Instructor:
Junhyong Kim
Lynch Life Sci Bldg, 103H
215-746-5187
Office Hours: Any time by appointment
Teaching Assistants:
TBA
Prerequisites:
(You may talk to me if you would like to take the course but cannot meet the prereqs).
College level introductory biology required; undergraduate or graduate level
statistics taken previously or concurrently required; molecular biology and/or
genetics encouraged; familiarity with computers encouraged.
Course Description:
Biology 536 is a “principles” of computational biology course designed for both biology students and computer science, engineering students. The course will cover fundamentals of algorithms, statistics, and mathematics as applied to biological problems. The basic idea of the course is that after taking this course you will understand the algorithmic/mathematical principles of quantitative methods used in computational biology and gain appreciation of quantitative modeling in biomedical problems. Emphasis will be placed on small “paper-and pencil” problems to help understand theoretical basis of the computational biology methods. Actual code implementation and the use of standard bioinformatics tools is not emphasized in this course.
Course Web page:
This course will utilize Penn’s “Electronic Blackboard” web software at: https://courseweb.upenn.edu/. All updates, materials, discussion, and grades will be posted here. Visit the site often. This is a secure site requiring your PennKey. If you have trouble with access, please contact the support staff at bb-support@ccat.sas.upenn.edu.
Grading and other important stuff:
In-course exams (3 total)
Exercises: Bonus points
Short quizzes: no points
For students enrolled in BGS or other PhD programs, a final project will be required. Topic will be discussed with the student individually.
Final grades will be assigned as follows:
Undergraduates and Masters: Each exam is worth 33.33% of final grade. Bonus points applied as detailed below.
PhD and MD/PhD program students: Each exam is worth 23.33% of final grade. Final project is worth 30%.
There will be three short exams given during the semester. These exams will be modularized to the material covered in the previous periods.
Periodically, exercise problems will be posted to help with concrete understanding of the lecture material. These problems will not be graded per se, but if you hand them in and have completed greater than 80% of the exercises by the end of the semester BONUS points will be given (see below for how bonus points will be used). Other impromptu opportunities to collect BONUS points will be presented during lectures.
When new material is introduced, there will be short pop quizzes. These quizzes are designed as a diagnostic tool for me to know what you already know and what you do not know. They are not used in giving you grades.
Make-up exams:
Make up exams will be given only if you submit a request for permission prior to the exam. No makeup will be given if requested after the exams unless you have documented emergency.
Grades:
Numerical grades will be converted to letter grades based loosely on distribution and absolute standards. If your numerical grade is sufficiently close to a letter grade cutoff, I will use your BONUS points to raise your letter grade.
Grade disputes:
Grading disputes must be submitted in writing. You must write me a letter stating in scientific manner why your answer is correct. I will not accept arguments of the form “but it’s all there.” No grade dispute letter will be accepted after the Reading Period.
Recommended Reading:
There are no required textbooks for this course. The following is a list of reference books you may use. Lecture notes will be posted weekly.
An introduction to bioinformatics algorithms (Computational Molecular Biology)
N.C. Jones and P.A. Pevzner
A Primer of Genome Science
Greg Gibson and Spencer Muse, North Carolina State University
Molecular Evolution: A Phylogenetic Approach
Roderic Page and E. C. Homes
Other books of interest:
Algorithms on strings, trees, and sequences: computer science and computational biology, Dan Gusfield, Cambridge Univ Press. (A very comprehensive algorithms book with easy to read style)
Computational Molecular Biology: An Algorithmic Approach
Pavel A Pevzner (Somewhat hard core computational biology)
Genes VII, Ben Lewin, Oxford Press. (Standard textbook for molecular biology course. Rather lengthy)
Molecular Biology of the Cell, Alberts et al. (Again standard textbook—long)
Recombinant DNA, Watson et al., W.H. Freeman. (Quite outdated but still a great introduction to molecular biology)
Statistical methods in bioinformatics: An introduction, Grant and Ewens, Springer-Verlag. (This is one of the few statistically focused books on bioinformatics. And, you can directly talk to the author!)
Course Schedule:
Date |
Theme |
Topics |
Lecturer |
1/14 W |
Introduction |
Course organization and goals, student survey |
JK |
1/21 W |
Warm-up and Basics |
Basic Probability Calculus Basic Statistics Basic Vector Geometry, Matrix Algebra |
JK |
1/26 M |
Warm-up and Basics II |
Recursive relationships Algorithm concepts Optimization |
JK |
1/28 W |
Detecting Biological Homology |
Concept of homology First Pattern Search Algorithm |
JK |
2/2 M |
Sequence Alignment and Dynamical Programming |
Alignment as an optimization problem, Variations of sequence alignments |
JK |
2/4 W |
Advanced sequence alignment and homology detection |
Multiple sequence alignment BLAST and other homolog search |
JK |
2/9 M |
Advanced molecular evolution |
Stochastic models of sequence evolution |
JK |
2/11 W |
Biological Genealogy |
Gene families, molecular evolution, and stochastic processes |
JK |
2/16 M |
cont. |
Phylogenetic Estimation I |
JK |
2/18 W |
cont. |
Phylogenetic Estimation II |
|
2/23 M |
Test I |
Materials from 1/14 to 2/18 |
JK |
2/25 W |
Genomic Sequences and their function |
Pattern detection, repeats, finite state automata |
JK |
3/2 M |
cont. |
Probabilistic sequence models, Position Weight Matrices |
JK |
3/4 W |
cont. |
Hidden Markov Models and Generative Grammar Models |
JK |
3/16 M |
cont. |
Hidden Markov Models II. |
JK |
3/18 W |
Comparative Genomics |
Inferring natural selection from sequence comparisons |
JK |
3/23 M |
cont. |
Population genetics 101 |
JK |
3/25 W |
Functional Genomics |
Machine Learning Techniques |
|
3/30 M |
Cont. |
Machine Learning Techniques II |
JK |
4/1 W |
Test II |
Materials from 2/16-3/30 |
JK |
4/6 M |
cont. |
Gene Expression I |
JK |
4/8 W |
cont. |
Multivariate Statistics, Multiple hypothesis tests |
JK |
4/13 M |
Genome-wide Genetics |
HapMap database SNP mapping |
JK |
4/15 W |
System-wide modeling |
Concepts, dynamical systems |
JK |
4/20 M |
cont. |
Dynamical systems cont. |
JK |
4/22 W |
MISC Topic |
To be announced |
JK |
4/27 M |
Test III |
Material from 3/25—4/22 |