642:613 Mathematical Foundations of Systems Biology
Rutgers, Fall 07, Course Index Number: 35072

(click icon for wiki)

Instructor: Eduardo Sontag, email: sontag@math (add .rutgers.edu if mailing from outside Rutgers)

Please add the following word: ALLOW1 (no spaces) to the subject line of any email sent to me, so spam filters do not block it.

Class will meet Wednesdays 10-1, right before the BioMaPS seminar, in the BioMaPS seminar room, Hill 260.

If you are not receiving emails from the instructor, please send him an email to let him know. (Please check your spam filters first.)

Class notes and other reading materials:

Schedule

Course Announcement:

Life, whether at the level of the genome, cells, organs, organisms, or populations, can only be understood when seen as the result of interactions among multiple components. Whether dealing with signal transduction pathways in cells and their disruption in cancer, neuronal networks in brain function, the spread of epidemics in populations, or ecosystem responses to climate change, the typical "reductionist" approach to learning and doing biology is not powerful enough to describe, analyze, and interpret such complex behaviors. Quantitative (i.e, mathematical!) formalisms, concepts, tools, and models are required. Indeed, one could say that the Life Sciences are in the midst of a major revolution in quantitative theoretical formulations, not unlike the transformation that physics underwent in the 17th century.

There are a very large number of possible topics to be covered, and the syllabus will evolve based on student's interest and input. Some of the possible topics include the dynamics of cell signaling networks including memories, switches, and oscillators, chemotaxis, pattern formation, neural transmission, synthetic biology, reverse engineering of gene and protein networks, Markov chains for population models, epidemiology, and the mathematics behind phylogenetic trees, sequence alignment methods, and shotgun DNA sequencing.

The course will consist of instructor's lectures, student discussion of research papers, and perhaps have some guest speakers.

Level and Background:

As I expect that many or even the majority of students taking the course will come from programs other than math (BioMaPS, various Engineering departments, chemistry, genetics, physics, computer science, etc), I will not impose any explicit course prerequisites. At a minimum, however, students should have a working familiarity with linear algebra, differential equations, and basic probability, at the level of an advanced undergraduate or beginning graduate student.