Fourier Transform

Biomolecular Structure

Protein Structure

RNA Structure

Energy Functions

Molecular Conformation

Molecular Dynamics Simulation

Predicting Structures of Proteins and Other Biomolecules

Machine Learning Methods in Biomolecular Structure Prediction

Protein Design

Convolution

Image Analysis

Microscopy

Fluorescence Microscopy

Super-resolution Fluorescence Imaging

Diffusion

Cellular-Level Simulation

Ligand Docking

Virtual Screening

X-Ray Crystallography

Cryo-Electron Microscopy

CS 279 Computational Biology: Structure and Organization of Biomolecules and Cells Stanford University Fall 2022 This course focuses on computational techniques to study biomolecule and cell structures. Topics include molecular modeling methods, structural prediction, dynamics simulation, protein design, and computational analysis of optical microscopy images. Requires basic programming skills and biology knowledge. This course will focus on computational techniques used to study the structure and dynamics of biomolecules, cells, and everything in between. For example, what is the structure of proteins, DNA, and RNA? How do their motions contribute to their function? How do they bind to other molecules? How are molecules distributed and compartmentalized within a cell, and how do they move around? How might one modify the behavior of these systems using drugs or other therapeutics? How can structural information and associated computational methods contribute to the design of drugs, vaccines, proteins, or other important molecules?

Computation can contribute to addressing such questions in at least two distinct ways. First, computational analysis is required to extract useful information from experimental measurements. Second, one can use computational techniques to predict structures, dynamics, and important biochemical properties.

The course will cover (1) atomic-level molecular modeling methods for proteins and other biomolecules, including structure prediction, molecular dynamics simulation, docking, and protein design, (2) computational methods involved in solving molecular structures by x-ray crystallography and cryo-electron microscopy, and (3) computational methods for studying spatial organization of cells, including computational analysis of optical microscopy images and video, and simulations at the cellular scale. The course will cover both foundational material and cutting-edge research in each of these areas, including recent advances in machine learning for structural biology.  Elementary programming background (at the level of CS 106A) and introductory course in biology. There is no required textbook. We will suggest a variety of optional reading material throughout the course.

CS 279 Computational Biology: Structure and Organization of Biomolecules and Cells

Interdisciplinary

Big-O notation

Integer multiplication

Master theorem

Median finding

Coefficient/value representation

Roots of unity

Topological sorting

Paths in graphs

Greedy Algorithm

Kruskal's Algorithm

Linear Programming

Simplex Algorithm

Bipartite Matching

Zero Sum Games

Search problem

Reductions

Sampling

Streaming

Fast Fourier transform

Arithmetic

Recurrence relation

Matrix Multiplication

Polynomial multiplication

Complex numbers

Depth-first search (DFS)

Strongly connected components

Single Source Shortest Paths

Dynamic programming

Algorithms to Linear Programming

Duality

Multiplicative Updates

NP-completeness

Sketching

Minimum Spanning Tree (MST)

Network flow problem

CS 170: Efficient Algorithms and Intractable Problems UC Berkeley Spring 2020 This is an introductory course to computer science theory, exploring the design and analysis of various algorithms, number theory, and complexity. The prerequisites include familiarity with mathematical induction, big-O notation, basic data structures, and programming in a standard language. CS 170 is Berkeley’s introduction to the theory of computer science. In CS 170, we will study the design and analysis of graph algorithms, greedy algorithms, dynamic programming, linear programming, fast matrix multiplication, Fourier transforms, number theory, complexity, and NP-completeness.  The prerequisites for CS 170 are CS 61B and CS70. You will need to be comfortable with mathematical induction, big-O notation, basic data structures, and programming in a standard imperative language (e.g., Java/C/Python). Another “prerequisite” for doing well in the course is [mathematical maturity](https://en.wikipedia.org/wiki/Mathematical_maturity), or the ability to think about and work with proof-based math (which CS70 can help build). The required textbook is [Algorithms](http://cseweb.ucsd.edu/~dasgupta/book/index.html) by Dasgupta, Papadimitriou, and Vazirani, also known as DPV. Most readings will be from the textbook, and the relevant textbook chapters will be linked for each lecture. Any readings not from the textbook will be freely available as notes.

CS 170: Efficient Algorithms and Intractable Problems

Data Structures and Algorithms

Theoretical Computer Science