Combined 488/567 Course - Spring 2016


Instructor: Gyan Bhanot (gbhanot@rutgers.edu)


Running Title: An Introduction to Computational Biology


Course Synopsis: This course is targeted to senior and first year grad students in physics. There are two goals: The first is to introduce students from the physical sciences to the vocabulary of biology. This will be done by discussing several key drivers of evolution (drift, selection, mutation, recombination) as they impact areas such as the origin of life, energy production in cells, vision, hot blood, motion, multicellular life, sex and death. The second goal is to introduce the students to analytic methods that they need to know in order to work in research areas that have opened up as a result of the genomic revolution of the past 15 years. This goal will be achieved by discussing some key analytic methods in detail and then applying them to concrete problems from data available online using Matlab and online tools.


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 (1 lecture: 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, BLAST and its variants for Sequence Alignment, Phylogenetic Analysis, Clustering and Pattern Recognition Techniques, Monte Carlo Simulations, Neural Networks and Support Vector Machines. 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 (1 lecture: 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. Graduate students will also be required to make a brief in-class presentation on their term paper (15 minutes). The term paper (plus presentation if appropriate) will count as 30% of the grade.


Sakai

A Sakai website is also available for the course. Go there by following this link..


Minimum Requirements: Proficiency in Calculus and Linear Algebra.