Synthesizing and tuning chemical reaction networks with specified behaviours

TL;DR

This paper presents a novel two-stage method for synthesizing and tuning chemical reaction networks based on specified behaviors, combining formal synthesis and stochastic optimization to identify and refine networks for computational tasks.

Contribution

It introduces a combined synthesis and optimization framework for CRNs, enabling the generation of networks with desired computational properties and efficient parameter tuning.

Findings

Successfully identified CRNs for majority decision-making and division computation.

Demonstrated the ability to find both known and novel networks.

Analyzed expected termination times using Markov chain theory.

Abstract

We consider how to generate chemical reaction networks (CRNs) from functional specifications. We propose a two-stage approach that combines synthesis by satisfiability modulo theories and Markov chain Monte Carlo based optimisation. First, we identify candidate CRNs that have the possibility to produce correct computations for a given finite set of inputs. We then optimise the reaction rates of each CRN using a combination of stochastic search techniques applied to the chemical master equation, simultaneously improving the of correct behaviour and ruling out spurious solutions. In addition, we use techniques from continuous time Markov chain theory to study the expected termination time for each CRN. We illustrate our approach by identifying CRNs for majority decision-making and division computation, which includes the identification of both known and unknown networks.