Living Bacterial Reservoir Computers for Information Processing and Sensing

Abstract

We introduce a systems-level approach to sensing and computing in which Escherichia coli acts as a living reservoir computer, performing complex information processing through its native growth responses without requiring genetic modification or specialized instrumentation. We validate this framework by accurately classifying early-stage COVID-19 plasma samples (mild vs. severe) using only bacterial growth data, highlighting a diagnostic potential without infrastructure-dependent methods. By controlling nutrient media compositions, we also demonstrate that E. coli growth encodes nonlinear transformations that outperform linear regression, support vector machines, and multilayer perceptrons across diverse regression and classification tasks. Using simulations across genome-scale metabolic models from multiple bacterial species, we establish a strong link between phenotypic diversity and computational capacity, showing that learning capacities scale with the diversity of metabolic phenotypes. These findings position biological reservoir computing as a robust, scalable, and low-cost platform for intelligent biosensing, diagnostics, and hybrid bio-digital computation, while providing new mechanistic insights into the computational capabilities of living systems.