Multiscale models driving hypothesis and theory-based research in microbial ecology.

Multiscale models driving hypothesis and theory-based research in microbial ecology.
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DOI:
10.1098/rsfs.2023.0008
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发表时间:
2023-08-06
期刊:
影响因子:
4.4
通讯作者:
--
中科院分区:
生物学2区
文献类型:
--
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微生物生态学中基于假设和理论的研究被忽视,而倾向于那些描述性的和旨在收集未培养微生物物种数据的研究。这种倾向限制了我们创造新的微生物群落动力学机制解释的能力,阻碍了当前环境生物技术的改进。我们建议,多尺度建模自下而上的方法(拼凑子系统,产生更复杂的系统)可以作为一个框架,以产生机械的假设和理论(在硅片自下而上的方法)。要做到这一点,正式理解的数学模型设计需要连同一个系统的程序,在硅片自下而上的方法的应用。排除的信念,建模前的实验是必不可少的,我们建议,数学建模可以用来作为一种工具,通过验证微生物生态学的理论原理,指导实验。我们的目标是开发有效整合实验和建模工作的方法,以实现上级水平的预测能力。
Hypothesis and theory-based studies in microbial ecology have been neglected in favour of those that are descriptive and aim for data-gathering of uncultured microbial species. This tendency limits our capacity to create new mechanistic explanations of microbial community dynamics, hampering the improvement of current environmental biotechnologies. We propose that a multiscale modelling bottom-up approach (piecing together sub-systems to give rise to more complex systems) can be used as a framework to generate mechanistic hypotheses and theories (in-silico bottom-up methodology). To accomplish this, formal comprehension of the mathematical model design is required together with a systematic procedure for the application of the in-silico bottom-up methodology. Ruling out the belief that experimentation before modelling is indispensable, we propose that mathematical modelling can be used as a tool to direct experimentation by validating theoretical principles of microbial ecology. Our goal is to develop methodologies that effectively integrate experimentation and modelling efforts to achieve superior levels of predictive capacity.
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