Toward an algorithmic theory of biology

When you typically think of computer scientists working on questions in biology, you probably picture a bioinformatician. Although bionformatics makes heavy use of algorithms and machine learning, and its practitioners are often mildly familiar with computational complexity (enough to know that almost everything they study is NP-complete), it doesn’t really apply computational thinking to understand or building theories in biology. Instead, it often generates or analyzes petabytes of blind data that biologists subsequently use to generate or test traditional verbal or physics-inspired hypotheses. The 2nd workshop on Natural Algorithms and the Sciences took a completely different approach.

The workshop was held on May 20th and 21st by Princeton’s Center for Computational Intractability and attracted speakers from biology, computer science, engineering, math and elsewhere. The meeting had a heavy focus on theoretical computer science and a return to the founding spirit of Alan Turing by tackling big fundamental questions in the sciences. It saw applications of computational complexity, computability theory, machine learning, distributed and parallel computing, and information theory. Although the mandate of the workshop is broader than looking at biology, most of the talks returned to questions in the biological sciences. I greatly enjoyed my time at the workshop, and intended to live blog the event. However, a poor internet connection at my residence, other time commitments, and the vast amount of ideas I wanted to cover instead translated into a series of seven posts (this is the eighth) that spanned the last three weeks. To make reading (and later reference) easier, this post is a TL;DR of the last seven posts. Each section is a short summary of a post and a list of the talks discussed; at the end I include a partial bibliography for further reading. Click through on the headings to learn more and join the discussion on specific topics!
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