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 non-monotonic 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 non-equilibrium drive as the most effective single resource for steady-state computational expressivity in chemical reaction networks.
About the Speaker
Marco is a PhD student at the Complex Systems Lab in Barcelona under supervision of Prof. Ricard Solè and he collaborates with Dayhoff Labs on understanding how chemistry computes and becomes biology. He's a Physicist by training and loves programming and simulations.
Practical
This seminar is taking place online through Microsoft Teams. Join here.
