ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. Background: 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. Results: 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. Conclusion: 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.

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  1. Tyler Grimes, Somnath Datta: SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data (2021) not zbMATH
  2. Wang, Y. X. Rachel; Li, Lexin; Li, Jingyi Jessica; Huang, Haiyan: Network modeling in biology: statistical methods for gene and brain networks (2021)
  3. Ajmal, Hamda B.; Madden, Michael G.: Inferring dynamic gene regulatory networks with low-order conditional independencies -- an evaluation of the method (2020)
  4. Pensar, Johan; Xu, Yingying; Puranen, Santeri; Pesonen, Maiju; Kabashima, Yoshiyuki; Corander, Jukka: High-dimensional structure learning of binary pairwise Markov networks: a comparative numerical study (2020)
  5. Pio, Gianvito; Ceci, Michelangelo; Prisciandaro, Francesca; Malerba, Donato: Exploiting causality in gene network reconstruction based on graph embedding (2020)
  6. Wu, Yichong; Li, Tiejun; Liu, Xiaoping; Chen, Luonan: Differential network inference via the fused D-trace loss with cross variables (2020)
  7. Durón, Christina; Pan, Yuan; Gutmann, David H.; Hardin, Johanna; Radunskaya, Ami: Variability of betweenness centrality and its effect on identifying essential genes (2019)
  8. Karra, Kiran; Mili, Lamine: Copula index for detecting dependence and monotonicity between stochastic signals (2019)
  9. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  10. Shi, Jifan; Zhao, Juan; Li, Tiejun; Chen, Luonan: Detecting direct associations in a network by information theoretic approaches (2019)
  11. Shi, Rundong; Hu, Gang; Wang, Shihong: Reconstructing nonlinear networks subject to fast-varying noises by using linearization with expanded variables (2019)
  12. Young, William Chad; Yeung, Ka Yee; Raftery, Adrian E.: Identifying dynamical time series model parameters from equilibrium samples, with application to gene regulatory networks (2019)
  13. Champion, Magali; Picheny, Victor; Vignes, Matthieu: Inferring large graphs using (\ell_1)-penalized likelihood (2018)
  14. Romano, Simone; Vinh, Nguyen Xuan; Verspoor, Karin; Bailey, James: The randomized information coefficient: assessing dependencies in noisy data (2018)
  15. Fan, Yue; Wang, Xiao; Peng, Qinke: Inference of gene regulatory networks using Bayesian nonparametric regression and topology information (2017)
  16. Keith, Jonathan M. (ed.): Bioinformatics. Volume II: structure, function, and applications (2017)
  17. Possieri, Corrado; Teel, Andrew R.: Asymptotic stability in probability for stochastic Boolean networks (2017)
  18. Weishaupt, Holger; Johansson, Patrik; Engström, Christopher; Nelander, Sven; Silvestrov, Sergei; Swartling, Fredrik J.: Loss of conservation of graph centralities in reverse-engineered transcriptional regulatory networks (2017)
  19. Reddy, Gautam; Celani, Antonio; Vergassola, Massimo: Infomax strategies for an optimal balance between exploration and exploitation (2016)
  20. Weishaupt, Holger; Johansson, Patrik; Engström, Christopher; Nelander, Sven; Silvestrov, Sergei; Swartling, Fredrik J.: Graph centrality based prediction of cancer genes (2016)

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