ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context

Elucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genome-wide "reverse engineering" of such networks have been successful only for lower eukaryotes with simple genomes. Here we...

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Bibliographic Details
Published in	BMC bioinformatics Vol. 7; no. S1; p. S7
Main Authors	Margolin, Adam A, Nemenman, Ilya, Basso, Katia, Wiggins, Chris, Stolovitzky, Gustavo, Favera, Riccardo Dalla, Califano, Andrea
Format	Journal Article
Language	English
Published	England BioMed Central 20.03.2006
Subjects	Algorithms Animals B-Lymphocytes - metabolism Computational Biology - methods Computer Simulation Gene Expression Profiling Gene Expression Regulation Humans Models, Statistical Neural Networks (Computer) Oligonucleotide Array Sequence Analysis Phenotype Proceedings Reproducibility of Results Software Transcription, Genetic
Online Access	Get full text
ISSN	1471-2105 1471-2105
DOI	10.1186/1471-2105-7-S1-S7

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Abstract	Elucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genome-wide "reverse engineering" of such networks have been successful only for lower eukaryotes with simple genomes. Here we present ARACNE, a novel algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by co-expression methods. We prove that ARACNE reconstructs the network exactly (asymptotically) if the effect of loops in the network topology is negligible, and we show that the algorithm works well in practice, even in the presence of numerous loops and complex topologies. We assess ARACNE's ability to reconstruct transcriptional regulatory networks using both a realistic synthetic dataset and a microarray dataset from human B cells. On synthetic datasets ARACNE achieves very low error rates and outperforms established methods, such as Relevance Networks and Bayesian Networks. Application to the deconvolution of genetic networks in human B cells demonstrates ARACNE's ability to infer validated transcriptional targets of the cMYC proto-oncogene. We also study the effects of misestimation of mutual information on network reconstruction, and show that algorithms based on mutual information ranking are more resilient to estimation errors. ARACNE shows promise in identifying direct transcriptional interactions in mammalian cellular networks, a problem that has challenged existing reverse engineering algorithms. This approach should enhance our ability to use microarray data to elucidate functional mechanisms that underlie cellular processes and to identify molecular targets of pharmacological compounds in mammalian cellular networks.
AbstractList	Elucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genome-wide "reverse engineering" of such networks have been successful only for lower eukaryotes with simple genomes. Here we present ARACNE, a novel algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by co-expression methods. We prove that ARACNE reconstructs the network exactly (asymptotically) if the effect of loops in the network topology is negligible, and we show that the algorithm works well in practice, even in the presence of numerous loops and complex topologies. We assess ARACNE's ability to reconstruct transcriptional regulatory networks using both a realistic synthetic dataset and a microarray dataset from human B cells. On synthetic datasets ARACNE achieves very low error rates and outperforms established methods, such as Relevance Networks and Bayesian Networks. Application to the deconvolution of genetic networks in human B cells demonstrates ARACNE's ability to infer validated transcriptional targets of the cMYC proto-oncogene. We also study the effects of misestimation of mutual information on network reconstruction, and show that algorithms based on mutual information ranking are more resilient to estimation errors. ARACNE shows promise in identifying direct transcriptional interactions in mammalian cellular networks, a problem that has challenged existing reverse engineering algorithms. This approach should enhance our ability to use microarray data to elucidate functional mechanisms that underlie cellular processes and to identify molecular targets of pharmacological compounds in mammalian cellular networks. Elucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genome-wide "reverse engineering" of such networks have been successful only for lower eukaryotes with simple genomes. Here we present ARACNE, a novel algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by co-expression methods.BACKGROUNDElucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genome-wide "reverse engineering" of such networks have been successful only for lower eukaryotes with simple genomes. Here we present ARACNE, a novel algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by