Lecture Notes

This page presents complete lecture notes for modules 1 and 3, plus a couple of notes for module 2.

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

Course Overview

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

2 Review of UPGMA; Purpose of Phylogenetics; Newick Notation (PDF)

Phylogenetic Trees: Overview, Possible Trees

Python®: Trees; Data Structure, Parsing Function (PDF)

4 Parsimony; Sankoff Downpass Algorithm (PDF)
5 Downpass (cont.); Fitch's Up Pass (PDF)
6 Up Pass (cont.) (PDF)
7 Parsimony (cont.); Overall Strategy; Maximum Likelihood (ML); Jukes-Cantor; Evolutionary Model (PDF)

Greedy Algorithm for Trying Trees

Review (PDF)


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 (PDF)
11 MM/PD Lecture 2
12 MM/PD Lecture 3
13 Dihedrals, Build Order (PDF) (Courtesy of Mike Yee. Used with permission.)
14 MM/PD Lecture 5
15 MM/PD Lecture 6
16 MM/PD Lecture 7
17 MM/PD Lecture 8
18 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 (PDF)
20 Physics Model Underlying Exact Methods (cont.); Using Physics Model to Compute When a Reaction will Take Place. (PDF)
21 Review of How Physics Model Leads to Computational Method; The Complete Computational Method (Gillespie's Direct and First Reaction Methods) (PDF)
22 Difference Between Reaction Rate and Reaction Propensity; Achieving Faster Computation (PDF)
23 Next Reaction Method Algorithm; Application to Genetic Memory (Latch) (PDF)
24 Review of Key Concepts (PDF 1) (PDF 2)
25 Exam 3