What Makes a Bacterial Model a Good Reservoir Computer? Predicting Performance from Separability and Similarity

2026-04-17

AI Summary

This study presents the first systematic evaluation of bacterial metabolic networks as physical reservoir computers. Using dynamic flux balance analysis (dFBA), the authors simulate the growth dynamics of various microorganisms under time-varying sugar concentration inputs, treating these metabolic states as reservoir representations and coupling them with linear readouts to perform nonlinear classification tasks. The results demonstrate that model organisms such as wild-type *Escherichia coli* achieve high classification accuracy, confirming the inherent nonlinear computational capacity of metabolic dynamics. Single-gene knockouts substantially degrade performance, revealing the detrimental impact of genetic perturbations on dynamical richness. Furthermore, the work identifies a trade-off between convergence speed and peak performance and proposes the difference between kernel rank and generalization rank as an effective predictor of computational capability.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Machine Learning: Bio-inspired Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Biological systems are promising substrates for computation because they naturally process environmental information through complex internal dynamics. In this study, we investigate whether bacterial metabolic models can act as physical reservoirs and whether their computational performance can be predicted from dynamical properties linked to separability and similarity. We simulated the growth dynamics of five bacterial species, one yeast species, and 29 Escherichia coli single-gene deletion mutants using dynamic flux balance analysis (dFBA), with glucose and xylose concentrations as inputs and growth curves as reservoir states. Computational performance was assessed on random nonlinear classification tasks using a linear readout, while reservoir properties linked to separability and similarity were characterised through kernel and generalisation ranks computed from growth-curve state matrices. Several microbial models achieved high classification accuracy, showing that bacterial metabolic dynamics can support nonlinear computation. Clear differences were observed between species, with some models converging more rapidly and others reaching higher maximum accuracy, revealing a trade-off between convergence speed and peak performance. In contrast, all E. coli mutants were dominated by the wild-type model, suggesting that gene deletions reduce the dynamical richness required for efficient computation. The difference between kernel and generalisation ranks was generally associated with improved accuracy, but deviations across models and sensitivity at low rank values limited its predictive power in practice. Overall, these results show that bacterial metabolic models constitute promising substrates for reservoir computing and provide a first step towards identifying microbial strains with favourable computational properties for future experimental implementations.

Problem

• reservoir computing
• bacterial metabolic models
• separability
• similarity
• computational performance

Innovation

• reservoir computing
• bacterial metabolic models
• dynamic flux balance analysis
• separability and similarity
• kernel rank