Computational Biology View more

Learn and apply a breadth of skills to tackle intriguing problems like RNA folding and genome reconstruction.

Book 21 Lessons

Course description

This course was written in collaboration with quantitative biologists and biophysicists from leading research groups at Caltech and Duke.

Computational biology merges the algorithmic thinking of the computer scientist with the problem solving approach of physics to address the problems of biology. Since the year 2000, an ocean of sequencing data has emerged that allows us to ask new questions.

Topics covered

  • Algorithms (Python)
  • Ancestry
  • Data Analysis (Python)
  • DNA Sequencing
  • Forensics
  • Genetics
  • Genotyping
  • Molecular Biology
  • Mutual Information
  • Nussinov Algorithm
  • Protein Folding
  • RNA Folding

Prerequisites and next steps

You should have a basic understanding of programming with Python and knowledge of fundamental programming structures, including functions and loops. A working knowledge of thermodynamics would help but is not necessary.