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.

翻译：生物系统因其通过复杂内部动力学自然处理环境信息的能力而成为有前景的计算基板。在本研究中，我们探究细菌代谢模型能否作为物理储层，以及其计算性能是否可以从与可分性和相似性相关的动力学特性进行预测。我们采用动态通量平衡分析（dFBA）方法，以葡萄糖和木糖浓度为输入、生长曲线为储层状态，模拟了五种细菌物种、一种酵母物种和29个大肠杆菌单基因缺失突变体的生长动力学。计算性能通过线性读出器在随机非线性分类任务上进行评估，而与可分性和相似性相关的储层特性则通过从生长曲线状态矩阵计算的核秩和泛化秩来表征。几种微生物模型实现了高分类准确率，表明细菌代谢动力学能够支持非线性计算。不同物种间观察到明显差异，部分模型收敛更快，而另一些则达到更高最大准确率，揭示了收敛速度与峰值性能之间的权衡。相比之下，所有大肠杆菌突变体均受野生型模型主导，表明基因缺失降低了高效计算所需的动力学丰富性。核秩与泛化秩之差通常与准确率提升相关，但不同模型间的偏差及低秩值处的敏感性限制了其实际预测能力。总体而言，这些结果表明细菌代谢模型是储层计算的有前景基板，并为识别具有有利计算特性以用于未来实验实现的微生物菌株迈出了第一步。