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History Dependence in a Chemical Reaction Network Enables Dynamic Switching.

Dmitrii V Kriukov1, A Hazal Koyuncu1, Albert S Y Wong1,2,3

  • 1Department of Molecules and Materials, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, Enschede, 7522 NH, The Netherlands.

Small (Weinheim an Der Bergstrasse, Germany)
|March 8, 2022
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This study presents a three-component enzymatic network that exhibits dynamic switching and neuromorphic behaviors. This research pioneers using systems chemistry for chemical reaction networks to perform neural network computing.

Keywords:
adaptationautocatalysisbistabilitychemical reaction networkshysteresismolecular computing

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Area of Science:

  • Biochemistry
  • Systems Chemistry
  • Chemical Engineering

Background:

  • Enzymatic autocatalytic networks are fundamental in biological systems.
  • Achieving dynamic switching and complex behaviors in simple chemical systems remains a challenge.
  • Out-of-equilibrium conditions are crucial for non-linear chemical dynamics.

Purpose of the Study:

  • To design and characterize an enzymatic autocatalytic network capable of dynamic switching.
  • To explore neuromorphic computing capabilities within a minimal chemical system.
  • To establish a foundation for transforming chemical networks into computational platforms.

Main Methods:

  • Utilized a continuous flow stirred tank reactor to maintain out-of-equilibrium conditions.
  • Developed a 'linear inhibition sweep' technique to perturb the network dynamics.
  • Analyzed the network's response to controlled perturbations.

Main Results:

  • Demonstrated dynamic switching in a three-component enzymatic network (trypsinogen, soybean trypsin inhibitor, trypsin).
  • Observed emergent neuromorphic behaviors including hysteresis, synchronization, resonance, and adaptation.
  • Confirmed the system's ability to perform complex functions with minimal components.

Conclusions:

  • A simple, three-component enzymatic network can exhibit sophisticated dynamic and computational behaviors.
  • This work represents a significant step towards developing chemical reaction networks for neural network computing.
  • The developed methodology provides a new approach for studying complex chemical systems.