co-expression methods.We prove that ARACNE reconstructs the network exactly (asymptotically) if the effect of loops in the network topology is negligible, and we show that the algorithm works well in practice, even in the presence of numerous loops and complex topologies. We assess ARACNE's ability to reconstruct transcriptional regulatory networks using both a realistic synthetic dataset and a microarray dataset from human B cells. On synthetic datasets ARACNE achieves very low error rates and outperforms established methods, such as Relevance Networks and Bayesian Networks. Application to the deconvolution of genetic networks in human B cells demonstrates ARACNE's ability to infer validated transcriptional targets of the cMYC proto-oncogene. We also study the effects of misestimation of mutual information on network reconstruction, and show that algorithms based on mutual information ranking are more resilient to estimation errors.RESULTSWe prove that ARACNE reconstructs the network exactly (asymptotically) if the effect of loops in the network topology is negligible, and we show that the algorithm works well in practice, even in the presence of numerous loops and complex topologies. We assess ARACNE's ability to reconstruct transcriptional regulatory networks using both a realistic synthetic dataset and a microarray dataset from human B cells. On synthetic datasets ARACNE achieves very low error rates and outperforms established methods, such as Relevance Networks and Bayesian Networks. Application to the deconvolution of genetic networks in human B cells demonstrates ARACNE's ability to infer validated transcriptional targets of the cMYC proto-oncogene. We also study the effects of misestimation of mutual information on network reconstruction, and show that algorithms based on mutual information ranking are more resilient to estimation errors.ARACNE shows promise in identifying direct transcriptional interactions in mammalian cellular networks, a problem that has challenged existing reverse engineering algorithms. This approach should enhance our ability to use microarray data to elucidate functional mechanisms that underlie cellular processes and to identify molecular targets of pharmacological compounds in mammalian cellular networks.CONCLUSIONARACNE shows promise in identifying direct transcriptional interactions in mammalian cellular networks, a problem that has challenged existing reverse engineering algorithms. This approach should enhance our ability to use microarray data to elucidate functional mechanisms that underlie cellular processes and to identify molecular targets of pharmacological compounds in mammalian cellular networks.
ArticleNumber	S7
Author	Wiggins, Chris Favera, Riccardo Dalla Margolin, Adam A Stolovitzky, Gustavo Nemenman, Ilya Basso, Katia Califano, Andrea
AuthorAffiliation	4 Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY 10032 5 IBM T.J. Watson Research Center, Yorktown Heights, NY 10598 1 Department of Biomedical Informatics, Columbia University, New York, NY 10032 3 Institute for Cancer Genetics, Columbia University, New York, NY 10032 2 Joint Centers for Systems Biology, Columbia University, New York, NY 10032
AuthorAffiliation_xml	– name: 5 IBM T.J. Watson Research Center, Yorktown Heights, NY 10598 – name: 1 Department of Biomedical Informatics, Columbia University, New York, NY 10032 – name: 2 Joint Centers for Systems Biology, Columbia University, New York, NY 10032 – name: 3 Institute for Cancer Genetics, Columbia University, New York, NY 10032 – name: 4 Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY 10032
Author_xml	– sequence: 1 givenname: Adam A surname: Margolin fullname: Margolin, Adam A – sequence: 2 givenname: Ilya surname: Nemenman fullname: Nemenman, Ilya – sequence: 3 givenname: Katia surname: Basso fullname: Basso, Katia – sequence: 4 givenname: Chris surname: Wiggins fullname: Wiggins, Chris – sequence: 5 givenname: Gustavo surname: Stolovitzky fullname: Stolovitzky, Gustavo – sequence: 6 givenname: Riccardo Dalla surname: Favera fullname: Favera, Riccardo Dalla – sequence: 7 givenname: Andrea surname: Califano fullname: Califano, Andrea
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/16723010$$D View this record in MEDLINE/PubMed
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Snippet	Elucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for...
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SubjectTerms	Algorithms Animals B-Lymphocytes - metabolism Computational Biology - methods Computer Simulation Gene Expression Profiling Gene Expression Regulation Humans Models, Statistical Neural Networks (Computer) Oligonucleotide Array Sequence Analysis Phenotype Proceedings Reproducibility of Results Software Transcription, Genetic
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