Course Meeting Times
Lectures: 2 sessions / week, 1 hour / session
Prerequisites
Assignments and Exams
There will be short assignments distributed at almost every lecture. We expect that all assignments will be turned in by midnight on the day they are due. You may discuss assignments with your classmates but we expect that you will submit your own work. Late assignments (up to one week late) will be given 1/2 credit; solutions for assignments will be posted at the posted due date. A family crisis or severe illness requiring attention from the infirmary and prohibiting you from all your coursework are acceptable reasons for missing an exam; every effort will be made to accommodate you in these exceptional circumstances. More information regarding academic integrity is available here.
Grading
| ACTIVITIES | PERCENTAGES |
|---|---|
| Problem Sets (Equally Weighted) | 60% |
| Exam 1 | 10% |
| Exam 2 | 10% |
| Exam 3 | 10% |
| Class Participation | 10% |
Calendar
The course is divided into three modules, with exams at the end of each module.
Module 1: Phylogenetic Inference (Lec #1-8)
Module 2: Molecular Modeling / Protein Design (Lec #10-17)
Module 3: Discrete Reaction Event Network Modeling (Lec #19-24)
Each module covers the following general topics:
- Data Structure
- Optimization Problem
- Algorithms
| LEC # | TOPICS | KEY DATES |
|---|---|---|
| Module 1: Phylogenetic Inference (PI) (Instructor: Prof. Alm) | ||
| 1 |
Course Overview Introduction to Phylogenetic Inference; Case Studies; Phylogenetic Trees; Quick Review of Recursion |
|
| 2 | Review of UPGMA; Purpose of Phylogenetics; Newick Notation | |
| 3 |
Phylogenetic Trees: Overview, Possible Trees Python®: Trees; Data Structure, Parsing Function |
Homework 1 due |
| 4 | Parsimony; Sankoff Downpass Algorithm | Homework 2 due one day after Lec #4 |
| 5 | Downpass (cont.); Fitch’s Up Pass | Homework 3 due |
| 6 | Up Pass (cont.) | Homework 4 due |
| 7 | Parsimony (cont.); Overall Strategy; Maximum Likelihood (ML); Jukes-Cantor; Evolutionary Model | |
| 8 |
Greedy Algorithm for Trying Trees Review |
Homework 5 due five days after Lec #8 |
| 9 | Exam 1 | |
| Module 2: Molecular Modeling / Protein Design (MM/PD) (Instructor: Prof. Alm) | ||
| 10 | Introduction to The Protein Design Problem. What Makes Proteins Fold? Entropy | Homework 6 due |
| 11 | MM/PD Lecture 2 | |
| 12 | MM/PD Lecture 3 | |
| 13 | Dihedrals, Build Order | Homework 7 due two days after Lec #13 |
| 14 | MM/PD Lecture 5 | |
| 15 | MM/PD Lecture 6 | |
| 16 | MM/PD Lecture 7 | Homework 8 due |
| 17 | MM/PD Lecture 8 | |
| 18 | Exam 2 | Homework 9 due two days after Exam 2 |
| Module 3: Discrete Reaction Event Network Modeling (DRENM) (Instructor: Prof. Endy) | ||
| 19 | When to Use Computational Methods vs. Exact Methods; The Physics Model Underlying Exact Methods | |
| 20 | Physics Model Underlying Exact Methods (cont.); Using Physics Model to Compute When a Reaction will Take Place | |
| 21 | Review of How Physics Model Leads to Computational Method; The Complete Computational Method (Gillespie’s Direct and First Reaction Methods) | |
| 22 | Difference Between Reaction Rate and Reaction Propensity; Achieving Faster Computation | Homework 10 due five days after Lec #22 |
| 23 | Next Reaction Method Algorithm; Application to Genetic Memory (Latch) | |
| 24 | Review of Key Concepts | Homework 11 (optional) due |
| 25 | Exam 3 | |
