{"id":43376,"date":"2025-11-11T08:55:45","date_gmt":"2025-11-11T07:55:45","guid":{"rendered":"https:\/\/www.uni.lu\/fstm-en\/?post_type=news&#038;p=43376"},"modified":"2025-11-11T09:03:34","modified_gmt":"2025-11-11T08:03:34","slug":"flowgrn-reconstructing-the-dynamics-of-gene-regulation","status":"publish","type":"news","link":"https:\/\/www.uni.lu\/fstm-en\/news\/flowgrn-reconstructing-the-dynamics-of-gene-regulation\/","title":{"rendered":"FlowGRN: reconstructing the dynamics of gene regulation"},"content":{"rendered":"\n<section class=\"wp-block-unilux-blocks-free-section section\"><div class=\"container xl:max-w-screen-xl\">\n<p>How do genes control one another as a cell develops, differentiates, or transforms into disease? Understanding this complex interplay \u2014 known as&nbsp;gene regulatory networks (GRNs)&nbsp;\u2014 is key to explaining how stem cells become neurons, or how cancer cells develop themselves.&nbsp;<\/p>\n\n\n\n<p>Until now, scientists have been studying how genes interact using&nbsp;single-cell RNA sequencing (scRNA-seq), which lets them see the activity of thousands of individual cells. But this method only captures snapshots \u2014 like photos of cells at different stages. Without seeing the full journey, it\u2019s hard to figure out which genes are really driving the process.&nbsp;<\/p>\n\n\n<div class=\"wp-block-unilux-blocks-spacer is-spacer-size-md\"><\/div>\n\n\n<h2 class=\"has-text-align-left wp-block-unilux-blocks-heading\"        id=\"understanding-gene-changes-withflowgrn\"\n    >\nUnderstanding gene changes withFlowGRN<\/h2>\n\n\n\n<p>To address this challenge,&nbsp;doctoral student <a href=\"https:\/\/www.uni.lu\/fstm-en\/people\/tsz-pan-tong\/\">Tsz Pan Tong<\/a>&nbsp;and his supervisor <a href=\"https:\/\/www.uni.lu\/fstm-en\/people\/jun-pang\/\">Prof. Jun Pang<\/a> have developed&nbsp;FlowGRN, a novel computational method that leverages recent advances in&nbsp;<em>conditional flow matching<\/em>&nbsp;(CFM), a modern machine learning technique that helps models learn&nbsp;realistic transitions between different states. In this context, it enables the reconstruction of how cells evolve from one state to another. FlowGRN uses CFM to learn these hidden cellular dynamics, reconstruct cell trajectories from scRNA-seq data, and infer how genes influence one another along these developmental paths.&nbsp;<\/p>\n\n\n\n<p>In extensive tests on both simulated and experimental datasets, FlowGRN achieved high precision and ranked among the top performers in the&nbsp;BEELINE benchmark&nbsp;\u2014 a widely used framework that objectively compares gene network inference methods. The method also includes strategies to&nbsp;mitigate the impact of measurement errors in the data, helping it focus on the true biological signals. Importantly, FlowGRN can distinguish between genes that&nbsp;activate others (turning them \u201con\u201d) and those that&nbsp;suppress&nbsp;them (keeping them \u201coff\u201d), offering a more complete picture of how cellular processes are regulated.&nbsp;<\/p>\n\n\n\n<h2 class=\"has-text-align-left wp-block-unilux-blocks-heading\"        id=\"medical-applications\"\n    >\nMedical applications<\/h2>\n\n\n\n<p>By understanding how cells transform over time, FlowGRN helps scientists better understand the genetic changes that drive development and disease. These insights can make experiments more focused and efficient, and in the long run, support advances in&nbsp;precision medicine. For example, it could point to promising drug targets or help anticipate how patients might respond to treatment.&nbsp;<\/p>\n\n\n\n<p>\u201c<em>Biologists ultimately want to see what a cell is doing<\/em>,\u201d explains Tsz Pan. \u201c<em>If we can capture how stem cells grow into muscle or neural cells and understand what goes wrong when they become cancerous, we can start to decode the logic of life at the cellular level and find ways to prevent these transformations.\u201d<\/em>&nbsp;<\/p>\n\n\n<div class=\"wp-block-unilux-blocks-spacer is-spacer-size-sm\"><\/div>\n\n\n<p>Recently, the project drew international attention at the&nbsp;<a href=\"https:\/\/acm-bcb.org\">ACM BCB 2025 conference<\/a>&nbsp;in Philadelphia, where the team\u2019s paper earned a&nbsp;Best Paper Honorable Mention Award. This recognition underlines the quality and potential impact of their work. \u201c<em>This award strengthens our confidence in refining FlowGRN,<\/em>\u201d comments doctoral student Tsz Pan.&nbsp;\u201c<em>It\u2019s also an important step toward a more data-driven understanding of how cells work.<\/em>\u201d, adds Prof. Jun Pang. &nbsp;<\/p>\n\n\n\n<p>Looking ahead, the researchers aim to broaden FlowGRN\u2019s scope by applying it to other types of single-cell data, such as&nbsp;chromatin accessibility, which shows how DNA is folded and which genes are open for use, and&nbsp;spatial omics, which reveals where gene activity occurs inside tissues.&nbsp;<\/p>\n\n\n<div class=\"wp-block-unilux-blocks-spacer is-spacer-size-sm\"><\/div>\n\n\n<p><em>The project is funded under the Audacity Grant (AUDACITY-2021) from the Institute for Advanced Studies (IAS) and under INTER\/NCN\/24\/18732364\/EdgeCR from the Fonds National de Recherche (FNR). <\/em><\/p>\n<\/div><\/section>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":325,"featured_media":43377,"template":"","format":"standard","meta":{"featured_image_focal_point":{"x":0.54,"y":0.45},"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"","uluxPrePostTitle":"","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false},"news-category":[8],"news-topic":[14,23],"organisation":[31,206],"authorship":[325],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.3 (Yoast SEO v22.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>FlowGRN: reconstructing the dynamics of gene regulation - FSTM news I University of Luxembourg<\/title>\n<meta name=\"description\" content=\"How do genes control one another as a cell develops, differentiates, or transforms into disease? 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