Modeling non-genetic information dynamics in cells using reservoir computing

Dipesh Niraula; Issam El Naqa; Jack Adam Tuszynski; Robert A Gatenby

doi:10.1016/j.isci.2024.109614

Modeling non-genetic information dynamics in cells using reservoir computing

iScience. 2024 Mar 28;27(4):109614. doi: 10.1016/j.isci.2024.109614. eCollection 2024 Apr 19.

Authors

Dipesh Niraula¹, Issam El Naqa¹, Jack Adam Tuszynski^{2

3

4}, Robert A Gatenby⁵

Affiliations

¹ Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA.
² Departments of Physics and Oncology, University of Alberta, Edmonton, AB, Canada.
³ Department of Data Science and Engineering, The Silesian University of Technology, 44-100 Gliwice, Poland.
⁴ Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin 10129, Italy.
⁵ Departments of Radiology and Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL, USA.

Abstract

Virtually all cells use energy-driven, ion-specific membrane pumps to maintain large transmembrane gradients of Na⁺, K⁺, Cl^-, Mg⁺⁺, and Ca⁺⁺, but the corresponding evolutionary benefit remains unclear. We propose that these gradients enable a dynamic and versatile biological system that acquires, analyzes, and responds to environmental information. We hypothesize that environmental signals are transmitted into the cell by ion fluxes along pre-existing gradients through gated ion-specific membrane channels. The consequent changes in cytoplasmic ion concentration can generate a local response or orchestrate global/regional cellular dynamics through wire-like ion fluxes along pre-existing and self-assembling cytoskeleton to engage the endoplasmic reticulum, mitochondria, and nucleus.

Keywords: Biological sciences; Computer science; Health sciences.

Grants and funding

R01 CA233487/CA/NCI NIH HHS/United States