COURSE DESCRIPTION: An introduction to the mathematical topics necessary to conduct theoretical and computational systems biology. Topics covered are stochastic processes, dynamical systems, modeling using ODEs and PDEs, and the basics of machine learning. Lecture-based learning with office hours discussion.

COURSE OBJECTIVES: To equip students with the tools required to model and analyze computational models of biological systems, and provide them with the basic knowledge to understand and feel more fluent in the underlying mathematics.

PREREQUISITES: Calculus, Linear Algebra, Basic Probability Theory.

REQUIRED MATERIALS: Computer with MATLAB and R.

SUITABLE FOR 1ST YEAR STUDENTS: Yes

STUDENT ASSESSMENTS: 10% participation, 20% homework, 30% midterm, 40% final. A grade above 60 required to pass. Feedback regarding grades will be regularly provided during the course.

CREDIT HOURS: 3.0