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.


Problem Sets (Equally Weighted) 60%
Exam 1 10%
Exam 2 10%
Exam 3 10%
Class Participation 10%


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:

  1. Data Structure
  2. Optimization Problem
  3. Algorithms

Module 1: Phylogenetic Inference (PI) (Instructor: Prof. Alm)

Course Overview

Introduction to Phylogenetic Inference; Case Studies; Phylogenetic Trees; Quick Review of Recursion

2 Review of UPGMA; Purpose of Phylogenetics; Newick Notation

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

Greedy Algorithm for Trying Trees


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