Course Syllabus

Fall 2019 BIOINF 501

Mathematical Foundations for Bioinformatics


Kayvan Najarian, Daniel Burns, Jonathan Gryak, Reza Soroushmehr, and Ivo Dinov


Start Time, End Time and Location

MW 1:30PM - 3:00PM, Room: # Palmer Hall 2036 



Module 1: Review of Some Basic Methods in Mathematics

Probability functions

Review of complex variables and functions

Taught by: Kayvan Najarian

Duration: 2 lectures


Review of multi-variable calculus

Taught by: Reza Soroushmehr

Duration: 2 lectures


Module 2: Linear Algebra

Part I

Introduction to linear systems

Matrix products

The inverse of a linear transformation

Linear spaces


Determinants, eigenvalues and eigenvectors

Symmetric matrices and diagonalization

Solving systems of linear equations

Taught by: Jonathan Gryak

Duration: 5 lectures


Part II

Singular value decomposition

Principal component analysis

Spectral graph theory

Taught by: Jonathan Gryak

Duration: 3 lectures


Module 3: Differential Equations

Part I

Introduction to differential equations

First and second order linear equations

Existence and uniqueness of solutions

Difference equations

Systems of linear equations

Phase plane and bifurcation: diagrams and analysis

First order nonlinear systems

Taught by: Jonathan Gryak

Duration: 4 lectures


Part II

Differential equations for compartmental modeling of biomedical systems

Taught by: Reza Soroushmehr

Duration: 4 lectures


Module 4: Optimization

Data Science and Predictive Analytics EBook (University of Michigan Library)

Free (unconstrained) optimization vs. Constrained Optimization

Foundations of R

Equality and Inequality constraints

Lagrange Multipliers

Linear and Quadratic Programming

Manual vs. Automated Lagrange Multiplier Optimization

Data Denoising: Application of computer optimization techniques in medicine and biology

Heuristic methods - Genetic algorithms, simulated annealing

Applications (supervised classification & unsupervised clustering)

Instructor: Ivo Dinov

Duration: 6 lectures



Software tools: Matlab, R, Python, and open-science tools.

Course Summary:

Date Details Due