{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:51:47Z","timestamp":1762509107696,"version":"3.41.2"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"4","funder":[{"DOI":"10.13039\/501100003693","name":"Korea Institute of Science and Technology","doi-asserted-by":"publisher","award":["2E32981"],"award-info":[{"award-number":["2E32981"]}],"id":[{"id":"10.13039\/501100003693","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2022M3H4A1A03076638"],"award-info":[{"award-number":["2022M3H4A1A03076638"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Many organisms\u2019 survival and behavior hinge on their responses to environmental signals. While research on bacteria-directed therapeutic agents has increased, systematic exploration of real-time modulation of bacterial motility remains limited. Current studies often focus on permanent motility changes through genetic alterations, restricting the ability to modulate bacterial motility dynamically on a large scale. To address this gap, we propose a novel real-time control framework for systematically modulating bacterial motility dynamics.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We introduce MotGen, a deep learning approach leveraging Generative Adversarial Networks to analyze swimming performance statistics of motile bacteria based on live cell imaging data. By tracking objects and optimizing cell trajectory mapping under environmentally altered conditions, we trained MotGen on a comprehensive statistical dataset derived from real image data. Our experimental results demonstrate MotGen\u2019s ability to capture motility dynamics from real bacterial populations with low mean absolute error in both simulated and real datasets. MotGen allows us to approach optimal swimming conditions for desired motility statistics in real-time. MotGen\u2019s potential extends to practical biomedical applications, including immune response prediction, by providing imputation of bacterial motility patterns based on external environmental conditions. Our short-term, in-situ interventions for controlling motility behavior offer a promising foundation for the development of bacteria-based biomedical applications.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>MotGen is presented as a combination of Matlab image analysis code and a machine learning workflow in Python. Codes are available at https:\/\/github.com\/bgmseo\/MotGen, for cell tracking and implementation of trained models to generate bacterial motility statistics.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae170","type":"journal-article","created":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T22:54:13Z","timestamp":1711752853000},"source":"Crossref","is-referenced-by-count":1,"title":["MotGen: a closed-loop bacterial motility control framework using generative adversarial networks"],"prefix":"10.1093","volume":"40","author":[{"given":"BoGeum","family":"Seo","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Seoul National University , 08826 Seoul, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"DoHee","family":"Lee","sequence":"additional","affiliation":[{"name":"Center for Healthcare Robotics, Korea Institute of Science & Technology , 02792 Seoul, Republic of 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