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Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge
Florian Geier, Jens Timmer, Christian Fleck
BMC Systems Biology , 2007, DOI: 10.1186/1752-0509-1-11
Abstract: We identify linear Gaussian dynamic Bayesian networks and variable selection based on F-statistics as suitable methods for the reconstruction of gene-regulatory networks from time series data. Commonly used discrete dynamic Bayesian networks perform inferior and this result can be attributed to the inevitable information loss by discretization of expression data. It is shown that short time series generated under transcription factor knock-out are optimal experiments in order to reveal the structure of gene regulatory networks. Relative to the level of observational noise, we give estimates for the required amount of gene expression data in order to accurately reconstruct gene-regulatory networks. The benefit of using of prior knowledge within a Bayesian learning framework is found to be limited to conditions of small gene expression data size. Unobserved processes, like protein-protein interactions, induce dependencies between gene expression levels similar to direct transcriptional regulation. We show that these dependencies cannot be distinguished from transcription factor mediated gene regulation on the basis of gene expression data alone.Currently available data size and data quality make the reconstruction of gene networks from gene expression data a challenge. In this study, we identify an optimal type of experiment, requirements on the gene expression data quality and size as well as appropriate reconstruction methods in order to reverse engineer gene regulatory networks from time series data.The temporal and spatial coordination of gene expression patterns is the result of a complex integration of regulatory signals at the promotor of target genes [1,2]. In the last years numerous methods have been developed and applied to reconstruct the structure and dynamic rules of gene-regulatory networks from different high-throughput data sources, mainly microarray based gene expression analysis, promotor sequence information, chromatin immunoprecipitation (ChIP) and
A Bayesian Framework That Integrates Heterogeneous Data for Inferring Gene Regulatory Networks  [PDF]
Tapesh Santra
Frontiers in Bioengineering and Biotechnology , 2014, DOI: 10.3389/fbioe.2014.00013
Abstract: Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein–protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances.
Bayesian Inference of Genetic Regulatory Networks from Time Series Microarray Data Using Dynamic Bayesian Networks  [cached]
Yufei Huang,Jianyin Wang,Jianqiu Zhang,Maribel Sanchez
Journal of Multimedia , 2007, DOI: 10.4304/jmm.2.3.46-56
Abstract: Reverse engineering of genetic regulatory networks from time series microarray data are investigated. We propose a dynamic Bayesian networks (DBNs) modeling and a full Bayesian learning scheme. The proposed DBN directly models the continuous expression levels and also is associated with parameters that indicate the degree as well as the type of regulations. To learn the network from data, we proposed a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm. The RJMCMC algorithm can provide not only more accurate inference results than the deterministic alternative algorithms but also an estimate of the a posteriori probabilities (APPs) of the network topology. The estimated APPs provide useful information on the confidence of the inferred results and can also be used for efficient Bayesian data integration. The proposed approach is tested on yeast cell cycle microarray data and the results are compared with the KEGG pathway map.
Uncovering Gene Regulatory Networks from Time-Series Microarray Data with Variational Bayesian Structural Expectation Maximization  [cached]
Luna Isabel,Huang Yufei,Yin Yufang,Padillo Diego
EURASIP Journal on Bioinformatics and Systems Biology , 2007,
Abstract: We investigate in this paper reverse engineering of gene regulatory networks from time-series microarray data. We apply dynamic Bayesian networks (DBNs) for modeling cell cycle regulations. In developing a network inference algorithm, we focus on soft solutions that can provide a posteriori probability (APP) of network topology. In particular, we propose a variational Bayesian structural expectation maximization algorithm that can learn the posterior distribution of the network model parameters and topology jointly. We also show how the obtained APPs of the network topology can be used in a Bayesian data integration strategy to integrate two different microarray data sets. The proposed VBSEM algorithm has been tested on yeast cell cycle data sets. To evaluate the confidence of the inferred networks, we apply a moving block bootstrap method. The inferred network is validated by comparing it to the KEGG pathway map.
