Thompson Jaron, Connors Bryce M, Zavala Victor M, Venturelli Ophelia S
Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
bioRxiv. 2025 Jul 12:2025.07.08.663743. doi: 10.1101/2025.07.08.663743.
Microbial communities play essential roles in shaping ecosystem functions and predictive modeling frameworks are crucial for understanding, controlling, and harnessing their properties. Competition and cross-feeding of metabolites drives microbiome dynamics and functions. Existing mechanistic models that capture metabolite-mediated interactions in microbial communities have limited flexibility due to rigid assumptions. While machine learning models provide flexibility, they require large datasets, are challenging to interpret, and can over-fit to experimental noise. To overcome these limitations, we develop a physics-constrained machine learning model, which we call the Neural Species Mediator (NSM), that combines a mechanistic model of metabolite dynamics with a machine learning component. The NSM is more accurate than mechanistic or machine learning components on experimental datasets and provides insights into direct biological interactions. In summary, embedding a neural network into a mechanistic model of microbial community dynamics improves prediction performance and interpretability compared to its constituent mechanistic or machine learning components.
微生物群落对塑造生态系统功能起着至关重要的作用，而预测建模框架对于理解、控制和利用其特性至关重要。代谢物的竞争和交叉喂养驱动着微生物群落的动态和功能。现有的捕捉微生物群落中代谢物介导相互作用的机制模型由于严格的假设而灵活性有限。虽然机器学习模型具有灵活性，但它们需要大量数据集，难以解释，并且可能过度拟合实验噪声。为了克服这些限制，我们开发了一种物理约束的机器学习模型，我们称之为神经物种介导器（NSM），它将代谢物动力学的机制模型与机器学习组件相结合。在实验数据集上，NSM比机制或机器学习组件更准确，并能洞察直接的生物相互作用。总之，与组成它的机制或机器学习组件相比，将神经网络嵌入微生物群落动态的机制模型可提高预测性能和可解释性。
