Article

Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis. Bioinformatics

Monsanto Company, Mail zone CC1A, Chesterfield, MO 63017, USA.
Bioinformatics (Impact Factor: 4.98). 08/2011; 27(19):2686-91. DOI: 10.1093/bioinformatics/btr454
Source: PubMed

ABSTRACT

Reverse engineering gene regulatory networks, especially large size networks from time series gene expression data, remain a challenge to the systems biology community. In this article, a new hybrid algorithm integrating ordinary differential equation models with dynamic Bayesian network analysis, called Differential Equation-based Local Dynamic Bayesian Network (DELDBN), was proposed and implemented for gene regulatory network inference.
The performance of DELDBN was benchmarked with an in vivo dataset from yeast. DELDBN significantly improved the accuracy and sensitivity of network inference compared with other approaches. The local causal discovery algorithm implemented in DELDBN also reduced the complexity of the network inference algorithm and improved its scalability to infer larger networks. We have demonstrated the applicability of the approach to a network containing thousands of genes with a dataset from human HeLa cell time series experiments. The local network around BRCA1 was particularly investigated and validated with independent published studies. BRAC1 network was significantly enriched with the known BRCA1-relevant interactions, indicating that DELDBN can effectively infer large size gene regulatory network from time series data.
The R scripts are provided in File 3 in Supplementary Material.
zheng.li@monsanto.com; jingdong.liu@monsanto.com
Supplementary data are available at Bioinformatics online.

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Available from: Zheng Li, Oct 07, 2014
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    • "At present, numerous classical methods have been developed to unravel the interactions of GRNs, including Boolean network approaches in Shmulevich and Dougherty (2010), Bayesian network inference in Li et al. (2011), partial or conditional correlation analysis in Penfold et al. (2012), differential equation analysis in Karlebach and Shamir (2008), and others. However, while their absolute and comparative performances remain poorly understood, some of results are associated with heavy computational burdens. "
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    ABSTRACT: In this paper, a sparse reconstruction framework is proposed on the basis of steadystate experiment data to identify Gene Regulatory Networks (GRNs) structure. Different from traditional methods, this approach is adopted which is well suitable for a large-scale underdetermined problem in inferring a sparse vector. We investigate how to combine the noisy steady-state experiment data and a sparse reconstruction algorithm to identify causal relationships. Efficiency of this method is tested by an artificial linear network and the DREAM networks. The performance of the suggested approach is compared with two state-of-the-art algorithms, the widely adopted total least-squares (TLS) method and those available results on the DREAM project website. Actual results show that with a lower computational cost, the proposed method can significantly enhance estimation accuracy
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    • "For the machine learning-based approaches, the network is inferred through measuring the dependences or causalities between transcriptional factors (TFs) and target genes (Ku¨ffner et al., 2012). Popular methods in this category include partial correlation coefficient (De la Fuente et al., 2004, Saito et al., 2011), Bayesian network analysis (Li et al., 2011; Yeung et al., 2011), mutual information (MI) (Basso et al., 2005; Belcastro et al., 2011; Faith et al., 2007; Margolin et al., 2006b; Modi et al., 2011) and conditional mutual information (CMI) (Sumazin et al., 2011; Zhang et al., 2012). As one of the most popular methods, MI has been widely used to construct GRNs because it provides a natural generalization of correlation owing to its capability of characterizing non-linear dependency (Brunel et al., 2010). "

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    • "These methods are deterministic rather than statistical, although ODE methods can be combined with statistical methods. DBN on local networks within a larger ODE model inference method have been used, for example [8]. "
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    ABSTRACT: Genome-wide time-series data provide a rich set of information for discovering gene regulatory relationships.As genome-wide data for mammalian systems are being generated, it is critical to developnetwork inference methods that can handle tens of thousands of genes efficiently, provide a systematicframework for the integration of multiple data sources, yield robust, accurate and compact gene-togenerelationships. We developed and applied ScanBMA, a Bayesian inference method that incorporates external informationto improve the accuracy of the inferred network. In particular, we developed a new strategy toefficiently search the model space, applied data transformations to reduce the effect of spurious relationships,and adopted the g-prior to guide the search for candidate regulators. Our method is highlycomputationally efficient, thus addressing the scalability issue with network inference. The method isimplemented as the ScanBMA function in the networkBMA Bioconductor software package. We compared ScanBMA to other popular methods using time series yeast data as well as time-seriessimulated data from the DREAM competition. We found that ScanBMA produced more compact networkswith a greater proportion of true positives than the competing methods. Specifically, ScanBMAgenerally produced more favorable areas under the Receiver-Operating Characteristic and Precision-Recall curves than other regression-based methods and mutual-information based methods. In addition,ScanBMA is competitive with other network inference methods in terms of running time.
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