Using Consensus Bayesian Network to Model the Reactive Oxygen Species Regulatory Pathway  [PDF]
Liangdong Hu, Limin Wang
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0056832
Abstract: Bayesian network is one of the most successful graph models for representing the reactive oxygen species regulatory pathway. With the increasing number of microarray measurements, it is possible to construct the Bayesian network from microarray data directly. Although large numbers of Bayesian network learning algorithms have been developed, when applying them to learn Bayesian networks from microarray data, the accuracies are low due to that the databases they used to learn Bayesian networks contain too few microarray data. In this paper, we propose a consensus Bayesian network which is constructed by combining Bayesian networks from relevant literatures and Bayesian networks learned from microarray data. It would have a higher accuracy than the Bayesian networks learned from one database. In the experiment, we validated the Bayesian network combination algorithm on several classic machine learning databases and used the consensus Bayesian network to model the 's ROS pathway.
Effects of Time Point Measurement on the Reconstruction of Gene Regulatory Networks  [PDF]
Wenying Yan,Huangqiong Zhu,Yang Yang,Jiajia Chen,Yuanyuan Zhang,Bairong Shen
Molecules , 2010, DOI: 10.3390/molecules15085354
Abstract: With the availability of high-throughput gene expression data in the post-genomic era, reconstruction of gene regulatory networks has become a hot topic. Regulatory networks have been intensively studied over the last decade and many software tools are currently available. However, the impact of time point selection on network reconstruction is often underestimated. In this paper we apply the Dynamic Bayesian network (DBN) to construct the Arabidopsis gene regulatory networks by analyzing the time-series gene microarray data. In order to evaluate the impact of time point measurement on network reconstruction, we deleted time points one by one to yield 11 distinct groups of incomplete time series. Then the gene regulatory networks constructed based on complete and incomplete data series are compared in terms of statistics at different levels. Two time points are found to play a significant role in the Arabidopsis gene regulatory networks. Pathway analysis of significant nodes revealed three key regulatory genes. In addition, important regulations between genes, which were insensitive to the time point measurement, were also identified.
Integrating external biological knowledge in the construction of regulatory networks from time-series expression data
Kenneth Lo, Adrian E Raftery, Kenneth M Dombek, Jun Zhu, Eric E Schadt, Roger E Bumgarner, Ka Yeung
BMC Systems Biology , 2012, DOI: 10.1186/1752-0509-6-101
Abstract: We formulate network construction as a series of variable selection problems and use linear regression to model the data. Our method summarizes additional data sources with an informative prior probability distribution over candidate regression models. We extend the Bayesian model averaging (BMA) variable selection method to select regulators in the regression framework. We summarize the external biological knowledge by an informative prior probability distribution over the candidate regression models.We demonstrate our method on simulated data and a set of time-series microarray experiments measuring the effect of a drug perturbation on gene expression levels, and show that it outperforms leading regression-based methods in the literature.With recent advances in high-throughput biological data collection, reverse engineering of regulatory networks from large-scale genomics data has become a problem of broad interest to biologists. The construction of regulatory networks is essential for defining the interactions between genes and gene products, and predictive models may be used to develop novel therapies [1,2]. Both microarrays and more recently next generation sequencing provide the ability to quantify the expression levels of all genes in a given genome. Often, in such experiments, gene expression is measured in response to drug treatment, environmental perturbations, or gene knockouts, either at steady state or over a series of time points. This type of data captures information about the effect of one gene’s expression level on the expression level of another gene. Hence, such data can, in principle, be reverse engineered to provide a regulatory network that models these effects.A regulatory network can be represented as a directed graph, in which each node represents a gene (in our case an mRNA level) and each directed edge (r→g) represents the relationship between regulator r and gene g. We aim to infer the directed edges that describe the relationships among
Overview of methods of reverse engineering of gene regulatory networks: Boolean and Bayesian networks  [PDF]
Frolova A. O.
