{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T17:53:50Z","timestamp":1781718830194,"version":"3.54.5"},"reference-count":23,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:00:00Z","timestamp":1781654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"\n Background\/introduction<\/jats:title>\n Monitoring the population dynamics of industrial methanotrophic bacterial consortia is critical for optimization of single-cell protein (SCP) production from natural gas. Traditional manual microscopy is labor-intensive, subjective, and limited in throughput. AI-based computer vision offers a promising alternative for automated, quantitative analysis of cell morphotypes in mixed cultures.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n A convolutional neural network (YOLO11x-seg) with a P2 activation layer for small-object detection and Focaler-MPDIoU loss function was trained on 250 phase-contrast micrographs of an industrial methanotrophic consortium based on\n Methylococcus capsulatus<\/jats:italic>\n KN2, cultivated continuously at dilution rates of 0.15\u20130.25 h\n \u20131<\/jats:sup>\n . The dataset comprised nine cellular morphotypes across 50,410 annotated objects (training: 200 images; validation: 50 images), with synthetic data augmentation applied to reduce class imbalance in the tetracocci class. The trained model was then applied to a pure\n M. capsulatus<\/jats:italic>\n KN2 culture during substrate-unlimited batch growth (\u03bc\n max<\/jats:sub>\n = 0.223 h\n \u20131<\/jats:sup>\n , t\n d<\/jats:sub>\n = 3.11 h) across three biological replicates and four time points (3, 5, 7, 9 h), analyzing 277 micrographs and 15,124 objects.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n On the validation set, the model achieved mAP@0.5:0.95 = 0.52, with class-weighted Precision = 0.87, Recall = 0.85, and F1 = 0.82. Per-morphotype F1 scores were:\n monococci<\/jats:italic>\n 0.75,\n diplococci<\/jats:italic>\n 0.89,\n tetracocci<\/jats:italic>\n 0.65. In the industrial consortium, producer cells (\n M. capsulatus<\/jats:italic>\n KN2) constituted 88.5% of the population (\n monococci<\/jats:italic>\n 33.7%,\n diplococci<\/jats:italic>\n 61.9%,\n tetracocci<\/jats:italic>\n 4.4%), while satellite bacteria comprised 11.5%. During batch cultivation of the pure producer strain, the\n diplococci<\/jats:italic>\n fraction increased from 61.0% to 68.6% over 7 h, negatively correlating with a decline in\n monococci<\/jats:italic>\n from 35.4% to 28.8% (Pearson\n r<\/jats:italic>\n = \u20130.996,\n p<\/jats:italic>\n = 0.004).\n Tetracocci<\/jats:italic>\n showed no statistically significant correlation with either morphotype and are considered a stochastic subpopulation of\n diplococci<\/jats:italic>\n . Cell cycle analysis revealed elongation of the M-phase from 71.6% to 78.4% of t\n d<\/jats:sub>\n .\n Monococci<\/jats:italic>\n cell radius, intracellular volume, and periplasmic surface area all increased over 9 h, while the surface-to-volume ratio declined.\n <\/jats:p>\n <\/jats:sec>\n \n Discussion\/conclusion<\/jats:title>\n \n The observed M-phase elongation is consistent with incipient substrate limitation (CH\n 4<\/jats:sub>\n or O\n 2<\/jats:sub>\n ) in gas-tight batch flasks, detectable through morphotype ratio shifts before standard process parameters register any change. The approach enables a proof-of-concenpt for real-time culture quality monitoring, early prediction of growth limitations, and optimization of SCP production in industrial bioreactors.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fmicb.2026.1831666","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T17:10:22Z","timestamp":1781716222000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Population dynamics analysis of an industrial methanotrophic consortium based on Methylococcus capsulatus KN2 using AI technologies"],"prefix":"10.3389","volume":"17","author":[{"given":"Maksim V.","family":"Zakhartsev","sequence":"first","affiliation":[{"name":"C1BioEngineering LLC","place":["Moscow, Russia"]},{"name":"Research Center of Biotechnology RAS","place":["Moscow, Russia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dmitriy A.","family":"Pavlov","sequence":"additional","affiliation":[{"name":"Research Center of Biotechnology RAS","place":["Moscow, Russia"]},{"name":"Kuban State Agrarian University","place":["Krasnodar, Russia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Igor Y.","family":"Oshkin","sequence":"additional","affiliation":[{"name":"Research Center of Biotechnology RAS","place":["Moscow, Russia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victoria A.","family":"Saltykova","sequence":"additional","affiliation":[{"name":"Research Center of Biotechnology RAS","place":["Moscow, Russia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Azret A.","family":"Kochkarov","sequence":"additional","affiliation":[{"name":"Research Center of Biotechnology RAS","place":["Moscow, Russia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"5520","DOI":"10.1099\/ijsem.0.004442","article-title":"Methylococcus geothermalis sp. nov., a methanotroph isolated from a geothermal field in the republic of Korea.","volume":"70","author":"Awala","year":"2020","journal-title":"Int. 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