Abstract

The ability to reliably re-program cells has great potential for tackling societal challenges, in areas such as health, food and energy. DNA is the code that stores the information required to replicate life, and is read by cellular machinery. In addition to its role in the natural sense, it is also possible to use DNA as a substrate for computing, by taking advantage of its physical properties. Such DNA computing offers an exciting potential to perform complex calculations within the cellular environment, and interface to the natural machinery of the cell to perform conditional actions. In this talk, I’ll show how we have used DNA strand displacement to create complex dynamical behaviours. The network design benefits from the Visual DSD software, developed by ourselves, to build, analyse and test models of DNA strand displacement networks. We formally implement chemical reaction networks (CRNs), which are capable of a rich set of nonlinear dynamics. By combining DNA implementations of non-catalytic and autocatalytic reactions, we construct a circuit that implements the approximate majority algorithm, the fastest way for a population to reach a collective majority decision between two possible outcomes. The experimentally observed dynamics of the full circuit are well predicted by a model whose parameters are inferred from experimental measurements of the constituent reactions using Markov chain Monte Carlo parameter estimation.