{"id":12998,"date":"2026-08-29T14:17:16","date_gmt":"2026-08-29T05:17:16","guid":{"rendered":"https:\/\/www.ibs.re.kr\/bimag\/?post_type=tribe_events&#038;p=12998"},"modified":"2026-08-29T18:31:23","modified_gmt":"2026-08-29T09:31:23","slug":"design-of-single-cell-experiments-to-efficiently-infer-predictive-mechanistic-models-for-stochastic-gene-expression-brian-munsky","status":"publish","type":"tribe_events","link":"https:\/\/www.ibs.re.kr\/bimag\/event\/design-of-single-cell-experiments-to-efficiently-infer-predictive-mechanistic-models-for-stochastic-gene-expression-brian-munsky\/","title":{"rendered":"Design of Single-Cell Experiments to Efficiently Infer Predictive Mechanistic Models for Stochastic Gene Expression &#8211; Brian Munsky"},"content":{"rendered":"<p><span data-sheets-root=\"1\">Biochemical assays have made outstanding progress to elucidate how cells sense and respond to stimuli, but mechanistic and parametric uncertainties preclude quantitative predictions for the full spatial, temporal, and heterogeneous responses of signal-activated gene expression. Stochastic models use random noise as an abstraction to account for these unknown or uncertain dynamics. When inferred from appropriate single-cell experiments, such as smFISH or immunocytochemistry (ICC), these models can quantitatively predict complex biological responses in new environments. However, many smFISH\/ICC experiments are possible for different induction levels, measurement times, or observables, and each may be time-consuming, expensive, or subject to labeling, imaging, or data processing errors. We introduce the Finite State Projection based Fisher Information Matrix (FSP-FIM) as a rigorous guide for the design of single-cell experiments. We extend the FSP-FIM with empirical probabilistic distortion operators to account for unavoidable measurement errors. By analyzing different combinations of models, experiment designs, and data distortions, we discover practical working principles to simplify single-cell experiments while allowing for the use of inexpensive (or \u2018crappy\u2019) imaging conditions. We validate the FSP-FIM approach in HeLa cells using ICC data for glucocorticoid receptor transport and smFISH data for DUSP1 gene regulation upon stimulation with a synthetic corticosteroid.<\/span><\/p>\n<p>Zoom : 997 8258 4700 (pw : 1234)<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Biochemical assays have made outstanding progress to elucidate how cells sense and respond to stimuli, but mechanistic and parametric uncertainties preclude quantitative predictions for the full spatial, temporal, and heterogeneous &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/www.ibs.re.kr\/bimag\/event\/design-of-single-cell-experiments-to-efficiently-infer-predictive-mechanistic-models-for-stochastic-gene-expression-brian-munsky\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Design of Single-Cell Experiments to Efficiently Infer Predictive Mechanistic Models for Stochastic Gene Expression &#8211; Brian Munsky&#8221;<\/span><\/a><\/p>\n","protected":false},"author":13,"featured_media":13023,"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":[],"tribe_events_cat":[221],"class_list":["post-12998","tribe_events","type-tribe_events","status-publish","has-post-thumbnail","hentry","tribe_events_cat-biomedical-mathematics-colloquium","cat_biomedical-mathematics-colloquium"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Design of Single-Cell Experiments to Efficiently Infer Predictive Mechanistic Models for Stochastic Gene Expression - Brian Munsky - Biomedical Mathematics Group<\/title>\n<meta name=\"description\" content=\"Biochemical assays have made outstanding progress to elucidate how cells sense and respond to stimuli, but mechanistic and parametric uncertainties preclude quantitative predictions for the full spatial, temporal, and heterogeneous responses of signal-activated gene expression. Stochastic models use random noise as an abstraction to account for these unknown or uncertain dynamics. When inferred from appropriate single-cell experiments, such as smFISH or immunocytochemistry (ICC), these models can quantitatively predict complex biological responses in new environments. However, many smFISH\/ICC experiments are possible for different induction levels, measurement times, or observables, and each may be time-consuming, expensive, or subject to labeling, imaging, or data processing errors. We introduce the Finite State Projection based Fisher Information Matrix (FSP-FIM) as a rigorous guide for the design of single-cell experiments. We extend the FSP-FIM with empirical probabilistic distortion operators to account for unavoidable measurement errors. By analyzing different combinations of models, experiment designs, and data distortions, we discover practical working principles to simplify single-cell experiments while allowing for the use of inexpensive (or \u2018crappy\u2019) imaging conditions. We validate the FSP-FIM approach in HeLa cells using ICC data for glucocorticoid receptor transport and smFISH data for DUSP1 gene regulation upon stimulation with a synthetic corticosteroid.