论文标题
使用概率宏观化学建模推断微生物生物量的产量和细胞重量
Inferring Microbial Biomass Yield and Cell Weight using Probabilistic Macrochemical Modeling
论文作者
论文摘要
生长速率和生物量产率是微生物研究中使用的关键描述符，以了解微生物物种如何应对环境变化。其中，通常使用细胞计数和进料底物的测量值获得生物质产量估计。但是，这些数量被测量噪声扰动。也许最关键的是，根据需要评估产量的细胞计数估算生物量取决于假定的细胞重量。这些假设上的噪声和差异可能会导致有关微生物反应的结论发生重大变化。本文提出了一种使用微生物生长的概率宏观化学模型来应对这些挑战的方法。结果表明，可以开发模型以充分使用实验数据，放松假设并大大提高对细胞重量的先验估计的鲁棒性，并提供关键参数的不确定性估计。在特定案例研究的背景下，该方法证明了这种方法，并且使用合成生成的微生物生长数据在几种情况下验证了估计特征。
Growth rates and biomass yields are key descriptors used in microbiology studies to understand how microbial species respond to changes in the environment. Of these, biomass yield estimates are typically obtained using cell counts and measurements of the feed substrate. These quantities are perturbed with measurement noise however. Perhaps most crucially, estimating biomass from cell counts, as needed to assess yields, relies on an assumed cell weight. Noise and discrepancies on these assumptions can lead to significant changes in conclusions regarding the microbes' response. This article proposes a methodology to address these challenges using probabilistic macrochemical models of microbial growth. It is shown that a model can be developed to fully use the experimental data, relax assumptions and greatly improve robustness to a priori estimates of the cell weight, and provides uncertainty estimates of key parameters. This methodology is demonstrated in the context of a specific case study and the estimation characteristics are validated in several scenarios using synthetically generated microbial growth data.
