Programmable reaction-diffusion fronts

TL;DR

This paper introduces a modular, DNA-based programmable reaction-diffusion system that allows precise control over pattern formation, enabling the design of complex, self-organizing structures beyond traditional stationary patterns.

Contribution

The authors develop a fully programmable DNA-based reaction-diffusion platform with tunable kinetics and diffusion, demonstrating control over pattern propagation and interactions.

Findings

Reaction-diffusion fronts propagate at ~100 μm/min

Diffusion coefficients can be reduced by up to 2.7 times

The system's behavior aligns with Fisher-KPP models

Abstract

Morphogenesis is central to biology but remains largely unexplored in chemistry. Reaction-diffusion (RD) mechanisms are, however, essential to understand how shape emerges in the living world. While numerical methods confirm the incredible potential of RD mechanisms to generate patterns, their experimental implementation, despite great efforts, has yet to surpass the paradigm of stationary Turing patterns achieved 25 years ago. The principal reason for our difficulty to synthesize arbitrary concentration patterns from scratch is the lack of fully programmable reaction-diffusion systems. To solve this problem we introduce here a DNA-based system where kinetics and diffusion can be individually tuned. We demonstrate the capability to precisely control reaction-diffusion properties with an autocatalytic network that propagates in a one-dimensional reactor with uniform velocity, typically 100 {\mu}m min-1. The diffusion coefficient of the propagating species can be reduced up to a factor 2.7 using a species-specific strategy relying on self-assembled hydrodynamic drags. Our approach is modular as we illustrate by designing three alternative front generating systems, two of which can pass through each other with little interaction. Importantly, the strategies to control kinetics and diffusion are orthogonal to each other resulting in simple programming rules. Our results can be quantitatively predicted from first-principle RD equations and are in excellent agreement with a generalized Fisher- Kolmogorov-Petrovskii-Piscunov analytical model. Together, these advances open the way for the rational engineering of far-from-equilibrium arbitrary patterns and could lead to the synthesis of self-organizing materials.