640:338 Discrete and Probabilistic Models in Biology:
Home Page, Spring 2007

Class meets: TTH6 (5:00-6:20), SEC, Room 218
Text: The text is available (free!) on-line at http://sites.math.rutgers.edu/courses/338/coursenotes/coursetext.html.
Instructor: Daniel Ocone, ocone@math.rutgers.edu
Office Hours: Hill 518: Tuesday and Thursday, 3:15-4:45 (before class); Wednesday, 10:30-11:30; or by appointment.
Link for Problem Sets and Reading Assignments
Link for Class by Class schedule Additional handouts will be posted on this page.
Syllabus by topic
Tests, homework, grades: There will be weekly, graded problem sets, two midterms, and a final.
The final grade will be computed from an average of the final grade (200 points), the midterm grades (100 points each), and the homework grades (100 points). The midterms are tentatively scheduled for Feb. 20 and March 29.

Useful links and Announcements

  • Undergraduate Summer Fellowships in Cancer Research 2007. Undergraduate summer research fellowships of $3,222 plus $600 travel are available for sophomores and juniors in biology, chemistry, physics, math, statistics, computer science, and engineering. Deadline Feb. 19, 2007.

    Information and applications http://icbp.nci.nih.gov/edu_outreach/FellowshipProgram/announcements

  • Research Experiences for Undergraduates at Penn State Erie Penn State-Behrend is hosting an undergraduate summer research program in mathematical biology. The program is six weeks long. Those accepted receive travel money, room and board, and a stipend. For information go to http://reu.bd.psu.edu.

Spring 2007 Course Description

THE SPRING 2007 semester will focus on probabilistic and dynamic programming methods in the analysis of biological sequences and in genetics. The course introduces models from population biology for the study of the evolution of gene frequency, models from genomics for the statistical description and for the evolution of genomes and proteomes, and the mathematical tools for their analysis. More details may be found on the week-by-week syllabus.

Here is a summary:

  • Basic populations genetics as an introduction to probabilistic models in biology.
  • Continuous random variables. Independent, identically distributed random sequences, geometric, exponential, and normal random variables, and Poisson processes. Application to coverage analysis in DNA sequencing strategies.
  • Likelihood functions, Maximum Likelihood Estimation, hypothesis testing, and application to models for biological sequences.
  • Dynamic Programming and sequence alignment.
  • Markov chain basics and Hidden Markov models for sequence evolution and applications to alignment.