Physics 01:750:431, Spring 2017

Instructor: Gyan Bhanot

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Location/Time: SEC 220: Tu/Th 3:20 PM – 4:40 PM

Title: Introduction to Computational Biology for Physicists

Course Synopsis: In the twentieth century, physicists such as Leo Szilard, Erwin Schrodinger, Francis Crick, Walter Gilbert and Venki Ramakrishnan played a major role in developing some of the key ideas in biology. The sequencing of the human genome and the big-data genomic revolution it has unleashed have created new and exciting opportunities for physicists to make further discoveries in biology. This course is intended for junior and senior physics majors who are interested in working in the exciting area of biophysics and computational biology. The goal is to introduce the students to the ideas and methods they need to master to do research and make exciting new discoveries in biology in the data driven genomic age.

Detailed Description of the Course (24-26 ninety minute lecture classes divided into 3 sections):

I. Biology and the 4 forces of Nature (7 lectures):

The question that modern biology poses is: given the rules of physics and chemistry, how did the world get to be the way it is. The first 2 lectures will cover the basics of biology that physics students need to learn to appreciate the discoveries that have resulted from the genomic revolution of the past 15 years. The next 4 lectures will analytically explore the biological principles behind the four fundamental forces in biology: Drift, Mutation/Migration, Selection and Recombination, whose actions have resulted in the diversity of life we see today. There will be an in-class midterm after this section is completed (mid-term 1 : 20 % of grade).

II. Analytical Methods and Matlab (13 lectures):

Next we will develop analytical methods to understand genetic and genomic data, beginning with a 1 lecture tutorial on Matlab, followed by 11 lectures on Probability Theory including Bayesian analysis, The Central Limit theorem, Parametric and Non Parametric Tests of Significance, Sequence Alignment, Phylogenetic Analysis, Clustering and Pattern Recognition Techniques, Monte Carlo Simulations, Neural Networks and Evolutionary Game Theory. Students will learn to use Matlab programming on databases and software available online to solve many of the homework problems. All the methods and ideas presented will be developed using concrete examples of how they apply to biological phenomena. There will be an in-class midterm after this section is completed (mid term 2: 20 % of grade).

III. Application of Methods to problems of research interest (6 lectures):

In the next 6 lectures, we will apply the methods to solve 3-6 concrete problems of current research interest using data from sources such as Mitomap, the HapMap projects, the 1000 genomes project, Viral Databases, The Cancer Genome Atlas (TCGA) etc. Examples of some of the projects we will explore are inferring selection (GWAS), identifying biomarkers from gene expression, SNPs, methylation, CNV and histone marks, modeling viral diseases, building phylogenies from sequence data, etc.

IV. Homework:

Homework will be handed out in class at regular intervals and will be due in one week. It will count for 30 % of the grade for the course.

V. Lecture Notes, Text books:

There is no textbook for this course. However, reading material, including a list of books the students should read during the course which will be handed out on the first day. These books will be useful to students when they do the projects in Section III above. Setailed notes covering each lecture will be provided to the students via Sakai.

Final, Term paper/Oral Presentation:

There will be no final. Instead, all students will be required to write a term paper, either on their work in Section III, or on a topic they can choose from a list that will be provided to them. Students will also be required to make a brief in-class presentation on their term paper (15 minutes). The term paper plus presentation will count as 30% of the grade.

A Sakai website will be available for the course. Details will be mailed out to registered students.

Minimum Requirements: Proficiency in Calculus and Linear Algebra.