A computational framework for gene regulatory network inference that combines multiple methods and datasets

Gupta, R.; Stincone, Anna; Antczak, Philipp; Durant, Sarah M.; Bicknell, Roy; Bikfalvi, Andréas; Falciani, Francesco

doi:10.1186/1752-0509-5-52

Cited by 29 publications

(24 citation statements)

References 39 publications

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“…Previous integrative methods for transcriptional network inference were mostly centered on expression-based approaches, either integrating different types of expression data (Greenfield et al 2010;Gupta et al 2011) or using an additional data source as a fixed prior for expression-based inference (Bernard and Hartemink 2005). Supervised inference methods were used to integrate known interactions from curated databases as training data, but only used expression data as an input feature to predict interactions (Qian et al 2003;Seok et al 2010).…”

Section: Comparison With Other Integrative Approachesmentioning

confidence: 99%

Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

Marbach¹,

Roy²,

Ay³

et al. 2012

Genome Res.

111

131

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Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein–protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level.

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Section: Comparison With Other Integrative Approachesmentioning

confidence: 99%

Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

Marbach¹,

Roy²,

Ay³

et al. 2012

Genome Res.

111

131

View full text Add to dashboard Cite

show abstract

“…In the work of Ma and Zeng [50], the metabolic networks of 80 sequenced organisms are reconstructed in silico from genome data and a bioreaction database. A recent work presents a method that integrates multiple inference methods and experiments using multiobjective optimization [51]. This method was applied for modeling E. coli acid stress and in-vivo tumour development.…”

Section: Statistical Models and Toolsmentioning

confidence: 99%

Modeling Metabolic Networks for Mammalian Cell Systems: General Considerations, Modeling Strategies, and Available Tools

Gerdtzen

2011

Genomics and Systems Biology of Mammalian Cell Culture

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Over the past decades, the availability of large amounts of information regarding cellular processes and reaction rates, along with increasing knowledge about the complex mechanisms involved in these processes, has changed the way we approach the understanding of cellular processes. We can no longer rely only on our intuition for interpreting experimental data and evaluating new hypotheses, as the information to analyze is becoming increasingly complex. The paradigm for the analysis of cellular systems has shifted from a focus on individual processes to comprehensive global mathematical descriptions that consider the interactions of metabolic, genomic, and signaling networks. Analysis and simulations are used to test our knowledge by refuting or validating new hypotheses regarding a complex system, which can result in predictive capabilities that lead to better experimental design. Different types of models can be used for this purpose, depending on the type and amount of information available for the specific system. Stoichiometric models are based on the metabolic structure of the system and allow explorations of steady state distributions in the network. Detailed kinetic models provide a description of the dynamics of the system, they involve a large number of reactions with varied kinetic characteristics and require a large number of parameters. Models based on statistical information provide a description of the system without information regarding structure and interactions of the networks involved. The development of detailed models for mammalian cell metabolism has only recently started to grow more strongly, due to the intrinsic complexities of mammalian systems, and the limited availability of experimental information and adequate modeling tools. In this work we review the strategies, tools, current advances, and recent models of mammalian cells, focusing mainly on metabolism, but discussing the methodology applied to other types of networks as well.

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“…Gupta et al [18] used multi-objective optimization to integrate different methods for reverse-engineering. To illustrate this, they used a combination of linear ODE and correlation-based methods, using data from time-course and gene inactivation (knock-out) experiments.…”

Section: Reverse Engineering and Data Combinationmentioning

confidence: 99%

Reverse engineering of gene regulation models from multi-condition experiments

Kennedy

Mizeranschi

Thompson

et al. 2013

2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

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Reverse-engineering of quantitative, dynamic generegulatory network (GRN) models from time-series gene expression data is becoming important as such data are increasingly generated for research and other purposes. A key problem in the reverse-engineering process is the underdetermined nature of these data. Because of this, the reverseengineered GRN models often lack robustness and perform poorly when used to simulate system responses to new conditions. In this study, we present a novel method capable of inferring robust GRN models from multi-condition GRN experiments. This study uses two important computational intelligence methods: artificial neural networks and particle swarm optimization.

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A computational framework for gene regulatory network inference that combines multiple methods and datasets

Cited by 29 publications

References 39 publications

Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

Modeling Metabolic Networks for Mammalian Cell Systems: General Considerations, Modeling Strategies, and Available Tools

Reverse engineering of gene regulation models from multi-condition experiments

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