History Dependence in a Chemical Reaction Network Enables Dynamic Switching

Dmitrii V. Kriukov, A. Hazal Koyuncu, Albert S.Y. Wong*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
54 Downloads (Pure)


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.

Original languageEnglish
Article number2107523
Issue number16
Early online date21 Apr 2022
Publication statusPublished - Apr 2022


  • adaptation
  • autocatalysis
  • bistability
  • chemical reaction networks
  • hysteresis
  • molecular computing
  • UT-Hybrid-D


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