | Algorithms in Computational Biology | 3 |
BCB 5810 | Bioinformatics Colloquium | 1 |
BCB Elective | 3 | |
Credits | 7 | |
Spring | ||
BCB Electives | 6 | |
Credits | 6 | |
Total Credits | 30 |
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