Chemical reaction networks improve computing ability with energy input
Local energetic coupling enhances the expressivity of chemical computation
Emerging Technologies
Summary
Computers in living things work by using chemical reactions to understand their environment, but it's unclear what makes chemical systems better at this kind of processing. The authors created chemical networks that can perform specific complex calculations when given environmental signals by training the energy levels inside the reactions. They found that making some reactions driven out of equilibrium—powered so they don't just settle naturally—greatly improves the network's ability to perform complicated calculations. This suggests that adding energy to chemical systems can boost their computational power.
What this means in practice
- •For synthetic biologists: Design chemical reaction networks that perform specific computations in living or artificial cells by tuning energy parameters for better function.
- •For chemical engineers: Build chemical systems that process input signals to control reactions more precisely using energy-driven networks with enhanced computational capacities.
Authors
Marco Tuccio, Jason W. Rocks, Joshua E. Goldford
Abstract
Living systems compute with chemistry by mapping environmental signals onto specific internal chemical states. Despite recent advances in molecular programming, it remains unclear which physicochemical features control the computational expressivity of chemical systems. Here we inverse-design thermodynamically consistent chemical reaction networks whose steady-state response to an environmental input computes a target nonlinear function. Using implicit differentiation we train the free-energy landscape directly: standard chemical potentials, transition-state energies and thermodynamic drives. Increasingly large networks generated by elementary ligation and cleavage steps fit increasingly complex nonmonotonic polynomial functions, with expressivity scaling logarithmically with network size, predicted primarily by the number of reactions. Training individual energetic parameter classes reveals that internal thermodynamic drives, capable of breaking detailed balance, dominate trainability, with comparable performances achieved only by pairs of parameter classes. These results identify nonequilibrium drive as the most effective single resource for steady-state computational expressivity in chemical reaction networks.