Biopolymers and Cell , 2012,
Abstract: Reverse engineering of gene regulatory networks is an intensively studied topic in Systems Biology as it reconstructs regulatory interactions between all genes in the genome in the most complete form. The extreme computational complexity of this problem and lack of thorough reviews on reconstruction methods of gene regulatory network is a significant obstacle to further development of this area. In this article the two most common methods for modeling gene regulatory networks are surveyed: Boolean and Bayesian networks. The mathematical description of each method is given, as well as several algorithmic approaches to modeling gene networks using these methods; the complexity of algorithms and the problems that arise during its implementation are also noted.
Using mechanistic Bayesian networks to identify downstream targets of the Sonic Hedgehog pathway
Abhik Shah, Toyoaki Tenzen, Andrew P McMahon, Peter J Woolf
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-433
Abstract: We introduce a new general-purpose analytic method called Mechanistic Bayesian Networks (MBNs) that allows for the integration of gene expression data and known constraints within a signal or regulatory pathway to predict new downstream pathway targets. The MBN framework is implemented in an open-source Bayesian network learning package, the Python Environment for Bayesian Learning (PEBL). We demonstrate how MBNs can be used by modeling the early steps of the sonic hedgehog pathway using gene expression data from different developmental stages and genetic backgrounds in mouse. Using the MBN approach we are able to automatically identify many of the known downstream targets of the hedgehog pathway such as Gas1 and Gli1, along with a short list of likely targets such as Mig12.The MBN approach shown here can easily be extended to other pathways and data types to yield a more mechanistic framework for learning genetic regulatory models.A general problem in systems biology is the integration of observational experimental data such as gene expression, with known pathways topologies. Ideally these two data sources should be complementary, however in practice there are few methods to systematically integrate these two kinds of information. In this paper, we introduce a method called Mechanistic Bayesian Networks (MBN) in an attempt to use knowledge about the topology of a pathway with gene expression data related to the same pathway. As a sample case we used the topology of the Sonic Hedgehog (Shh) signaling pathway as a model pathway along with a targeted gene expression dataset. Using these data we used the MBN approach to identify regulatory targets of the Shh pathway.The Shh pathway plays a central role in organismal development and the progression of some cancers [1]. Because of its central role, the Shh pathway is well studied providing us with an ideal test case to validate our MBN approach. The details of Shh are reviewed in detail elsewhere [2-4], but here we will
Quantitative utilization of prior biological knowledge in the Bayesian network modeling of gene expression data
Shouguo Gao, Xujing Wang
BMC Bioinformatics , 2011, DOI: 10.1186/1471-2105-12-359
Abstract: We introduce a new method to incorporate the quantitative information from multiple sources of prior knowledge. It first uses the Na?ve Bayesian classifier to assess the likelihood of functional linkage between gene pairs based on prior knowledge. In this study we included cocitation in PubMed and schematic similarity in Gene Ontology annotation. A candidate network edge reservoir is then created in which the copy number of each edge is proportional to the estimated likelihood of linkage between the two corresponding genes. In network simulation the Markov Chain Monte Carlo sampling algorithm is adopted, and samples from this reservoir at each iteration to generate new candidate networks. We evaluated the new algorithm using both simulated and real gene expression data including that from a yeast cell cycle and a mouse pancreas development/growth study. Incorporating prior knowledge led to a ~2 fold increase in the number of known transcription regulations recovered, without significant change in false positive rate. In contrast, without the prior knowledge BN modeling is not always better than a random selection, demonstrating the necessity in network modeling to supplement the gene expression data with additional information.our new development provides a statistical means to utilize the quantitative information in prior biological knowledge in the BN modeling of gene expression data, which significantly improves the performance.Reverse engineering of genetic networks will greatly facilitate the dissection of cellular functions at the molecular level [1-3]. The time course gene expression study offers an ideal data source for transcription regulatory network modeling. However, in a typical microarray experiment usually up to tens of thousands of genes are measured in only several dozens or less samples, data from such experiments alone is significantly underpowered, leading to high rate of false positive predictions [4]. Network reconstruction from microarray data
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