Predicting microbial growth dynamics in response to nutrient availability.
Predicting microbial growth dynamics in response to nutrient availability.
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DOI：
10.1371/journal.pcbi.1008817
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发表时间：
2021-03
影响因子：
4.3
通讯作者：
Gudelj I
中科院分区：
文献类型：
--
作者：
Nev OA;Lindsay RJ;Jepson A;Butt L;Beardmore RE;Gudelj I
开发精确预测微生物生长动态的数学模型仍然是生态学、进化论、生物技术和公共卫生领域的关键挑战。为了繁殖和生长，微生物需要从环境中吸收必要的养分，而数学模型经典地假设养分摄取率是营养浓度的饱和函数。在自然界中，微生物在所有环境尺度上经历不同水平的养分可获得性，但影响养分吸收功能的参数通常是针对单个初始养分浓度进行估计的。这阻碍了模型在环境条件变化时准确捕捉微生物动态。为了解决这个问题，我们对一系列微生物进行了生长实验，包括人类真菌病原体、面包师酵母和常见的大肠菌群，并发现了以下模式。我们观察到，最大养分吸收速率和生物量都是初始营养浓度的递减函数。生物产量和初始营养浓度之间的关系已经从第一代谢原理推导出函数形式，这里我们也推导出最大养分吸收速率和初始营养浓度之间的关系形式。与保持生长参数不变相比，将这两个功能整合到微生物生长模型中，可以实现可变的生长参数，并使我们能够在一定的初始营养浓度范围内大幅提高对微生物动态的预测。我们预测微生物种群动态的能力对生态学、进化论、生物技术和公共卫生领域至关重要。然而，目前用于预测微生物生长的数学模型存在固有的局限性。它们是使用在单一初始营养浓度下执行的微生物生长的经验测量来进行参数化的。这忽视了这样一个事实：在自然界中，微生物在所有环境尺度上面临不同程度的营养可获得性：从危重病人血液中的血糖波动到海洋环境中溶解的有机碳波动。目前的文献压倒性地表明，在单一初始营养浓度下估计生长参数阻碍了模型在环境条件变化时准确地捕捉微生物动态。在这里，我们使用数学建模和实验室实验之间的相互作用来解决这个问题，这些实验涵盖了人类真菌病原体、常见的大肠菌群和面包酵母。我们提出了一种模型方法，将生长参数作为初始营养浓度的函数纳入其中。重要的是，我们证明，与假设固定生长参数的经典模型相比，我们的方法在预测微生物生长和不同初始营养浓度下物种间竞争的结果方面表现得明显更好。
Developing mathematical models to accurately predict microbial growth dynamics remains a key challenge in ecology, evolution, biotechnology, and public health. To reproduce and grow, microbes need to take up essential nutrients from the environment, and mathematical models classically assume that the nutrient uptake rate is a saturating function of the nutrient concentration. In nature, microbes experience different levels of nutrient availability at all environmental scales, yet parameters shaping the nutrient uptake function are commonly estimated for a single initial nutrient concentration. This hampers the models from accurately capturing microbial dynamics when the environmental conditions change. To address this problem, we conduct growth experiments for a range of micro-organisms, including human fungal pathogens, baker’s yeast, and common coliform bacteria, and uncover the following patterns. We observed that the maximal nutrient uptake rate and biomass yield were both decreasing functions of initial nutrient concentration. While a functional form for the relationship between biomass yield and initial nutrient concentration has been previously derived from first metabolic principles, here we also derive the form of the relationship between maximal nutrient uptake rate and initial nutrient concentration. Incorporating these two functions into a model of microbial growth allows for variable growth parameters and enables us to substantially improve predictions for microbial dynamics in a range of initial nutrient concentrations, compared to keeping growth parameters fixed. Our ability to predict microbial population dynamics is of key importance for the fields of ecology, evolution, biotechnology, and public health. Yet, current mathematical models used to predict microbial growth have an inherent limitation. They are parameterised using empirical measurements of microbial growth performed at a single initial nutrient concentration. This overlooks the fact that in nature microbes face different levels of nutrient availability at all environmental scales: from glucose fluctuations in the blood of critically ill patients to dissolved organic carbon fluctuations in marine environments. Current literature overwhelmingly suggests that estimating growth parameters at a single initial nutrient concentration hampers the models from accurately capturing microbial dynamics when the environmental conditions change. Here we tackle this problem using an interplay between mathematical modelling and laboratory experiments spanning human fungal pathogens, common coliform bacteria, and baker’s yeast. We propose a modelling approach that incorporates growth parameters as a function of initial nutrient concentration. Importantly, we demonstrate that our approach performs significantly better at predicting microbial growth and the outcomes of between-species competition across different initial nutrient concentrations, compared to the classical models which assume fixed growth parameters.
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