论文标题

通过记忆扩展微生物生长的单模型

Extending the Monod Model of Microbial Growth with Memory

论文作者

Amirian, Mohammad M., Irwin, Andrew J., Finkel, Zoe V.

论文摘要

Monod的模型描述了使用细胞外资源浓度的双曲线功能的微生物的生长。在波动或有限的资源浓度下,该模型在实验数据上的性能较差,从而激发了更复杂的下垂模型,具有随时间变化的内部存储池。我们扩展了monod模型以结合过去条件的记忆,并添加了由分数演算分析激励的单个参数。我们展示了如何在生物环境中解释内存元素并描述其与资源存储池的联系。在非平衡条件下的氮饥饿下,我们通过从硅藻(T. pseudonana和T. weissflogii)的实验室培养物获得的模拟和经验数据来验证该模型,并在全球影响力的Phytopplankton分类群中(Micromonas sp。和O. Tauri)(Micromonas sp。和O. Tauri)获得。使用统计分析,我们表明我们的单模内记忆模型估计生长速率,细胞密度和资源浓度以及下垂模型,同时需要一个较小的状态变量。我们的简单模型可以以比目前可实现的计算成本低的计算成本来改善复杂地球系统模型中浮游植物动力学的描述。

Monod's model describes the growth of microorganisms using a hyperbolic function of extracellular resource concentration. Under fluctuating or limited resource concentrations this model performs poorly against experimental data, motivating the more complex Droop model with a time-varying internal storage pool. We extend the Monod model to incorporate memory of past conditions, adding a single parameter motivated by a fractional calculus analysis. We show how to interpret the memory element in a biological context and describe its connection to a resource storage pool. Under nitrogen starvation at non-equilibrium conditions, we validate the model with simulations and empirical data obtained from lab cultures of diatoms (T. pseudonana and T. weissflogii) and prasinophytes (Micromonas sp. and O. tauri), globally influential phytoplankton taxa. Using statistical analysis, we show that our Monod-memory model estimates the growth rate, cell density, and resource concentration as well as the Droop model while requiring one less state variable. Our simple model may improve descriptions of phytoplankton dynamics in complex earth system models at a lower computational cost than is presently achievable.

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