Identifying the dynamics of small and large microbial communities

描述（由申请人提供）：该项目的目标是开发一个计算框架，可以从小型和大型微生物群落的时间序列中学习微生物-微生物和宿主-微生物的相互作用。对人类相关微生物群落的分子分析已经开始揭示群落结构与人类健康和疾病之间的联系。从这些初步研究中还可以明显看出，宿主相关微生物组的动态性质——即使在健康个体中，微生物组即使在几天内也会发生变化，更不用说在人的一生中更长的时间里了。这些变化，以及作为疾病开始或治疗后健康恢复基础的微生物组变化，只有通过阐明社区成员之间复杂的相互作用网络才能充分理解。这些网络目前还不能通过实验观察到，因为科学家们还没有完全描述社区成员的基因组结构。目前的建议目标是通过间接检查有关宿主相关社区组成的时间序列数据来推断相互作用网络及其参数的方法。新的分析方法将被开发出来，可以从时间序列数据中稳健地学习动态模型的参数。此外，将探索几种方法来降低由数百到数千个微生物组成的大型微生物群落衍生的系统的复杂性。开发的方法将在模拟和真实数据集上进行评估，以验证方法，并评估实验参数（如微生物组数据的处理，噪声水平或时间序列信息的稀疏度）对重建典型遇到的人类相关微生物群落动态模型的能力的影响。

DESCRIPTION (provided by applicant): The goal of this project is to develop a computational framework that can learn microbe-microbe and host-microbe interactions from time series of small and large microbial communities. Molecular analyses of human-associated microbial communities have already started to reveal associations between community structure and human health and disease. Also apparent from these initial studies is the dynamic nature of the host-associated microbiomes ¿ even in healthy individuals the microbiome changes even within days, let alone over longer periods of a person's life. These changes, as well as the microbiome changes that underlie the initiation of disease, or the restoration of health after treatment, can only be fully understood by elucidating the complex networks of interactions between the members of the community. These networks cannot currently be observed experimentally as scientists have yet to fully characterize the genomic structure of the members of the community. The current proposal targets methods for inferring the interaction networks, and their parameters, by indirectly examining time-series data about the composition of host-associated communities. Novel analytical methods will be developed that can robustly learn the parameters of dynamic models from time-series data. Furthermore, several approaches will be explored for reducing the complexity of the systems derived from large microbial communities comprising hundreds to thousands of microbes. The methods developed will be evaluated on simulated and real datasets, both to validate the methods, and to evaluate the effects of experimental parameters (such as processing of microbiome data, level of noise, or sparsity of time-series information) on the ability to reconstruct dynamic models of typically encountered human-associated microbial communities.