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.
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.
