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迭代恒化器:一个将生物合成与生态和进化时间尺度上的营养循环联系起来的建模框架。

Iterative chemostat: A modelling framework linking biosynthesis to nutrient cycling on ecological and evolutionary time scales.

机构信息

Bryan College of Health Sciences, Bryan Medical Center, Lincoln NE 68506.

出版信息

Math Biosci Eng. 2019 Jan 30;16(2):990-1004. doi: 10.3934/mbe.2019046.

Abstract

In the classical chemostat, the output of the system has no effect on its input. This contrasts with many ecological systems, where the output at the end of a growing season affects nutrient inputs for subsequent seasons. Here, an iterative-continuous modelling framework is introduced that retains the structure of classical ecological models within each iteration but accounts for nutrient feedbacks between iterations. As an example, the framework is applied to the classical chemostat model, where nutrient outputs affect the supply ratio at each iteration. Furthermore, the biotic parameters in the model, including organismal demands for nitrogen (N) and phosphorus (P), are linked to core biogenic processes-protein and rRNA synthesis. This biosynthesis is further deconstructed into 11 biological constants and rates, most of which are deeply shared among all organisms. By linking the fundamental macromolecular machinery to the cycling of nutrients on the ecosystem scale, the framework enables to rigorously formulate qualitative and quantitative questions about the evolution of nutrient ratios and the existence of stoichiometric attractors, such as the puzzling persistence of the Redfield N:P ratio of 16 in the ocean. While the framework presented here is theoretical, it readily permits setting up empirical experiments for testing its predictions.

摘要

在经典恒化器中,系统的输出对输入没有影响。这与许多生态系统形成对比,在这些生态系统中,生长季节结束时的输出会影响后续季节的养分输入。在这里,引入了一种迭代连续建模框架,该框架在每次迭代中保留经典生态模型的结构,但考虑了迭代之间的养分反馈。作为一个例子,该框架应用于经典的恒化器模型,其中养分输出会影响每次迭代的供应比例。此外,模型中的生物参数,包括生物体对氮(N)和磷(P)的需求,与核心生物过程——蛋白质和 rRNA 合成有关。这种生物合成进一步分解为 11 个生物学常数和速率,其中大多数在所有生物体中都有深度共享。通过将基本的大分子机器与生态系统尺度上的养分循环联系起来,该框架使人们能够严格地提出关于养分比例的演化和化学计量吸引子(如海洋中令人困惑的 Redfield N:P 比例 16 的存在)的定性和定量问题。虽然这里提出的框架是理论性的,但它很容易为测试其预测设置经验实验。

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