{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T17:07:23Z","timestamp":1787504843837,"version":"build-2736575974"},"reference-count":41,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T00:00:00Z","timestamp":1689638400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NIH","award":["R01CA255432"],"award-info":[{"award-number":["R01CA255432"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>\n                    As a powerful but computationally intensive method, hybrid computational models study the dynamics of multicellular systems by evolving discrete cells in reacting and diffusing extracellular microenvironments. As the scale and complexity of studied biological systems continuously increase, the exploding computational cost starts to limit large-scale cell-based simulations. To facilitate the large-scale hybrid computational simulation and make it feasible on easily accessible computational devices, we develop Gell (GPU Cell), a fast and memory-efficient open-source GPU-based hybrid computational modeling platform for large-scale system modeling. We fully parallelize the simulations on GPU for high computational efficiency and propose a novel voxel sorting method to further accelerate the modeling of massive cell-cell mechanical interaction with negligible additional memory footprint. As a result, Gell efficiently handles simulations involving tens of millions of cells on a personal computer. We compare the performance of Gell with a state-of-the-art paralleled CPU-based simulator on a hanging droplet spheroid growth task and further demonstrate Gell with a ductal carcinoma\n                    <jats:italic>in situ<\/jats:italic>\n                    (DCIS) simulation. Gell affords ~150X acceleration over the paralleled CPU method with one-tenth of the memory requirement.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0288721","type":"journal-article","created":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T13:25:01Z","timestamp":1689686701000},"page":"e0288721","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":11,"title":["Gell: A GPU-powered 3D hybrid simulator for large-scale multicellular system"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3172-2730","authenticated-orcid":true,"given":"Jiayi","family":"Du","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihua","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6696-5409","authenticated-orcid":true,"given":"Ke","family":"Sheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2023,7,18]]},"reference":[{"issue":"1","key":"pone.0288721.ref001","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/0951-7715\/23\/1\/R01","article-title":"Nonlinear modelling of cancer: bridging the gap between cells and tumours","volume":"23","author":"JS Lowengrub","year":"2009","journal-title":"Nonlinearity"},{"issue":"3","key":"pone.0288721.ref002","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1200\/CCI.18.00069","article-title":"A Review of Cell-Based Computational Modeling in Cancer Biology","author":"J Metzcar","year":"2019","journal-title":"JCO Clin Cancer Inform"},{"issue":"138","key":"pone.0288721.ref003","doi-asserted-by":"crossref","first-page":"20170703","DOI":"10.1098\/rsif.2017.0703","article-title":"Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues.","volume":"15","author":"A Karolak","year":"2018","journal-title":"J R Soc Interface."},{"issue":"10","key":"pone.0288721.ref004","article-title":"Simulating Properties of In Vitro Epithelial Cell Morphogenesis","volume":"2","author":"MR Grant","year":"2006","journal-title":"PLoS Comput Biol [Internet]."},{"issue":"4","key":"pone.0288721.ref005","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1039\/c0ib00092b","article-title":"Phenotypic transition maps of 3D breast acini obtained by imaging-guided agent-based modeling.","volume":"3","author":"J Tang","year":"2011","journal-title":"Integr Biol."},{"issue":"5","key":"pone.0288721.ref006","article-title":"Front Instabilities and Invasiveness of Simulated 3D Avascular Tumors.","volume":"5","author":"NJ Poplawski","year":"2010","journal-title":"PLoS ONE [Internet]."},{"issue":"4","key":"pone.0288721.ref007","article-title":"Multiscale Modelling of Vascular Tumour Growth in 3D: The Roles of Domain Size and Boundary Conditions.","volume":"6","author":"H Perfahl","year":"2011","journal-title":"PLoS ONE [Internet]."},{"key":"pone.0288721.ref008","article-title":"The Role of Tumor Tissue Architecture in Treatment Penetration and Efficacy: An Integrative Study.","volume":"3","author":"KA Rejniak","year":"2013","journal-title":"Front Oncol [Internet]."},{"issue":"1","key":"pone.0288721.ref009","article-title":"Spatial Metrics of Tumour Vascular Organisation Predict Radiation Efficacy in a Computational Model.","volume":"12","author":"JG Scott","year":"2016","journal-title":"PLoS Comput Biol [Internet]."},{"issue":"1","key":"pone.0288721.ref010","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/s10710-006-9021-7","article-title":"Cellular Automaton Modeling of Biological Pattern Formation: Characterization, Applications, and Analysis Authors: Andreas Deutsch and Sabine Dormann, Birkh\u00e4user, 2005, XXVI, 334 p., 131 illus., Hardcover. 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