Local energetic coupling enhances the expressivity of chemical computation
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.
Related
- Deep TMS modulates neurovascular responses and enhances dual-task computation in healthy adults
- Entrainment of prefrontal theta synchrony increases interbrain coupling and enhances social interaction in mouse models of autism
- Apospory Expressivity and Segregation in Intraspecific Hybrids of Paspalum notatum Flügge
- The Computational Value of Sensory-Aligned Receptive Fields Depends on Neuronal Expressivity
- On the Promise (and Practice) of Deep Comparative Methods: Expressivity, Limitations, and Design
BibTeX
@misc{tuccio2026local,
title = {{Local energetic coupling enhances the expressivity of chemical computation}},
author = {Marco Tuccio and Jason W. Rocks and Joshua E. Goldford},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.17339},
eprint = {2609.17339},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.17339},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Local energetic coupling enhances the expressivity of chemical computation
AU - Marco Tuccio
AU - Jason W. Rocks
AU - Joshua E. Goldford
PY - 2026
DA - 2026/09/15
PB - arXiv
DO - 10.48550/arxiv.2609.17339
UR - https://arxiv.org/abs/2609.17339
AB - 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.
ER -