{"id":12388,"date":"2025-08-20T21:25:28","date_gmt":"2025-08-20T21:25:28","guid":{"rendered":"https:\/\/www.europesays.com\/ie\/12388\/"},"modified":"2025-08-20T21:25:28","modified_gmt":"2025-08-20T21:25:28","slug":"ai-tool-shows-exactly-when-genes-turn-on-and-off","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ie\/12388\/","title":{"rendered":"AI Tool Shows Exactly When Genes Turn On and Off"},"content":{"rendered":"<p><strong>Summary: <\/strong>Researchers have developed an AI-powered tool called chronODE that models how genes turn on and off during brain development. By combining mathematics, machine learning, and genomic data, the method identifies exact \u201cswitching points\u201d that determine when genes reach maximum activity.<\/p>\n<p>These findings reveal that most genes follow predictable activation patterns and can be classified into subtypes such as accelerators, switchers, and decelerators. The approach could eventually allow doctors to time gene therapies or drug interventions at the most effective moment.<\/p>\n<p><strong>Key Facts<\/strong><\/p>\n<ul class=\"wp-block-list\">\n<li><strong>chronODE Tool:<\/strong> Uses math and AI to model real-time gene activation and chromatin changes.<\/li>\n<li><strong>Switching Points:<\/strong> Identifies critical moments when intervention could alter disease progression.<\/li>\n<li><strong>Gene Patterns:<\/strong> Reveals predictable categories of gene behavior during development.<\/li>\n<\/ul>\n<p><strong>Source: <\/strong>Yale<\/p>\n<p><strong>A Yale research team has created a new computer tool that can pinpoint when exactly genes turn on and off over time during brain development \u2014 a finding that may one day help doctors identify the optimal window to deploy gene therapy\u00a0treatments.<\/strong><\/p>\n<p>Dubbed \u201cchronODE,\u201d the tool uses math and machine learning to model how gene activity and chromatin (the\u00a0DNA\u00a0and protein mix that forms chromosomes) patterns change over time. The tool may offer a variety of applications in disease modeling and basic genomic research and perhaps lead to future therapeutic\u00a0uses.<\/p>\n<p>  <img fetchpriority=\"high\" decoding=\"async\" width=\"1200\" height=\"800\" src=\"https:\/\/www.europesays.com\/ie\/wp-content\/uploads\/2025\/08\/ai-genetics-brin-development-neuroscience.jpg\" alt=\"This shows DNA and computer code.\"  \/> They found that most genes follow predictable developmental patterns, which are dictated by their role in a cell and determine how quickly they reach maximum influence on the cell. Credit: Neuroscience News<\/p>\n<p>\u201cBasically, we have an equation that can determine the precise moment of gene activation, which may dictate important steps such as the transition from one developmental or disease stage to another,\u201d\u00a0said Mor Frank, a postdoctoral associate in the Department of Biophysics and Biochemistry in Yale\u2019s Faculty of Arts and Sciences (FAS) and study co-author.<\/p>\n<p>\u201cConsequently, this may represent a potential way to identify, in the future,\u00a0critical points for therapeutic\u00a0intervention.\u201d\u00a0<\/p>\n<p>Results of the study were published August 19 in the journal\u00a0Nature Communications.<\/p>\n<p>For the study, the research team wanted to determine not just\u00a0when\u00a0genes activate, but\u00a0how\u00a0their activation changes over the course of brain development. Genes activate at different points in cell development, but mapping gene development has been difficult. And past studies have focused on isolated moments in time, not on how gene expression evolves over\u00a0time.<\/p>\n<p>In this case, the researchers used a logistic equation (a mathematical equation useful for modeling dynamic processes) to measure when and how rapidly genes turn on and off in developing mouse brains.<\/p>\n<p>They found that most genes follow simple and gradual activation patterns, and that genes can be grouped into subtypes, including accelerators that speed up during late stages of development; switchers that speed up and then slow down; and decelerators that just slow\u00a0down.<\/p>\n<p>Researchers then developed an\u00a0AI\u00a0model to predict gene expression over time based on changes in nearby chromatin. The model worked well, especially for genes with a more complex regulation, and the entire procedure established the chronODE\u00a0method.<\/p>\n<p>They found that most genes follow predictable developmental patterns, which are dictated by their role in a cell and determine how quickly they reach maximum influence on the\u00a0cell.<\/p>\n<p>\u201cIn a situation where you\u2019re treating genetic disease, you\u2019d want to shut down the gene before it reaches its full potential, after which it\u2019s too late,\u201d said co-author Beatrice Borsari, who is also a postdoctoral associate in biophysics and\u00a0biochemistry.<\/p>\n<p>\u201cOur equation will tell you exactly the switching point \u2014 or the point of no return after which the drug will not have the same effect on the gene\u2019s expression,\u201d Borsari\u00a0said.<\/p>\n<p>\u201cThere are many cases where it\u2019s not just important to characterize the developmental direction you go, but also how fast you reach a certain point, and that\u2019s what this model is allowing us to do for the first time,\u201d added Mark Gerstein, the\u00a0Albert L. Williams Professor of Biomedical Informatics at Yale School of Medicine and a professor of molecular biophysics and biochemistry, computer science, and of statistics and data science in\u00a0FAS, and the\u00a0study\u2019s lead\u00a0author.