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-500: Success Skills in Bioinformatics 

 Credits: 1

Category: Seminars/Discussion 

 Offered Fall term.

Restricted to incoming Bioinformatics students only.  

This course covers topics to help incoming Bioinformatics graduate students succeed and immerses students into the department. Topics include finding a mentor, a bioinformatics research area, and career path; using library, computational, and funding resources; writing papers and student grants. 

 

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-504: Rigor and Transparency to Enhance Reproducibility

Credits: 1

Category: Basic Skills and Rigor
Offered Fall term as 1 week workshop in late August.
Pre-req: programming skills and 1 year of graduate courses
Syllabus (PDF)

This course fulfills the new NIH requirements for rigor & reproducibility. It covers how to carry out rigorous, transparent, and reproducible computational biomedical research. Specific topics include developing a rigorous study design, data quality control and processing, rigor & transparency for code and software, following the FAIR principles, and dissemination of data and software.

 

BIOINF-520: Computational Systems Biology in Physiology 

Credits: 3

Category: Advanced Bioinformatics 
Syllabus (PDF) 

This course provides an introduction to mathematical and computational modeling for both experimentally and theoretically inclined students, as well the currently employed strategies to investigate physiological problems with computational modeling. In our course, we select important physiological problems whose solution will involve some useful computational modeling. After briefly discussing the required scientific background, we formulate a relevant computational problem with some care. The formulation step is often difficult. Not many courses or textbooks actually demonstrate this. In our course, we plan to give due emphasis to the challenges involved in constructing computational models. The goals of this approach is empower student to build their own models, and become effective performers of systems and computational physiology research.

 

BIOINF-523: Introductory Biology for Computational Scientists 

Credits: 3

Offered Fall term. 

Introduces basic biology to graduate students without any prior college biology. Geared towards students in Bioinformatics, Biostatistics, or other computational fields who have quantitative training (computer science, engineering, mathematics, statistics, etc.). Will cover major topics related to biomedical research including: organic and biochemistry, molecular biology, genetics, cell biology, and microbiology.

 

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 sections focusing on foundational information, statistics, and systems biology, respectively.

This course replaces BIOINF 525. 

 

BIOINF-527: Introduction to Bioinformatics & Computational Biology 

Credits:4

Category: Non-major courses in Bioinformatics/Introductory Bioinformatics 

Offered Fall term. 

Lecture Schedule (PDF)
Lab Schedule (PDF)

Students will be introduced to the fundamental theories and practices of Bioinformatics through a series of integrated lectures and labs. A broad range of topics will be covered illustrating how bioinformatics is shaping the modern landscape of biomedical research. Students develop practical skills for processing, visualizing, and analyzing high-throughput biomedical data.

If any questions, please contact the Course Director, Prof. Stephen Guest ([email protected]).

 

BIOINF-528: Structural Bioinformatics 

Credits: 3

Category: Advanced Bioinformatics and Computational Biology
Offered in Fall

Fridays, 9:00 am - 12:00 noon
Rm. 2036 Palmer Commons Bldg. 
Syllabus (PDF)

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. 
Syllabus (PDF)
2020 Schedule (PDF)

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 explores methods and principles for constructing structure and function of biological networks using real datasets. After introducing basic linear algebra and MATLAB, we will discuss properties of networks, genomics technologies, spectral graph theory, Laplacians (Fiedler number and Fiedler vector), Network inference and controllability, Dynamic Mode Decomposition, Tensor Factorizations. 

 

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

Credits: 3

Category: Advanced Bioinformatics and Computational Biology

Syllabus (PDF) 

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: Mathematics of Data

Credits: 3

Category: Advanced Bioinformatics and Computational Biology 
Instructor: Indika Rajapakse ([email protected], 734-615-3134) 

Offered Winter term.

Tuesday, Thursday 4:00-5:30 PM
4096 East Hall 
Syllabus (PDF)

Prerequisites: Open to upper-level undergraduates and graduate students 

This course is open to graduate students and upper-level undergraduates in applied mathematics, bioinformatics, statistics, and engineering, who are interested in learning from data. Students with other backgrounds such as life sciences are also welcome, provided they have maturity in mathematics. I will start with a very basic introduction to data representation as vectors, matrices (graphs, networks), and tensors. Then I will teach geometric methods for dimension reduction (manifold learning, diffusion maps, t-distributed stochastic neighbor embedding (t-SNE), etc.) and topological data reduction (introduction to computational homology groups, etc.). I will bring an application-based approach to spectral graph theory, address the combinatorial meaning of eigenvalues and eigenvectors of matrices associated with graphs, and discuss extensions to tensors [1, 2]. I will also provide an introduction to the application of dynamical systems theory to data [3, 4]. Real data examples will be given wherever possible and I will work with you to solve these examples. The methods discussed in this class are shown primarily for biological data, but are useful in handling data across many fields. 

Homework and Projects:  We will have homework assignments every two weeks, mini-projects, and a final project in lieu of a final exam.  

