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4.1 Computational Biology
Learn and apply a breadth of skills to tackle intriguing problems like RNA folding and genome reconstruction.
What is Life?
DNA Fingerprints
How Big is a Genome?
Protein Origami
Information and Order
Dogmatic Structures
DNA Composition
Programming Gene Expression
Universal Translator
Evolution
Reading our own Blueprints
DNA Forensics
Genotyping
Ancestry
How to Fold a Molecule
Finding Palindromes
RNA Folding
Nussinov Algorithm
Folding with Information
RNA World
Folding with Randomness
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. 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.
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.
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5.1 Quantum Computing
Solve hard problems by computing with quantum mechanics.
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