Computational Biology

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.
Here we'll develop intuition for a selection of foundational problems in computational biology like genome reconstruction, sequence alignment, and building phylogenetic trees to look at evolutionary relationships.
We also address certain physicochemical problems of molecular biology like RNA folding.

• Biological Numeracy: Get to know biology by the numbers through these guided explorations of information and structure.
• What is Life?: Get to know the guiding light of this course: life evolves in a discrete and quantifiable way.
• DNA Fingerprints: DNA fingerprints can help explore your ancestry, unlock your health, and even track down a killer.
• How Big is a Genome?: What does a nuclear explosion have to do with the amount of information in your genome?
• Protein Origami: How molecules fold into biological machines is one of the greatest unsolved problems in biology.
• Order and Information: Discover the central dogma of molecular biology via computational experiments.
• Information and Order: What are genes and how do they store the blueprints of bacteria and human beings?
• Dogmatic Structures: Molecular biology follows a simple set of rules to turn information into living things.
• DNA Composition: Use Python to replicate a series of experiments that proved once and for all the role of DNA in biology.
• Programming Gene Expression: Biology uses a series of enzymes to transcribe DNA sequences into RNA. You'll use Python.
• Universal Translator: Your cells understand two languages. Learn how to translate between them with Python.
• Evolution: Plan a mission to Mars to learn how organisms adapt and overcome.
• Genomics: Learn your way around the human genome with techniques like DNA profiling, genotyping, and ancestry analysis.
• Reading our own Blueprints: A teaspoon of DNA can store a trillion gigabytes of data. But reading any of that data is difficult.
• DNA Forensics: The FBI's CODIS database has all the data you need to track down the Golden State Killer.
• Genotyping: Icelandic DNA tells a story of Viking colonization and plunder across the Northern Atlantic.
• Ancestry: Build a map of Africa using the geography encoded in DNA.
• Molecular Folding: Use insights from thermodynamics and evolution to build algorithms that find the structures of biological sequences.
• How to Fold a Molecule: Use some tricks from origami to predict how DNA and proteins find their shape.
• Finding Palindromes: The first step of RNA folding requires finding all the palindromes.
• RNA Folding: There are many ways to fold a molecule, but only one is best. How do we find it?
• Nussinov Algorithm: Ruth Nussinov found a clever shortcut to make computational RNA folding easy.
• Folding with Information: Combine a genetic dataset with some probability tricks to make Nussinov's algorithm even better.
• RNA World: Put computational RNA folding to the test to explore the RNA machines at work in your cells.
• Folding with Randomness: You can bet on the Monte Carlo algorithm to speed up protein folding.