Course Descriptions

Below are courses offered by the Bioinformatics Program. Information about these or any other courses can be found in the course catalog via Wolverine Access.

BIOINF-463: Mathematical Modeling in Biology

Credits: 3
Category:Advanced Bioinformatics and Computational Biology

Offered Fall term.

An introduction to the use of continuous and discrete differential equations in the biological sciences. Modeling in biology, physiology and medicine.

BIOINF-501: Mathematical Foundations for Bioinformatics 

Credits: 3
Category: Elective
Offered Fall Term.
Pre-req: Calc II or equivalent

The course provides a review of some of the fundamental mathematical techniques commonly used in bioinformatics and biomedical research. These include: 1) principles of multi-variable calculus, and complex numbers/functions, 2) foundations of linear algebra, such as linear spaces, eigen values and vectors, singular value decomposition, spectral graph theory and Markov chains, 3) differential equations and their usage in biomedical system, which includes topic such as existence and uniqueness of solutions, two dimensional linear systems, bifurcations in one and two dimensional systems and cellular dynamics, and 4) optimization methods, such as free and constrained optimization, Lagrange multipliers, data denoising using optimization and heuristic methods. MATLAB, R and Python will be introduced as tools to simulate/implement the mathematical ideas.

BIOINF-520: Computational Systems Biology in Physiology

Credits: 3
Category: Advanced Bioinformatics

This course is an introduction to dynamic modeling in physiology for both experimental and theoretical inclined students. We use selected physiological systems to introduce concepts in computational systems biology. This is done through the use of increasingly more complex cellular functions modeled with scientific software.

BIOINF-523: Bioinformatics Basic Biology Lab

Credits: 2

Introduces basic biology to graduate students without any prior college biology. Geared towards students pursuing training in Bioinformatics or Biostatistics who have quantitative training (computer science, engineering, mathematics, statistics, physics). After a brief introduction to organic and biochemistry, boot camp will have lectures on molecular biology, cell biology and laboratory tools used in both, as well as introductory molecular biology laboratory experiments.

*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.

BIOINF-524: Foundations for Bioinformatics

Credits: 3
Category: Related Bioinformatics Courses
Offered Winter term.

This course provides an introduction to the principles and practical approaches of bioinformatics as applied to genes and proteins. The overall course content is broken down into 3 sections focusing on foundational information, statistics, and systems biology, respectively.

This course is a meet-together with BIOINF-525. Please read the 525 course description for further details.

BIOINF-525: Foundations in Bioinformatics and Systems Biology

Category: Related Bioinformatics Courses
Offered Winter term.
Syllabus (PDF)

This course is comprised of three modules. Each module is 1 credit hour; a student may register for any one or all three modules. Alternately, a student may select BIOINF 524 (3 cr. hrs.) which is registration for all modules in the course.

Who should take this course?

  • This course is ideal for first year PIBS students or second year students with little experience in bioinformatics. Descriptions for each module are as follows:

Introductory Programming and Exploratory Data Analysis

  • This module provides an introduction to practical issues of computer-based handling and interpretation of biomolecular and genomic datasets. We specifically target bioinformatics software and data resources freely available on the Internet.

Statistics in Bioinformatics

  • Basic statistics as used in bioinformatics, especially standard statistical tests of significance and when they apply, is the focus for this module. Applications to genetics, experimental and observational medical data, as well as exploration of multiple testing issues that arise in bioinformatics and other experimental settings are also addressed. 

Biological Networks and Systems-level Modeling

  • This section concentrates on computational analysis of biological networks, OMICs data (genomics, transcriptomics, metabolomics, proteomics). Additional topics include: Application of advanced analysis and modeling approaches to study pathways and networks, with an emphasis on using existing high throughput data sets alongside newly generated data to analyze and interpret research findings.

BIOINF-527: Introduction to Bioinformatics & Computational Biology

Category: Non-major courses in Bioinformatics/Introductory Bioinformatics

Offered Fall term.

This course introduces students to the fundamental theories and practices of Bioinformatics and Computational Biology via a series of integrated lectures and labs. These lectures and labs will focus on the basic knowledge required in this field, methods of high-throughput data generation, accessing public genome-related information and data, and tools for data mining and analysis. The course is divided into four areas: Basics of Bioinformatics, Computational Phylogeny (includes sequence analysis), Systems Biology and Modeling. There will be weekly homework, two take-home exams, and students will prepare and present group projects.

If any questions, please contact the Course Director, Prof. Stephen Guest (

BIOINF-528: Structural Bioinformatics

Credits: 3
Category: Advanced Bioinformatics and Computational Biology
Instructor: Prof. Yang Zhang

Offered in Fall
Fridays, 9:00 am - 12:00 noon
Rm. 2036 Palmer Commons Bldg.

