Physics 01:750:431, Spring 2018

Instructor: Gyan Bhanot 

Preferred email: gyanbhanot@gmail.com

Departmental email: gbhanot@physics.rutgers.edu

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 needed to solve exciting problems in the 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):
Given the rules that govern physics and chemistry, how did the world get to be the way it is. The 2 lectures will cover the basics of biology such as the origin of life, the structure of the cell, and evolutiona. The next 4 lectures will analytically explore the four 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):
In this section 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 we discussed to solve 3-6 concrete problems of current research interest using publicly available data bases. 
 
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. Detailed notes covering each lecture will be provided to the students via Sakai.
 
Final, Term paper/Oral Presentation:
There will be no final exam. Instead, all students will write a term paper on a topic from a list I will provide to them or on a topic on their own choice. Students will also make a brief in-class presentation on their term paper (15 minutes). The term paper plus presentation will count for 30% of the grade.
 
Minimum Requirements: Proficiency in Calculus and Linear Algebra.