Towards heterotic computing with droplets in a fully automated droplet-maker platform

Abstract

The control and prediction of complex chemical systems is a difficult problem due to the nature of the interactions, transformations and processes occurring. From self-assembly to catalysis and self-organization, complex chemical systems are often heterogeneous mixtures that at the most extreme exhibit system-level functions, such as those that could be observed in a living cell. In this paper, we outline an approach to understand and explore complex chemical systems using an automated droplet maker to control the composition, size and position of the droplets in a predefined chemical environment. By investigating the spatio-temporal dynamics of the droplets, the aim is to understand how to control system-level emergence of complex chemical behaviour and even view the system-level behaviour as a programmable entity capable of information processing. Herein, we explore how our automated droplet-maker platform could be viewed as a prototype chemical heterotic computer with some initial data and example problems that may be viewed as potential chemically embodied computations.

Heterotic computing: exploiting hybrid computational devices

“…The same computation can be embedded in different computational substrates and even embedded in different ways in the same substrate [5]. Henson et al [13] describe a system comprising a computer, a robot and a chemical reaction system, linked together in a fully automated experimental system to search for chemical products that exhibit interesting complex spatio-temporal dynamics (motile and dividing droplets). Importantly, the procedure provides reproducible synthetic pathways to such products.…”

Cellular Automaton Belousov–Zhabotinsky Model for Binary Full Adder

“…Our approach may be proved efficient where discretised excitable media are concerned. Namely, our techniques could be adopted in implementation of computing circuits in BZ medium encapsulated in arrays of micro-droplets [Wang et al, 2016;Henson et al, 2015], nano-scale realisations of reaction-diffusion computers [Epstein & Xu, 2016], and wave-fronts of electrical potential travelling in bioengineered living tissue [McNamara et al, 2016].…”

Combinatorial microfluidic droplet engineering for biomimetic material synthesis

“…By using genetic algorithms to rapidly identify surfactant/oil combinations that give droplets with specific target properties, it will become possible to create droplets optimized for activities, such as the maintenance of cell growth ( 16 ), DNA amplification ( 8 , 18 ), or chemical processes, including catalysis ( 27 ) and polymerization ( 55 ). It may also be possible to target droplet computation with such platforms by modifying PDMS device design to offer more complex channel schemes and integrating robotically controlled closed-loop droplet optimization systems ( 62 ). To illustrate the potential of our technology, we identified combinatorial oil/surfactant chemistries that offer enhanced droplet stability.…”