This course introduces fundamental concepts and methods for structural bioinformatics and the advanced applications. Topics covered include sequence, structure and function databases of DNA and protein molecules, advanced sequence and structure alignment methods, methods of protein folding and protein structure prediction (homologous modeling, threading and ab initio folding), basics of molecular dynamics and Monte Carlo simulation, principle and application of machine learning, and techniques of protein structure determination (X-ray crystallography, NMR and cryo-EM). Emphasis is on the understanding of the concepts taught and the practical utilization, with the objective to help students to use the cutting-edge bioinformatics tools/methods to solve problems in their own research. For this term, top experts are invited to give lectures on mass spectrometry and proteomics (Prof. Philip Andrews), NMR spectroscopy (Prof. Tomek Cierpicki), Cryo-electron microscopy (Prof. Melanie Ohi), and X-ray crystallography (Prof. Mark Saper).

BIOINF-529: Bioinformatics Concepts and Algorithms

Credits: 3
Category: Introductory Bioinformatics

Instructors: Alan Boyle and Ryan Mills
Offered Winter term.

This course introduces Bioinformatics Program students to common topics in bioinformatics as well as corresponding computational approaches in those areas. Students will learn how to implement and apply various algorithms and statistical models to solve challenging problems and will also build a foundation for developing tools for future technologies.

BIOINF-540: Mathematics of Biological Networks

Credits: 3
Instructor: Indika Rajapakse

Offered every Fall term.
Syllabus (PDF)

This course addresses methods and principles involved in constructing and studying the structure and function of biological networks using examples from real datasets. The course is structured so that any necessary background will be introduced as needed. A comprehensive website containing all reading materials and class notes will be maintained throughout the term.

BIOINF-545: High-throughput Molecular Genomic and Epigenomic Data Analysis

Credits: 3
Category: Advanced Bioinformatics and Computational Biology

Prerequisites: Graduate Standing and STATS 400, BIOSTAT 523, BIOSTAT 553 or equivalent (or permission of instructor)

The course will cover basic analysis of microarrays, RNA-Seq, and ChIP-Seq data including hands-on lab sessions. The class also covers an introduction to the underlying biology and the technologies used for measuring RNA levels, transcription factor binding and epigenetic modifications, and quality control of microarray and deep sequencing data. Topics: technologies, experimental design, data preprocessing, normalization, quality control, statistical inference (group comparisons, peak detection), multiple comparison adjustments, power calculations, clustering, functional enrichment testing.

BIOINF-547: Probabilistic Modeling in Bioinformatics

Credits: 3
Category: Advanced Bioinformatics and Computational Biology 

Instructor: Dan Burns, Indika Rajapakse

This course will review some classical problems in DNA sequence analysis, problems such as multiple sequence alignment, protein families and parsing the linear structure of protein coding gene sequences, and then proceed to more recent epigenetic and structural features of DNA. We will discuss how recent mathematical methods used to describe protein folding can be applied to understanding chromatin dynamics. This course will furthermore detail quantitative and experimental techniques used to define the multi-dimensional genome, and how to integrate information from multiple methodologies into a framework for understanding genome dynamics. These principles can be applied to the analysis of high-dimensional biological data.

BIOINF-551: Proteome Informatics

Credits: 3
Category: Advanced Bioinformatics and Computational Biology

Offered Fall term, every other year.

Introduction to proteomics, mass spectrometry, peptide identification and protein inference, statistical methods and computational algorithms, post-translational modifications, genome annotation and alternative splicing, quantitative proteomics and differential protein expression analysis, protein-protein interaction networks and protein complexes, data mining and analysis of large-scale data sets, clinical applications, related technologies such as metabolomics and protein arrays, data integration and systems biology.

BIOINF-563: Advanced Mathematical Methods for the Biological Sciences

Credits: 3
Category: Advanced Bioinformatics and Computational Biology

Offered Winter term.

This course focuses on discovering the way in which spatial variation influences the motion, dispersion, and persistence of species. Specific topics may include i) Models of Cell Motion: Diffusion, Convection, and Chemotaxis; ii) Transport Processes in Biology; iii) Biological Pattern Formation; and iv) Delay-differential Equations and Age-structured Models of Infectious Diseases.

BIOINF-568: Mathematics and Computational Neuroscience

Credits: 3
Category: Advanced Bioinformatics and Computational Biology

Offered Fall term, alternate years.
Instructor: Victoria Booth

Computational neuroscience provides a set of quantitative approaches to investigate the biophysical mechanisms and computational principles underlying the function of the nervous system. This course introduces students to mathematical modeling and quantitative techniques used to investigate neural systems at many different scales, from single neuron activity to the dynamics of large neuronal networks.

