# From Electric Circuits to Chemical Networks

arXiv: https://arxiv.org/abs/1812.03308 · HTML: https://mlsanity.com/paper/1812.03308/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2018.

## Built on by (most cited first, 1 of 1)

- [Neural CRNs: A Natural Implementation of Learning in Chemical Reaction Networks](https://mlsanity.com/paper/2409.00034/) (2024)

## Abstract (arXiv)

Electric circuits manipulate electric charge and magnetic flux via a small set of discrete components to implement useful functionality over continuous time-varying signals represented by currents and voltages. Much of the same functionality is useful to biological organisms, where it is implemented by a completely different set of discrete components (typically proteins) and signal representations (typically via concentrations). We describe how to take a linear electric circuit and systematically convert it to a chemical reaction network of the same functionality, as a dynamical system. Both the structure and the components of the electric circuit are dissolved in the process, but the resulting chemical network is intelligible. This approach provides access to a large library of well-studied devices, from analog electronics, whose chemical network realization can be compared to natural biochemical networks, or used to engineer synthetic biochemical networks.