1. HOW NATURE BEAT US TO THE SUPERCOMPUTER MARTIN HANZEL
DNA Computing
HOW NATURE BEAT US TO THE SUPERCOMPUTER
MARTIN HANZEL

2. SUPERCOMPUTERS HAVE EXISTED FOR MILLIONS OF YEARS
What if I told you...?

3. NP-COMPLETE PROBLEMS
1:00 end
Travelling Salesman Problem: What is the shortest path through all vertices?
Hamiltonian Path Problem: Does there exist a path that goes through each vertex once?

4. WHY ARE THESE IMPORTANT?
If an efficient solution is found for one NP-complete problem, it works for all NP problems.
No efficient solution has been found yet.
P = NP: This is the holy grail of computer science.
If you solve one, you solve them all.
In the process, you solve most optimization problems in CS.

5. WHY ARE THESE PROBLEMS HARD?
Solutions to the TSP and HPP must consider all combinations of paths. This is very slow.
A single computer might take longer than the lifetime of the universe.
What if we use millions of computers?
2:00 end

6. Genetic engineers think like electrical engineers
Beal, J., Lu, T., & Weiss, R. (2011). Automatic Compilation from High-Level Biologically-Oriented Programming Language to Genetic Regulatory Networks. PLoS ONE, 6(8), e22490.

7. I DNA can mimic logic gates, computers are made out of logic gates, and computers compute things...
Beal, J., Lu, T., & Weiss, R. (2011). Automatic Compilation from High-Level Biologically-Oriented Programming Language to Genetic Regulatory Networks. PLoS ONE, 6(8), e22490.

8. can compute things!
DNA

9. A single test tube contains millions of cells or DNA molecules.
Each one is a single computational unit.

10. HOW DNA SOLVES HARD PROBLEMS
Having millions of DNA molecules or cells solving a problem makes it likely that the correct solution appears randomly.
Optimal outputs can be selected e.g. by length.
Solutions to the TSP, HPP (Adleman), and SAT (Braich et al) have been shown to work in DNA computers.
5:00 end

11. DISRUPTIVE TECHNOLOGY
Logistics, shipping, transportation
High-density storage
Circuit design, network design, and code optimization
Machine learning, computer vision
Big data
Genomics, bioinformatics, drug discovery, diagnostics

12. WHY IS DNA COMPUTING BETTER?
A test tube of cells or DNA can compute millions of things at the same time.
DNA is cheap and self-replicating.
DNA will spontaneously arrange into things that look like solutions – one of which is likely to be the thing that you’re looking for.
One of those things is going to be the thing you're looking for, as opposed to a classical computer, which may never even stumble upon that thing in your lifetime.

13. References
Adleman, L. M. (1994). Molecular Computation Of Solutions To Combinatorial Problems. University of Southern California.
Beal, J., Lu, T., & Weiss, R. (2011). Automatic Compilation from High-Level Biologically- Oriented Programming Language to Genetic Regulatory Networks. PLoS ONE, 6(8), e
Braich, R. S., Johnson, C., Rothemund, P. W. K., Hwang, D., Chelyapov, N., & Adleman, L. M. (2001). Solution of a satisfiability problem on a gel-based DNA computer (pp. 27–42). Springer, Berlin, Heidelberg