Abstract

The development of technology to read and write DNA quickly and cheaply is enabling new opportunities for programming biological systems. One example of this is DNA computing, a field devoted to implementing computation in purely biological materials. The hope is that this would enable computation to be performed inside cells, which could pave the way for so-called “smart therapeutics”. Naturally, what we have learned in computer science can be applied to DNA computing systems, and has enabled the implementation of a wide variety of examples of performing computation. Examples include DNA circuits for computing a square root, implementing artificial neural networks, and a general scheme for describing arbitrary chemical reaction networks (CRNs), which itself can be thought of as a compiler.

We have used such a CRN compiler of DNA circuitry to implement the approximate majority (AM) algorithm, which seeks to determine the initial majority of a population of agents holding different beliefs. In its simplest form, the algorithm can be described by three chemical reactions. In this talk, I will describe how we implemented, characterized and modelled a purely DNA implementation of the AM reactions. Along the way, I will demonstrate our software platform for programming biological computation. The platform brings together a variety of stochastic methods that are relevant for both programming and understanding biochemical systems, including stochastic simulation, integration of the chemical master equation, a linear noise approximation, and Markov chain Monte Carlo methods for parameter inference. I will also show preliminary work on synthesizing CRNs with specified probabilistic behaviours.

Related Links

• http://research.microsoft.com/en-us/people/ndalchau/ – Personal website
• http://research.microsoft.com/en-us/projects/dna/ – 'Programming DNA circuits' project