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首页> 外文期刊>Environmental Science & Technology >Guiding Mineralization Co-Culture Discovery Using Bayesian Optimization
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Guiding Mineralization Co-Culture Discovery Using Bayesian Optimization

机译：使用贝叶斯优化指导矿化共文化发现

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摘要

Many disciplines rely on testing combinations of compounds, materials, proteins, or bacterial species to drive scientific discovery. It is time-consuming and expensive to determine experimentally, via trial-and-error or random selection approaches, which of the many possible combinations will lead to desirable outcomes. Hence, there is a pressing need for more rational and efficient experimental design approaches to reduce experimental effort. In this work, we demonstrate the potential of machine learning methods for the in silico selection of promising co-culture combinations in the application of bioaugmentation. We use the example of pollutant removal in drinking water treatment plants, which can be achieved using co-cultures of a specialized pollutant degrader with combinations of bacterial isolates. To reduce the experimental effort needed to discover high-performing combinations, we propose a data-driven experimental design. Based on a dataset of mineralization performance for all pairs of 13 bacterial species co-cultured with MSH1, we built a Gaussian process regression model to predict the Gompertz mineralization parameters of the co-cultures of two and three species, based on the single-strain parameters. We subsequently used this model in a Bayesian optimization scheme to suggest potentially high-performing combinations of bacteria. We achieved good performance with this approach, both for predicting mineralization parameters and for selecting effective co-cultures, despite the limited dataset. As a novel application of Bayesian optimization in bioremediation, this experimental design approach has promising applications for highlighting co-culture combinations for in vitro testing in various settings, to lessen the experimental burden and perform more targeted screenings.
机译：许多学科依靠测试化合物，材料，蛋白质或细菌物种的组合来推动科学发现。通过反复试验或随机选择方法进行实验确定是耗时且昂贵的，许多可能组合中的哪一种将导致理想的结果。因此，迫切需要更合理和有效的实验设计方法以减少实验工作量。在这项工作中，我们展示了机器学习方法在生物增强应用中计算机筛选有希望的共培养组合的潜力。我们以饮用水处理厂中的污染物去除为例，这可以通过将专门的污染物降解剂与细菌分离物的组合进行共培养来实现。为了减少发现高性能组合所需的实验工作，我们提出了一种数据驱动的实验设计。基于与MSH1共培养的所有13种细菌对的成矿性能数据集，我们建立了一个高斯过程回归模型，以基于单菌株预测两种和三种物种的共培养物的Gompertz矿化参数。参数。随后，我们在贝叶斯优化方案中使用了该模型，以建议潜在的高性能细菌组合。尽管数据集有限，但该方法在预测矿化参数和选择有效的共培养方面均取得了良好的性能。作为贝叶斯优化在生物修复中的一种新颖应用，这种实验设计方法在突出共培养组合以在各种环境中进行体外测试，减轻实验负担并进行更有针对性的筛选方面具有广阔的应用前景。

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