  1. Cichocki, A., Zdunek, R., Phan, A. H., & Amari, S. I. (2009). Nonnegative matrix and tensor factorizations: applications to exploratory multi-way data analysis and blind source separation. John Wiley & Sons.
  2. Eldén, L. (2007). Matrix methods in data mining and pattern recognition (Vol. 4). SIAM.
  3. Smale, S. (1980). The mathematics of time (pp. 133-4). New York etc.: Springer.
  4. Strogatz, S. H. (2018). Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering. CRC Press. 

 

BIOINF-551: Proteome and Metabolome Informatics 

Credits: 3

Category: Advanced Bioinformatics and Computational Biology 

Offered Fall term, every other year. 

Introduction to proteomics and metabolomics, 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, targeted and untargeted metabolomics and lipidomics, data mining and analysis of large-scale data sets, clinical applications, 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 

Offered Fall term

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

Programming Laboratory in Bioinformatics --- This course introduces the principles of general computer programming and relational databases as tools to solve problems in bioinformatics data analysis. General programming and graph generation is taught using the object oriented language Python but some variations may occur. The relational database language SQL is taught in conjunction with database design, construction and querying. Packages that extend the capabilities of Python are explored. Grades are based on homework, quizzes, participation in class discussions, and cooperative development of a group project or homework.

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.

BIOINF-576: Tool Development for Bioinformatics

Credits: 3

Offered Winter term

Category: Computing and Informatics, Advanced Bioinformatics and Computing

This class presents and implements all the steps and phases of software development (design, implementation, documentation, issue tracking, peer review, and release). Students can choose an already implemented tool and add a new feature to it or implement a new tool. Students should be familiar with R or python programming language. Instructor permission is required. 

 

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

Credits: 3

Category: Advanced Bioinformatics and Computing and Informatics
Offered Fall term 

Instructor: Kayvan Najarian 

The course covers signal processing, image processing, artificial intelligence (AI) and machine learning (ML) methods with an emphasis on their applications in medicine and biology. Students will need a basic understanding of linear algebra for this course. Topics include: 1) Transforms and feature extraction – Fourier transform, wavelet transformation, fundamentals of information in theory. 2) Introduction to AI and ML – predictive vs generative AI, clustering vs classification, Naïve Bayes, Classification and Regression Trees. Random Forest, Support Vector Machines, introduction to Neural Networks. 3) introduction to conventional image processing methods, 4) Introduction to deep learning methods, 5) Introduction to generative AI, 6) Applications in medicine and biology.

 

BIOINF-585: Deep Learning in Bioinformatics 

Credits:4

Category: Advanced Bioinformatics and Computing and Informatics 

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-590: Image Processing and Advanced Machine Learning for Cancer Bioinformatics

Credits: 3

Category: Advanced Bioinformatics and Computing and Informatics 
Instructor: Arvind Rao

Fall Term

Syllabus (PDF)

This course intends to build on the fundamentals of signal processing and machine learning to explore concepts from these areas in the context of cancer bioinformatics. Motivating examples from cancer genomics, cancer imaging and drug discovery will be used to examine these principles. The course will comprise instructor-led lectures, student lectures, and course projects.

Pre-requisite: BIOINF 580 or instructor consent.

 

BIOINF-593: Machine Learning in Computational Biology

Credits: 3

Category: Advanced Bioinformatics and Computational Biology
Prerequisites: familiarity with multivariate calculus, linear algebra, and probability

This course introduces the foundational machine learning techniques used in computational biology and describes their applications to biological data. The course emphasizes theoretical foundations and practical implementation of the techniques, in addition to the biological background needed for computational biology applications. Expertise in programming, calculus, linear algebra, and probability is required.

BIOINF-597:  Artificial Intelligence for Medicine and Biomedical Sciences

Credits: 3

Category: Advanced Bioinformatics & Computational Biology

BIOINF 597 covers both some of the conventional AI and some of the recently developed deep learning and generative AI methods, beyond those covered in BIOINF 580. We will discuss classic AI methods such as agent-based models and probabilistic methods (such as Hidden Markov Models and Bayesian network), interpretable methods (such as fuzzy models and fuzzy neural networks). We will also discuss more recently developed methods such reinforcement learning, active learning and representation learning. We will emphasize some emerging topics in deep learning such as attention mechanisms, transformers and generative models.

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. 

Small group review sessions occur 11:30 a.m. - 12:50 p.m. Wednesdays. 

Students in 603 attend one review session, the Wednesday prior to their presentation date. 

Students in 602 attend two review sessions, on Wednesdays prior to the each of the presentations they've been assigned to cover. 

 

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. 

Small group review sessions occur 11:30 a.m. - 12:50 p.m. Wednesdays. 

Students in 603 attend one review session, the Wednesday prior to their presentation date.  

Students in 602 attend two review sessions, on Wednesdays prior to the each of the presentations they've been assigned to cover. 

 

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.