**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.