{"id":3985,"date":"2021-02-23T18:45:56","date_gmt":"2021-02-23T09:45:56","guid":{"rendered":"https:\/\/www.ibs.re.kr\/bimag\/?post_type=tribe_events&#038;p=3985"},"modified":"2021-04-06T16:53:37","modified_gmt":"2021-04-06T07:53:37","slug":"2020-12-24_2","status":"publish","type":"tribe_events","link":"https:\/\/www.ibs.re.kr\/bimag\/event\/2020-12-24_2\/","title":{"rendered":"Dae Wook Kim, Neural network aided approximation and parameter inference of stochastic models of gene expression"},"content":{"rendered":"<p>We will discuss about &#8220;Neural network aided approximation and parameter inference of stochastic models of gene expression&#8221;, Jian <em>et al.<\/em>, bioRxiv (2020).<\/p>\n<p>Non-Markov models of stochastic biochemical kinetics often incorporate explicit time delays to effectively model large numbers of intermediate biochemical processes. Analysis and simulation of these models, as well as the inference of their parameters from data, are fraught with difficulties because the dynamics depends on the system\u2019s history. Here we use an artificial neural network to approximate the time-dependent distributions of non-Markov models by the solutions of much simpler time-inhomogeneous Markov models; the approximation does not increase the dimensionality of the model and simultaneously leads to inference of the kinetic parameters. The training of the neural network uses a relatively small set of noisy measurements generated by experimental data or stochastic simulations of the non-Markov model. We show using a variety of models, where the delays stem from transcriptional processes and feedback control, that the Markov models learnt by the neural network accurately reflect the stochastic dynamics across parameter space.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We will discuss about &#8220;Neural network aided approximation and parameter inference of stochastic models of gene expression&#8221;, Jian et al., bioRxiv (2020). Non-Markov models of stochastic biochemical kinetics often incorporate &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/www.ibs.re.kr\/bimag\/event\/2020-12-24_2\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Dae Wook Kim, Neural network aided approximation and parameter inference of stochastic models of gene expression&#8221;<\/span><\/a><\/p>\n","protected":false},"author":3,"featured_media":0,"template":"","meta":{"_editorskit_title_hidden":false,"_editorskit_reading_time":0,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","_uag_custom_page_level_css":"","_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[229,230],"tribe_events_cat":[219,231],"class_list":["post-3985","tribe_events","type-tribe_events","status-publish","hentry","tag-neural-network","tag-parameter-inference","tribe_events_cat-journal-club","tribe_events_cat-seminar","cat_journal-club","cat_seminar"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Dae Wook Kim, Neural network aided approximation and parameter inference of stochastic models of gene expression - Biomedical Mathematics Group<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ibs.re.kr\/bimag\/event\/2020-12-24_2\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Dae Wook Kim, Neural network aided approximation and parameter inference of stochastic models of gene expression - Biomedical Mathematics Group\" \/>\n<meta property=\"og:description\" content=\"We will discuss about &#8220;Neural network aided approximation and parameter inference of stochastic models of gene expression&#8221;, Jian et al., bioRxiv (2020). Non-Markov models of stochastic biochemical kinetics often incorporate &hellip; Continue reading &quot;Dae Wook Kim, Neural network aided approximation and parameter inference of stochastic models of gene expression&quot;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.ibs.re.kr\/bimag\/event\/2020-12-24_2\/\" \/>\n<meta property=\"og:site_name\" content=\"Biomedical Mathematics Group\" \/>\n<meta property=\"article:modified_time\" content=\"2021-04-06T07:53:37+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/2020-12-24_2\\\/\",\"url\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/2020-12-24_2\\\/\",\"name\":\"Dae Wook Kim, Neural network aided approximation and parameter inference of stochastic models of gene expression - Biomedical Mathematics Group\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/#website\"},\"datePublished\":\"2021-02-23T09:45:56+00:00\",\"dateModified\":\"2021-04-06T07:53:37+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/2020-12-24_2\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/2020-12-24_2\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/2020-12-24_2\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Events\",\"item\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/events\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Dae Wook Kim, Neural network aided approximation and parameter inference of stochastic models of gene expression\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/#website\",\"url\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/\",\"name\":\"Biomedical Mathematics Group\",\"description\":\"\uae30\ucd08\uacfc\ud559\uc5f0\uad6c\uc6d0 \uc758\uc0dd\uba85\uc218\ud559\uadf8\ub8f9\",\"publisher\":{\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/#organization\",\"name\":\"IBS Biomedical Mathematics Group\",\"url\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/cms\\\/wp-content\\\/uploads\\\/2021\\\/02\\\/ibs-circle-1.png\",\"contentUrl\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/cms\\\/wp-content\\\/uploads\\\/2021\\\/02\\\/ibs-circle-1.png\",\"width\":250,\"height\":250,\"caption\":\"IBS Biomedical Mathematics Group\"},\"image\":{\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/#\\\/schema\\\/logo\\\/image\\\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Dae Wook Kim, Neural network aided approximation and parameter inference of stochastic models of gene expression - Biomedical Mathematics Group","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.ibs.re.kr\/bimag\/event\/2020-12-24_2\/","og_locale":"en_US","og_type":"article","og_title":"Dae Wook Kim, Neural network aided approximation and parameter inference of stochastic models of gene expression - Biomedical Mathematics Group","og_description":"We will discuss about &#8220;Neural network aided approximation and parameter inference of stochastic models of gene expression&#8221;, Jian et al., bioRxiv (2020). Non-Markov models of stochastic biochemical kinetics often incorporate &hellip; Continue reading \"Dae Wook Kim, Neural network aided approximation and parameter inference of stochastic models of gene expression\"","og_url":"https:\/\/www.ibs.re.kr\/bimag\/event\/2020-12-24_2\/","og_site_name":"Biomedical Mathematics Group","article_modified_time":"2021-04-06T07:53:37+00:00","twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"1 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.ibs.re.kr\/bimag\/event\/2020-12-24_2\/","url":"https:\/\/www.ibs.re.kr\/bimag\/event\/2020-12-24_2\/","name":"Dae Wook Kim, Neural network aided approximation and parameter inference of stochastic models of gene expression - Biomedical Mathematics Group","isPartOf":{"@id":"https:\/\/www.ibs.re.kr\/bimag\/#website"},"datePublished":"2021-02-23T09:45:56+00:00","dateModified":"2021-04-06T07:53:37+00:00","breadcrumb":{"@id":"https:\/\/www.ibs.re.kr\/bimag\/event\/2020-12-24_2\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.ibs.re.kr\/bimag\/event\/2020-12-24_2\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.ibs.re.kr\/bimag\/event\/2020-12-24_2\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.ibs.re.kr\/bimag\/"},{"@type":"ListItem","position":2,"name":"Events","item":"https:\/\/www.ibs.re.kr\/bimag\/events\/"},{"@type":"ListItem","position":3,"name":"Dae Wook Kim, Neural network aided approximation and parameter inference of stochastic models of gene expression"}]},{"@type":"WebSite","@id":"https:\/\/www.ibs.re.kr\/bimag\/#website","url":"https:\/\/www.ibs.re.kr\/bimag\/","name":"Biomedical Mathematics Group","description":"\uae30\ucd08\uacfc\ud559\uc5f0\uad6c\uc6d0 \uc758\uc0dd\uba85\uc218\ud559\uadf8\ub8f9","publisher":{"@id":"https:\/\/www.ibs.re.kr\/bimag\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.ibs.re.kr\/bimag\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.ibs.re.kr\/bimag\/#organization","name":"IBS Biomedical Mathematics Group","url":"https:\/\/www.ibs.re.kr\/bimag\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.ibs.re.kr\/bimag\/#\/schema\/logo\/image\/","url":"https:\/\/www.ibs.re.kr\/bimag\/cms\/wp-content\/uploads\/2021\/02\/ibs-circle-1.png","contentUrl":"https:\/\/www.ibs.re.kr\/bimag\/cms\/wp-content\/uploads\/2021\/02\/ibs-circle-1.png","width":250,"height":250,"caption":"IBS Biomedical Mathematics Group"},"image":{"@id":"https:\/\/www.ibs.re.kr\/bimag\/#\/schema\/logo\/image\/"}}]}},"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false,"dimag-thumbnail":false,"twentyseventeen-featured-image":false,"twentyseventeen-thumbnail-avatar":false},"uagb_author_info":{"display_name":"BIMAG","author_link":"https:\/\/www.ibs.re.kr\/bimag\/author\/hphongblog\/"},"uagb_comment_info":0,"uagb_excerpt":"We will discuss about &#8220;Neural network aided approximation and parameter inference of stochastic models of gene expression&#8221;, Jian et al., bioRxiv (2020). Non-Markov models of stochastic biochemical kinetics often incorporate &hellip; Continue reading \"Dae Wook Kim, Neural network aided approximation and parameter inference of stochastic models of gene expression\"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tribe_events\/3985","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tribe_events"}],"about":[{"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/types\/tribe_events"}],"author":[{"embeddable":true,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/users\/3"}],"version-history":[{"count":4,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tribe_events\/3985\/revisions"}],"predecessor-version":[{"id":4155,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tribe_events\/3985\/revisions\/4155"}],"wp:attachment":[{"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/media?parent=3985"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tags?post=3985"},{"taxonomy":"tribe_events_cat","embeddable":true,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tribe_events_cat?post=3985"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}