<\/p>\n<p>Borsari and Frank underscore that the potential applications in the pharmacokinetic area are\u00a0major.<\/p>\n<p>Researchers called their new method \u201cchronODE,\u201d a name that merges the concept of time (Chronos is the god of time in Greek mythology) with the mathematical framework of ordinary differential equations (ODEs.)<\/p>\n<p>\u201cWe analyze time-series biological data using the logistic\u00a0ODE,\u201d Borsari said. \u201cIn a sense, the name captures the multidisciplinary nature of our research. We work where biology meets the beauty of math. We use mathematical models to describe and predict complex biological phenomena \u2014 in our case, temporal patterns in genomic\u00a0data.\u201d<\/p>\n<p>Borsari is a computational biologist with expertise in genetics and bioinformatics, while Frank is a biomedical engineer with a strong foundation in machine learning and mathematics. \u201cOur diverse skills create a highly synergistic collaboration, and we learn a lot from each other,\u201d Borsari\u00a0said.<\/p>\n<p>Other study authors include research associates Eve S. Wattenberg, Ke Xu, Susanna X. Liu, and Xuezhu\u00a0Yu.<\/p>\n<p>About this AI, genetics, and neurodevelopment research news<\/p>\n<p class=\"has-background\" style=\"background-color:#ffffe8\"><strong>Author: <\/strong><a href=\"http:\/\/neurosciencenews.com\/cdn-cgi\/l\/email-protection#1570797c6f747770617d3b767a7b7b7a79796c556c7479703b707160\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Bess Connolly<\/a><br \/><strong>Source: <\/strong><a href=\"http:\/\/yale.edu\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Yale<\/a><br \/><strong>Contact: <\/strong>Bess Connolly \u2013 Yale<br \/><strong>Image: <\/strong>The image is credited to Neuroscience News<\/p>\n<p class=\"has-background\" style=\"background-color:#ffffe8\"><strong>Original Research: <\/strong>Open access.<br \/>\u201c<a href=\"https:\/\/www.nature.com\/articles\/s41467-025-61921-9\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">The chronODE framework for modelling multi-omic time series with ordinary differential equations and machine learning<\/a>\u201d by Eve S. Wattenberg et al. Nature Communications<\/p>\n<p><strong>Abstract<\/strong><\/p>\n<p><strong>The chronODE framework for modelling multi-omic time series with ordinary differential equations and machine learning<\/strong><\/p>\n<p>Many genome-wide studies capture isolated moments in cell differentiation or organismal development. Conversely, longitudinal studies provide a more direct way to study these kinetic processes.<\/p>\n<p>Here, we present an approach for modeling gene-expression and chromatin kinetics from such studies: chronODE, an interpretable framework based on ordinary differential equations. chronODE incorporates two parameters that capture biophysical constraints governing the initial cooperativity and later saturation in gene expression.<\/p>\n<p>These parameters group genes into three major kinetic patterns: accelerators, switchers, and decelerators.<\/p>\n<p>Applying chronODE to bulk and single-cell time-series data from mouse brain development reveals that most genes (~87%) follow simple logistic kinetics.<\/p>\n<p>Among them, genes with rapid acceleration and high saturation values are rare, highlighting biochemical limitations that prevent cells from attaining both simultaneously.<\/p>\n<p>Early- and late-emerging cell types display distinct kinetic patterns, with essential genes ramping up faster.<\/p>\n<p>Extending chronODE to chromatin, we find that genes regulated by both enhancer and silencer\u00a0cis-regulatory elements are enriched in brain-specific functions.<\/p>\n<p>Finally, we develop a bidirectional recurrent neural network to predict changes in gene expression from corresponding chromatin changes, successfully capturing the cumulative effect of multiple regulatory elements.<\/p>\n<p>Overall, our framework allows investigation of the kinetics of gene regulation in diverse biological systems.<\/p>\n","protected":false},"excerpt":{"rendered":"Summary: Researchers have developed an AI-powered tool called chronODE that models how genes turn on and off during&hellip;\n","protected":false},"author":2,"featured_media":12389,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[272],"tags":[291,289,11766,1277,612,11767,18,458,19,17,610,1280,11768,1281,133,11769],"class_list":{"0":"post-12388","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-genetics","8":"tag-ai","9":"tag-artificial-intelligence","10":"tag-brain-development","11":"tag-brain-research","12":"tag-deep-learning","13":"tag-developmental-neuroscience","14":"tag-eire","15":"tag-genetics","16":"tag-ie","17":"tag-ireland","18":"tag-machine-learning","19":"tag-neurobiology","20":"tag-neurodevelopment","21":"tag-neuroscience","22":"tag-science","23":"tag-yale"},"share_on_mastodon":{"url":"","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/12388","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/comments?post=12388"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/12388\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media\/12389"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media?parent=12388"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/categories?post=12388"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/tags?post=12388"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}