Modeling non-genetic information dynamics in cells using reservoir computing

TL;DR

This paper proposes a novel model of cellular information processing based on ion fluxes and reservoir computing, suggesting that ion gradients enable dynamic cellular responses and learning of complex behaviors.

Contribution

It introduces a quasi-physical Cell-Reservoir model that captures intra-cellular ion dynamics as a form of reservoir computing, linking ion fluxes to cellular information processing.

Findings

Ion dynamics enable rapid cellular responses to environmental changes.

The model aligns with experimental observations of cellular ion flux behavior.

Ion gradients facilitate complex nonlinear cellular information processing.

Abstract

Virtually all cells use energy and ion-specific membrane pumps to maintain large transmembrane gradients of Na$^+$, K$^+$, Cl$^-$, Mg$^{++}$, and Ca$^{++}$. Although they consume up to 1/3 of a cell's energy budget, the corresponding evolutionary benefit of transmembrane ion gradients remain unclear. Here, we propose that ion gradients enable a dynamic and versatile biological system that acquires, analyzes, and responds to environmental information. We hypothesize environmental signals are transmitted into the cell by ion fluxes along pre-existing gradients through gated ion-specific membrane channels. The consequent changes of cytoplasmic ion concentration can generate a local response and orchestrate global or regional responses through wire-like ion fluxes along pre-existing and self-assembling cytoskeleton to engage the endoplasmic reticulum, mitochondria, and nucleus. Here, we frame our hypothesis through a quasi-physical (Cell-Reservoir) model that treats intra-cellular ion-based information dynamics as a sub-cellular process permitting spatiotemporally resolved cellular response that is also capable of learning complex nonlinear dynamical cellular behavior. We demonstrate the proposed ion dynamics permits rapid dissemination of response to information extrinsic perturbations that is consistent with experimental observations.