Inferring microbial interaction networks from metagenomic data using SgLV-EKF algorithm
Inferring microbial interaction networks from metagenomic data using SgLV-EKF algorithm
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DOI：
10.1186/s12864-017-3605-x
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发表时间：
2017-01-01
期刊：
影响因子：
4.4
通讯作者：
Younes, Ahmad Bani
中科院分区：
文献类型：
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作者：
Alshawaqfeh, Mustafa;Serpedin, Erchin;Younes, Ahmad Bani
背景：推断微生物相互作用网络(MIN)并对其动力学进行建模对于了解细菌生态系统的机制以及设计抗生素和/或益生菌治疗方法至关重要。最近，人们提出了几种利用广义Lotka-Volterra(GLV)模型来推断MIN的方法。这些模型的主要缺点包括这些模型只考虑测量噪声，而没有考虑潜在动力学中的不确定性。此外，推断MIN的特点是观察数量有限和调节机制中的非线性。因此，需要新的估计技术来应对这些挑战。结果：本文提出了SgLV-EKF：一种采用扩展卡尔曼滤波(EKF)算法对最小动态建模的随机GLV模型。具体地说，SgLV-EKF通过向动态模型添加噪声项来补偿建模不确定性，从而对MIN进行随机建模。这种随机模型比传统的GLV模型更逼真，后者假设最小动态完全由GLV方程控制。在明确了随机模型的结构之后，我们提出了扩展卡尔曼滤波来估计最小值。在两个合成数据集和两个真实数据集上，将SgLV-EKF算法与两个基于相似度的算法、一个基于积分族的算法和两个基于回归的算法进行了比较。第一个数据集模拟了测量数据中的随机性，而第二个数据集结合了潜在动态中的不确定性。真实的数据集是由最近一项与抗生素介导的艰难梭菌感染有关的研究提供的。实验结果表明，SgLV-EKF算法在抗量测噪声、建模误差和动态跟踪方面均优于其他方法。结论：性能分析表明，SgLV-EKF算法是推断和跟踪MINS的一种强大而可靠的工具。
Background: Inferring the microbial interaction networks (MINs) and modeling their dynamics are critical in understanding the mechanisms of the bacterial ecosystem and designing antibiotic and/or probiotic therapies. Recently, several approaches were proposed to infer MINs using the generalized Lotka-Volterra (gLV) model. Main drawbacks of these models include the fact that these models only consider the measurement noise without taking into consideration the uncertainties in the underlying dynamics. Furthermore, inferring the MIN is characterized by the limited number of observations and nonlinearity in the regulatory mechanisms. Therefore, novel estimation techniques are needed to address these challenges.Results: This work proposes SgLV-EKF: a stochastic gLV model that adopts the extended Kalman filter (EKF) algorithm to model the MIN dynamics. In particular, SgLV-EKF employs a stochastic modeling of the MIN by adding a noise term to the dynamical model to compensate for modeling uncertainties. This stochastic modeling is more realistic than the conventional gLV model which assumes that the MIN dynamics are perfectly governed by the gLV equations. After specifying the stochastic model structure, we propose the EKF to estimate the MIN. SgLV-EKF was compared with two similarity-based algorithms, one algorithm from the integral-based family and two regression-based algorithms, in terms of the achieved performance on two synthetic data-sets and two real data-sets. The first data-set models the randomness in measurement data, whereas, the second data-set incorporates uncertainties in the underlying dynamics. The real data-sets are provided by a recent study pertaining to an antibiotic-mediated Clostridium difficile infection. The experimental results demonstrate that SgLV-EKF outperforms the alternative methods in terms of robustness to measurement noise, modeling errors, and tracking the dynamics of the MIN.Conclusions: Performance analysis demonstrates that the proposed SgLV-EKF algorithm represents a powerful and reliable tool to infer MINs and track their dynamics.