A variety of Systems Biology and Computational Molecular Biology courses are offered. Information about additional courses can be obtained from the Stanford University Bulletin.

  • Principles of Cancer Systems Biology (CBIO 243) The emergence of high throughput (HTP) technologies that probe global DNA, RNA and protein expression has spawned a "systems biology" approach to the study of cancer that integrates experimental and computational methods. PLEVRITIS (3 units, Spring)
  • Representations and Algorithms for Computational Molecular Biology (Biomedical Informatics 214/Computer Science 274). A programming course, introduces nuts and bolts of basic algorithms. ALTMAN (4 units, Fall)
  • Translational Bioinformatics (BMI 217) is a new virtual course covering the use of bioinformatics to assist translational medicine. BUTTE (3 units, Spring)
  • Computer Applications in Molecular Biology (Biochemistry 218). A course for biologists, introduces key ideas in bioinformatics. BRUTLAG (Winter)
  • Protein Architecture, Dynamics and Structure Prediction (Structural Biology 228). Introduces the basic concepts of molecular structure and how to compute with molecular structure. LEVITT (Fall, Spring)
  • Algorithms for structure and function in biology (CS273) covers algorithms for modeling and motion in molecular biology. BAZOGLOU/ LATOMBE (Spring)
  • Computational methods for analysis and reconstruction of biological networks (CS 279) covers the algorithms and data structures for analyzing and reconstructing biological networks. KOLLER (Fall, Spring)
  • Computational Systems Biology (CS 278) is an introduction to systems biology computing. DILL
  • Biomedical Informatics 212 is a project course that allows students with an interest in the field to work on teams of 3 or 4 students to create a novel software system in some area of biomedical informatics. ALTMAN/KLEIN/CHENG (Fall)
  • Chemical and Systems Biology 210: Cell Signalling This course focuses on the molecular mechanisms by which cells receive and respond to external signals. It covers biochemical, cellular, genetic, and pharmacologic approaches to this issue. (Instructors: Ferrell, J and Meyer, T) (Offered annually: Winter Quarter: 4 units)
  • Chemical and Systems Biology 240: Drug Discovery This course covers the scientific principles and technologies involved in making the transition from a basic biological observation to the creation of a new drug. (Offered 2010-2011 and alternating years: Spring Quarter: 4 units )
  • Chemical and Systems Biology 260: Quantitative Chemical Biology This course explores how chemical and quantitative methods have been used to understand and manipulate biological processes. Specific topics include chemical genetics, imaging technologies, protein homeostasis, cell signaling, fluorescent protein engineering, and bioinformatic analyses of protein structure. (Offered 2009-2010 and alternating years: 4 units)
  • Computational Biology: Structure and Organization of Biomolecules and Cells (CS 279)
    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, and how do their motions contribute to their function? 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 contribute to the design of drugs, proteins, or perhaps even cells? 
  • Deep Learning in Genomics and Biomedicine (CS 273B)
    Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and high-throughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results.