Computational Biology

What You'll Learn:

Computing has revolutionized the study of life sciences. Over the past two decades, the discipline has transformed from an experimentally driven science into a data science. As biology finds itself deluged in data, this course will show the beautiful approaches for sifting through that data that form the bedrock for the discipline of computational biology.

Whether we are assembling genomes, building evolutionary trees, or examining systems biology, the course will center on two general themes that recur throughout computational biology and that demonstrate the interdisciplinary nature of the field. First, when faced with a new, messy biological dataset, it may not be clear how to frame our problem in language that a computer scientist can understand. Second, once we have established what problem we wish to solve, we need to design an algorithm that takes this biological data as input and converts it into output that solve the underlying problem. 

Finally, throughout the course, we will show how some of the algorithms that we encounter can be used to answer questions about the SARS-CoV-2 pandemic, from assembling the virus’s genome, to identifying the location of genes within that genome, to forming evolutionary trees to understand the rise of different variants, to studying the structure of the virus’s spike protein.


Course Topics:

Biological topics
  • Genome assembly
  • Sequence alignment
  • Evolutionary tree construction
  • Read mapping
  • RNA sequencing
  • Algorithms in nature
  • Sequence and network motif finding
  • Systems biology
Algorithmic concepts
  • Network algorithms
  • Dynamic programming
  • Gibbs sampling
  • Expectation maximization
  • Clustering algorithms
  • Stochastic simulation
  • Neural networks