Relevant Courses

Ph.D. students must take some of the required courses in pairs in order to satisfy the statistics requirement (e.g., STATS 425 & 426 or STATS 601 & 602). If a student takes only 1 of the 2 courses, that does not satisfy the program requirement; the student must take – and receive a passing grade in - both.

Introductory Bioinformatics

BIOINF-527 Introduction to Bioinformatics & Computational Biology

Computing and Informatics

BIOINF-575 Prog Lab in Bioinf
EECS-402 Computer Programming For Scientists & Engineers
EECS-587 Parallel Computing

Probability & Statistics  

BIOSTAT-553 Appl Biostatistics
BIOSTAT-601 Prob&Distrib Theory
STATS-412 Intro Prob Stat
STATS-425 Intro Probabil
STATS-500 Applied Stat I

Molecular Biology  

BIOINF-523* Bioinformatics Basic Biology Lab
BIOLCHEM-515 Intro Biochem
BIOLCHEM-516 Intro Biochem Lab
BIOLCHEM-550 Macromol Struc&Func
BIOPHYS-608 / MCDB-608/ PHYSICS-608 Biophysical Principles of Microscopy
CDB-530 Cell Biology
HUMGEN-541 Gene Structur&Regul
HUMGEN-542 Molec Basis HG Dis
MCDB-427 Molecular Biology
MCDB-428 Cell Biology
MICRBIOL-553 / PATH-553 Cancer Biology
NEUROSCI-602 Princ Neurosc II
PHYSIOL-502 Human Physiology
PHYSIOL-555 Integrative Genomics

Advanced Bioinformatics & Computational Biology

BIOINF-463 Mathematical Modeling in Biology
BIOINF-520/ PHYSIOL-520 Computational Systems Biology in Physiology
BIOINF-528 Bioinformatics and Computational Modeling of Proteins
BIOINF-545 High-throughput Molecular Genomic and Epigenomic Data Analysis
BIOINF-547 Probabilistic Modeling in Bioinformatics
BIOINF-551 Proteome Inf
BIOINF-563 Advanced Mathematical Methods for Biological Sciences
BIOINF-665/ BIOSTAT-665/ HUMGEN-665 Statist Popul Genet
BIOINF-800 / MATH-559 Computational and Mathematical Neuroscience
BIOSTAT-615 Statistical Computing
BIOSTAT-666 Num Meth Hum Gen
BIOSTAT-830 Advanced Topics in Biostatistics
CMPLXSYS-510 / MATH-550 Adaptive Dynamics: The mathematics of sustainability
CMPLXSYS-530 Computer Modeling
MATH-559 Computational Neuroscience
STAT-710 Special Topics in Theoretical Statistics I

Electives

BIOLCHEM-602/ BIOPHYS-602/ CHEM-602 Protein Crystallography: Principles of Macromolecular Crystallography
BIOPHYS-521 Biophysical Chemistry II
BIOINF-555 Current Concepts & Case Studies in Heath Informatics
BIOINF-668 Introduction to Health Informatics
BIOPHYS-520/CHEM-520 Biophysical Chemistry I
BIOPHYS-602/CHEM-602 Protein Crystallography
BIOSTAT-602 Biostat Inference
BIOSTAT-640 Exploratory Data Analysis (Offered irregularly)
BIOSTAT-866 Adv Topc Gen Model
CHEM-535 Physical Chemistry of Macromolecules
CHEM-570 Molecular Phys Chem
CHEM-576 Stat Mechanics
CMPLXSYS-501 Intro Complex Syst
CMPLXSYS-511 Theory of Complex Systems
EECS-477 Introduction to Algorithms
EECS-484 Database Mgt Syst
EECS-486 Object-Oriented Software Development
EECS-493 User Interface Development
EECS-543 Knowledge-Based Systems
EECS-545 Machine Learning
EECS-584 Advanced Database Systems
EECS-586 Algorithms
EECS-684 Knowledge-Based Systems (Even Years)
EECS-595 / LING-595/ SI-595 Natural Language Processing
MATH-571 Num Meth Sci Comp I
MATH-526 / STATS-526 Disc Stoc Proc
MATH-547 / SI-547 / STATS-547 Probabilistic Modeling in Bioinformatics
MATH-571 Num Meth Sci Comp I
MCDB-411 Protein Structure and Function
PATH-581 Tissue, Cellular and Molecular Basis of Disease
PHCHEM-758 Methods of Computational Chemistry
PHYS-510 Statistical Mechanics
SI-654 Special Topics: Database Application Design
SI-658 Info Architecture
SI-666 Org of Inf Resources
STATS-426 Introduction to Theoretical Statistics
STATS-503 Applied Multivariate Analysis
STATS-606 Statistical Computing

Seminars / Discussions

BIOINF-601 Bioinformatics Seminar discussion
BIOINF-602 Journal Club

Related Bioinformatics Courses

BIOINF-501 Introduction to Mathematics for the Life Sciences & Informatics
BIOINF-525 Foundations in Bioinformatics & Systems Biology

*BIOINF 523 prepares students with no biology background to take graduate level classes in biology. It alone is not sufficient to fulfill the Bioinformatics program requirements in molecular biology.