Identification of microbiota dynamics using robust parameter estimation methods.
Identification of microbiota dynamics using robust parameter estimation methods.
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DOI：
10.1016/j.mbs.2017.09.009
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发表时间：
2017-12
影响因子：
4.3
通讯作者：
Pop M
中科院分区：
文献类型：
--
作者：
Chung M;Krueger J;Pop M
宿主体内微生物群落（微生物群）的组成在宿主健康中发挥着重要作用，更好地了解微生物群在宿主从健康到疾病的转变中的作用，反之亦然，可能会带来新的医学治疗方法。实现这种理解的第一步是对微生物群的相互作用动力学进行建模，考虑到动力学的复杂性和收集足够数据的困难，这可能极具挑战性。已经开发出主微分分析、动态通量估计等方法来克服这些挑战。尽管有这些优点，这些方法在数学生物学等领域仍然没有得到充分利用，其潜在原因之一是它们的复杂实现。虽然本文侧重于将主微分分析应用于微生物群数据，但我们还提供了有关该方法的推导和数值的全面详细信息，并包括一个功能实现，以供读者参考。为了进一步验证这些方法，我们使用模拟研究证明了主微分分析的可行性，然后将该方法应用于肠道和阴道微生物群数据。在处理这些数据时，我们捕获了经过实验证实的动力学，同时也揭示了对系统动力学的潜在新见解。
The compositions of in-host microbial communities (microbiota) play a significant role in host health, and a better understanding of the microbiota’s role in a host’s transition from health to disease or vice versa could lead to novel medical treatments. One of the first steps toward this understanding is modeling interaction dynamics of the microbiota, which can be exceedingly challenging given the complexity of the dynamics and difficulties in collecting sufficient data. Methods such as principal differential analysis, dynamic flux estimation, and others have been developed to overcome these challenges. Despite their advantages, these methods are still vastly underutilized in fields such as mathematical biology, and one potential reason for this is their sophisticated implementation. While this paper focuses on applying principal differential analysis to microbiota data, we also provide comprehensive details regarding the derivation and numerics of this method and include a functional implementation for readers’ benefit. For further validation of these methods, we demonstrate the feasibility of principal differential analysis using simulation studies and then apply the method to intestinal and vaginal microbiota data. In working with these data, we capture experimentally confirmed dynamics while also revealing potential new insights into the system dynamics.
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影响因子：
17.1
作者：
Gajer P;Brotman RM;Bai G;Sakamoto J;Schütte UM;Zhong X;Koenig SS;Fu L;Ma ZS;Zhou X;Abdo Z;Forney LJ;Ravel J
通讯作者：
Ravel J
影响因子：
3.7
作者：
GEISSER, S;EDDY, WF
通讯作者：
EDDY, WF
影响因子：
1.8
作者：
Conrad, Matthias;Hubold, Christian;Peters, Achim
通讯作者：
Peters, Achim
影响因子：
4.7
作者：
Brendel, Marc;Bonvin, Dominique;Marquardt, Wolfgang
通讯作者：
Marquardt, Wolfgang
影响因子：
7
作者：
Chaffron, Samuel;Rehrauer, Hubert;von Mering, Christian
通讯作者：
von Mering, Christian