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Instead, heuristic machine learning algorithms have been proposed to infer the structure and parameters of a system of equations able to recapitulate a given gene expression pattern. However, these algorithms are computationally intensive as they need to simulate millions of candidate models, which limits their applicability and requires high computational resources. Graphics processing unit (GPU) computing is an affordable alternative for accelerating large-scale scientific computation, yet no method is currently available to exploit GPU technology for the reverse engineering of mechanistic GRNs from spatial phenotypes. Here we present an efficient methodology to parallelize evolutionary algorithms using GPU computing for the inference of mechanistic GRNs that can develop a given gene expression pattern in a multicellular tissue area or cell culture. The proposed approach is based on multi-CPU threads running the lightweight crossover, mutation and selection operators and launching GPU kernels asynchronously. Kernels can run in parallel in a single or multiple GPUs and each kernel simulates and scores the error of a model using the thread parallelism of the GPU. We tested this methodology for the inference of spatiotemporal mechanistic gene regulatory networks (GRNs)\u2014including topology and parameters\u2014that can develop a given 2D gene expression pattern. The results show a 700-fold speedup with respect to a single CPU implementation. This approach can streamline the extraction of knowledge from biological and medical datasets and accelerate the automatic design of GRNs for synthetic biology applications.<\/jats:p>","DOI":"10.1093\/bib\/bbab104","type":"journal-article","created":{"date-parts":[[2021,3,11]],"date-time":"2021-03-11T12:11:02Z","timestamp":1615464662000},"source":"Crossref","is-referenced-by-count":15,"title":["Inference of dynamic spatial GRN models with multi-GPU evolutionary computation"],"prefix":"10.1093","volume":"22","author":[{"given":"Reza","family":"Mousavi","sequence":"first","affiliation":[{"name":"Department of Biological Sciences at the University of Maryland, Baltimore, MD 21250, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sri Harsha","family":"Konuru","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences at the University of Maryland, Baltimore, MD 21250, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Lobo","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences at the University of Maryland, Baltimore, MD 21250, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,4,8]]},"reference":[{"key":"2021090814535249800_ref1","doi-asserted-by":"crossref","first-page":"41339","DOI":"10.1038\/srep41339","article-title":"Discovering novel phenotypes with automatically inferred dynamic models: a partial melanocyte conversion in Xenopus","volume":"7","author":"Lobo","year":"2017","journal-title":"Sci Rep"},{"key":"2021090814535249800_ref2","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.gde.2020.05.039","article-title":"Engineering and modeling of multicellular morphologies and patterns","volume":"63","author":"Kim","year":"2020","journal-title":"Curr Opin Genet Dev"},{"key":"2021090814535249800_ref3","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.artmed.2018.10.006","article-title":"Computational methods for gene regulatory networks reconstruction and analysis: a review","volume":"95","author":"Delgado","year":"2019","journal-title":"Artif Intell Med"},{"key":"2021090814535249800_ref4","doi-asserted-by":"crossref","first-page":"e12776","DOI":"10.1371\/journal.pone.0012776","article-title":"Inferring regulatory networks from expression data using tree-based methods","volume":"5","author":"Huynh-Thu","year":"2010","journal-title":"PLoS One"},{"key":"2021090814535249800_ref5","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1038\/nmeth.4463","article-title":"SCENIC: single-cell regulatory network inference and clustering","volume":"14","author":"Aibar","year":"2017","journal-title":"Nat Methods"},{"key":"2021090814535249800_ref6","doi-asserted-by":"crossref","first-page":"1614","DOI":"10.1093\/bioinformatics\/btu863","article-title":"Combining tree-based and dynamical systems for the inference of gene regulatory networks","volume":"31","author":"Huynh-Thu","year":"2015","journal-title":"Bioinformatics"},{"key":"2021090814535249800_ref7","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1093\/bioinformatics\/btz529","article-title":"Inference of differential gene regulatory networks based on gene expression and genetic perturbation data","volume":"36","author":"Zhou","year":"2020","journal-title":"Bioinformatics"},{"key":"2021090814535249800_ref8","doi-asserted-by":"crossref","first-page":"S19","DOI":"10.1186\/1471-2105-7-S4-S19","article-title":"Clustering of gene expression data: performance and similarity analysis","volume":"7","author":"Yin","year":"2006","journal-title":"BMC Bioinformatics"},{"key":"2021090814535249800_ref9","doi-asserted-by":"crossref","first-page":"e8","DOI":"10.1371\/journal.pbio.0050008","article-title":"Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles","volume":"5","author":"Faith","year":"2007","journal-title":"PLoS Biol"},{"issue":"Suppl 1","key":"2021090814535249800_ref10","doi-asserted-by":"crossref","first-page":"S7","DOI":"10.1186\/1471-2105-7-S1-S7","article-title":"ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context","volume":"7","author":"Margolin","year":"2006","journal-title":"BMC Bioinformatics"},{"key":"2021090814535249800_ref11","doi-asserted-by":"crossref","first-page":"i197","DOI":"10.1093\/bioinformatics\/btv268","article-title":"Integrative random forest for gene regulatory network inference","volume":"31","author":"Petralia","year":"2015","journal-title":"Bioinformatics"},{"key":"2021090814535249800_ref12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2007\/79879","article-title":"Information-theoretic inference of large transcriptional regulatory networks. 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