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Cytomorphic Electronic Systems: A review and perspective.

Douglas Raymond Beahm1, Yijie Deng1, Tanner G Riley2

  • 1Thayer School of Engineering, Dartmouth College.

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|March 4, 2022
PubMed
Summary
This summary is machine-generated.

Cytomorphic systems leverage common thermodynamic laws to simulate complex biochemical reactions efficiently. These systems offer a significant advantage over digital computers for systems biology simulations, enabling faster drug discovery and synthetic biology design.

Keywords:
COVID-19analog computingbiological design automationcytomorphicdrug discoverysupercomputersynthetic biologysystems biology

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Area of Science:

  • Biophysics
  • Computational Biology
  • Analog Computing

Background:

  • Stochastic biochemical reactions and transistor electron flux follow common Boltzmann-exponential thermodynamic laws.
  • Existing digital systems face limitations in simulating large-scale, complex, and nonlinear biological networks due to rapid scaling in simulation time.

Purpose of the Study:

  • To introduce and explore the application of cytomorphic systems for simulating stochastic biochemical reaction networks.
  • To highlight the advantages of cytomorphic systems over digital systems for systems biology and computational medicine.

Main Methods:

  • Mapping and simulating arbitrary stochastic biochemical reaction networks using cytomorphic systems built on subthreshold analog circuits.
  • Utilizing digitally programmable parameters and connectivity to compile complex biological networks onto cytomorphic hardware.

Main Results:

  • Cytomorphic systems accurately model noisy, nonlinear, asynchronous, stiff, and non-modular feedback dynamics in biological networks.
  • Simulation time for stochastic networks scales constantly with the number of reactions or molecules in cytomorphic systems, unlike digital systems.

Conclusions:

  • Cytomorphic systems offer a powerful new paradigm for large-scale systems biology simulations, enabling supercomputing capabilities for complex biological models.
  • Applications include rapid drug-cocktail formulation, cancer network simulation, synthetic biology circuit design, and advancing personalized medicine.