History Dependence in a Chemical Reaction Network Enables Dynamic Switching

This work describes an enzymatic autocatalytic network capable of dynamic switching under out-of-equilibrium conditions. The network, wherein a molecular fuel (trypsinogen) and an inhibitor (soybean trypsin inhibitor) compete for a catalyst (trypsin), is kept from reaching equilibria using a continuous flow stirred tank reactor. A so-called 'linear inhibition sweep' is developed (i.e., a molecular analogue of linear sweep voltammetry) to intentionally perturb the competition between autocatalysis and inhibition, and used to demonstrate that a simple molecular system, comprising only three components, is already capable of a variety of essential neuromorphic behaviors (hysteresis, synchronization, resonance, and adaptation). This research provides the first steps in the development of a strategy that uses the principles in systems chemistry to transform chemical reaction networks into platforms capable of neural network computing.

Automated summary

This work presents an enzymatic autocatalytic network capable of dynamic switching under out-of-equilibrium conditions. By perturbing the competition between autocatalysis and inhibition, a simple molecular system comprising three components demonstrates a variety of essential neuromorphic behaviors.