BIOINF-575: Programming Laboratory in Bioinformatics

Category: Computing and Informatics

Monday/Wednesday 10:00 a.m. - 11:30 a.m.
Tuesday/Thursday 10:00 a.m. - 11:30 a.m.
Palmer Commons 2036

BIOINF 575 introduces the principles and application of general computer programming, relational databases, and statistical programming as tools to solve problems in bioinformatics data analysis. General programming is taught using the object oriented language Python and statistical programming and graph generation is introduced in R. The relational database language SQL is taught in conjunction with database design, construction and querying. Packages that extend the capabilities of Python are explored, and in the past have included topics such as high performance numerical computing in Numpy, XML parsing, database connectivity, and advanced graphics. A stress is placed on integrating Python, SQL, and R with other programs to build data processing pipelines or other tools. Grades are based on programming exercises, participation in class discussions, and cooperative development of group projects.

Prerequisites: Some familiarity with programming concepts is recommended, but motivated students with knowledge of a bioinformatics application area and a logical approach to problem solving can succeed in this course.

Permission of the instructor required.

BIOINF-580: Introduction to Signal Processing and Machine Learning in Biomedical Sciences

Credits: 3
Category: Advanced Bioinformatics and Computing and Informatics
Offered Winter term

Instructor: Kayvan Najarian

The course covers signal processing and machine learning methods with an emphasis on their applications in healthcare. Students will need a basic understanding in linear algebra for this course. Topics include: 1) transforms and feature entraction – Fourier transform, wavelet transformation, fundamentals of information in theory. 2) Introduction to machine learning – clustering vs classification, Naïve Bayes, Classification and regression trees. Random forest, support vector machines, introduction to neural networks, and sparse learning. 3) applications in medicine and biology.

BIOINF-585: Deep Learning in Bioinformatics

Category: Advanced Bioinformatics and Computing and Informatics

Instructor: Yuanfang Guan

E-mail Prof. Guan ( with the following:

  • Name
  • Program
  • Year
  • UMID #
  • A sample piece of code in Python

BIOINF 585 is a project-based course focused on deep learning and advanced machine learning in bioinformatics. The course will be comprised of deep learning and some other traditional machine learning in applications including regulatory genomics, health records, and biomedical images, and computation labs. The course is project-based, including two in-class projects and one at-home project, aimed at generating publication-quality reports.


BIOINF-602: Journal Club

Credits: 1
Category: Seminars/Discussions

Mondays, 12:00 p.m. – 1:00 p.m.
Rm. 2036 Palmer Commons Bldg.
Fall and Winter terms

Bioinformatics Journal Club entails a weekly discussion of current and classic papers concerning biology on a whole-genome scale, or using genome sequence based approaches. It is a great opportunity for students and researchers to be exposed to current topics of Bioinformatics. Although the presentations are on a volunteer basis, participants are encouraged to present. Each week's paper is chosen by the presenter a week in advance. Journal Club is open to anyone interested in participating.This course is for first-year students who have not taken a journal club before. No presentation is required.

BIOINF-603: Journal Club

Many areas of biology and medicine now involve massive quantities of data or physical analysis; analysis of such data is considered fundamental for the advancement of biomedical science. In this journal club readings and discussion of current research literature acquaint students with biological quantitative methods and research questions being applied to new data. Students presents and discuss papers, plus critique publication and presentation. Registered students must present background and discuss in detail articles focused on emerging topics and questions related to bioinformatics.

BIOINF-606: Introduction to Bio-computing

Credits: 1
Category: Bioinformatics courses for non-majors
Offered each year in August
Course directors: Ryan Mills
Syllabus (PDF)

This hands-on one week course introduces new graduate students, with little to no formal UNIX or programming training, to computational tools, techniques and best practices that foster reproducible research in bioinformatics, genome informatics and biostatistics.

Major concepts and tools covered include the UNIX system, version control, data management, software compilation, task automation and cluster computing. Participants will be encouraged to help one another and to apply what they have learned to their own research problems. Our tools of choice will be Python (for programming), R (for data analysis), Git (for version control), and PBS (for cluster resource management). However, lessons learned should be widely applicable for those looking to incorporate more productive computational approaches into their daily research work.

Sessions will be held in 3755 SPH1 from 9:00AM to 4:00PM with a short lunch break and will consist of interactive lectures with concurrent hands-on analysis. Participants will be required to bring their own laptop. For further information, please contact Dr. Ryan Mills (

BIOINF-665: Statistical Population Genetics

Category: Advanced Bioinformatics and Computational Biology
Offered Winter term, alternate years

Advanced course in population genetics, focusing on mathematical models and statistical models for data analysis. Topics include infinite and finite population phenomena, population structure, admixture, mutation models, coalescent methods, recombination, and linkage disequilibrium.