Alshawaqfeh Mustafa, Serpedin Erchin, Younes Ahmad Bani
Bioinformatics and Genomic Signal Processing Lab, ECEN Dept., Texas A&M University, College Station, TX, 77843-3128, USA.
AE Dept., Khalifa University, Abu Dhabi, UAE.
BMC Genomics. 2017 Mar 27;18(Suppl 3):228. doi: 10.1186/s12864-017-3605-x.
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.
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.
Performance analysis demonstrates that the proposed SgLV-EKF algorithm represents a powerful and reliable tool to infer MINs and track their dynamics.
推断微生物相互作用网络（MINs）并对其动态进行建模对于理解细菌生态系统的机制以及设计抗生素和/或益生菌疗法至关重要。最近，有人提出了几种使用广义洛特卡 - 沃尔泰拉（gLV）模型来推断MINs的方法。这些模型的主要缺点包括：这些模型仅考虑测量噪声，而未考虑潜在动态中的不确定性。此外，推断MIN的特点是观测数量有限且调节机制存在非线性。因此，需要新的估计技术来应对这些挑战。
这项工作提出了SgLV - EKF：一种随机gLV模型，它采用扩展卡尔曼滤波器（EKF）算法对MIN动态进行建模。具体而言，SgLV - EKF通过在动态模型中添加噪声项来对MIN进行随机建模，以补偿建模不确定性。这种随机建模比传统的gLV模型更现实，传统gLV模型假设MIN动态完全由gLV方程支配。在指定随机模型结构后，我们提出使用EKF来估计MIN。在两个合成数据集和两个真实数据集上的性能方面，将SgLV - EKF与两种基于相似性的算法、一种基于积分的算法家族中的算法以及两种基于回归的算法进行了比较。第一个数据集对测量数据中的随机性进行建模，而第二个数据集则包含潜在动态中的不确定性。真实数据集由最近一项关于抗生素介导的艰难梭菌感染的研究提供。实验结果表明，SgLV - EKF在对测量噪声、建模误差的鲁棒性以及跟踪MIN动态方面优于其他方法。
性能分析表明，所提出的SgLV - EKF算法是推断MINs并跟踪其动态的强大且可靠的工具。