\" \/>\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\/design-of-single-cell-experiments-to-efficiently-infer-predictive-mechanistic-models-for-stochastic-gene-expression-brian-munsky\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Design of Single-Cell Experiments to Efficiently Infer Predictive Mechanistic Models for Stochastic Gene Expression - Brian Munsky - Biomedical Mathematics Group\" \/>\n<meta property=\"og:description\" content=\"Biochemical assays have made outstanding progress to elucidate how cells sense and respond to stimuli, but mechanistic and parametric uncertainties preclude quantitative predictions for the full spatial, temporal, and heterogeneous responses of signal-activated gene expression. Stochastic models use random noise as an abstraction to account for these unknown or uncertain dynamics. When inferred from appropriate single-cell experiments, such as smFISH or immunocytochemistry (ICC), these models can quantitatively predict complex biological responses in new environments. However, many smFISH\/ICC experiments are possible for different induction levels, measurement times, or observables, and each may be time-consuming, expensive, or subject to labeling, imaging, or data processing errors. We introduce the Finite State Projection based Fisher Information Matrix (FSP-FIM) as a rigorous guide for the design of single-cell experiments. We extend the FSP-FIM with empirical probabilistic distortion operators to account for unavoidable measurement errors. By analyzing different combinations of models, experiment designs, and data distortions, we discover practical working principles to simplify single-cell experiments while allowing for the use of inexpensive (or \u2018crappy\u2019) imaging conditions. We validate the FSP-FIM approach in HeLa cells using ICC data for glucocorticoid receptor transport and smFISH data for DUSP1 gene regulation upon stimulation with a synthetic corticosteroid.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.ibs.re.kr\/bimag\/event\/design-of-single-cell-experiments-to-efficiently-infer-predictive-mechanistic-models-for-stochastic-gene-expression-brian-munsky\/\" \/>\n<meta property=\"og:site_name\" content=\"Biomedical Mathematics Group\" \/>\n<meta property=\"article:modified_time\" content=\"2026-08-29T09:31:23+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.ibs.re.kr\/bimag\/cms\/wp-content\/uploads\/2026\/08\/images-4-e1787995840661.jpeg\" \/>\n\t<meta property=\"og:image:width\" content=\"300\" \/>\n\t<meta property=\"og:image:height\" content=\"300\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\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=\"2 minutes\" \/>\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\\\/design-of-single-cell-experiments-to-efficiently-infer-predictive-mechanistic-models-for-stochastic-gene-expression-brian-munsky\\\/\",\"url\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/design-of-single-cell-experiments-to-efficiently-infer-predictive-mechanistic-models-for-stochastic-gene-expression-brian-munsky\\\/\",\"name\":\"Design of Single-Cell Experiments to Efficiently Infer Predictive Mechanistic Models for Stochastic Gene Expression - Brian Munsky - Biomedical Mathematics Group\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/design-of-single-cell-experiments-to-efficiently-infer-predictive-mechanistic-models-for-stochastic-gene-expression-brian-munsky\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/design-of-single-cell-experiments-to-efficiently-infer-predictive-mechanistic-models-for-stochastic-gene-expression-brian-munsky\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/cms\\\/wp-content\\\/uploads\\\/2026\\\/08\\\/images-4-e1787995840661.jpeg\",\"datePublished\":\"2026-08-29T05:17:16+00:00\",\"dateModified\":\"2026-08-29T09:31:23+00:00\",\"description\":\"Biochemical assays have made outstanding progress to elucidate how cells sense and respond to stimuli, but mechanistic and parametric uncertainties preclude quantitative predictions for the full spatial, temporal, and heterogeneous responses of signal-activated gene expression. Stochastic models use random noise as an abstraction to account for these unknown or uncertain dynamics. When inferred from appropriate single-cell experiments, such as smFISH or immunocytochemistry (ICC), these models can quantitatively predict complex biological responses in new environments. However, many smFISH\\\/ICC experiments are possible for different induction levels, measurement times, or observables, and each may be time-consuming, expensive, or subject to labeling, imaging, or data processing errors. We introduce the Finite State Projection based Fisher Information Matrix (FSP-FIM) as a rigorous guide for the design of single-cell experiments. We extend the FSP-FIM with empirical probabilistic distortion operators to account for unavoidable measurement errors. By analyzing different combinations of models, experiment designs, and data distortions, we discover practical working principles to simplify single-cell experiments while allowing for the use of inexpensive (or \u2018crappy\u2019) imaging conditions. We validate the FSP-FIM approach in HeLa cells using ICC data for glucocorticoid receptor transport and smFISH data for DUSP1 gene regulation upon stimulation with a synthetic corticosteroid.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/design-of-single-cell-experiments-to-efficiently-infer-predictive-mechanistic-models-for-stochastic-gene-expression-brian-munsky\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/design-of-single-cell-experiments-to-efficiently-infer-predictive-mechanistic-models-for-stochastic-gene-expression-brian-munsky\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/design-of-single-cell-experiments-to-efficiently-infer-predictive-mechanistic-models-for-stochastic-gene-expression-brian-munsky\\\/#primaryimage\",\"url\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/cms\\\/wp-content\\\/uploads\\\/2026\\\/08\\\/images-4-e1787995840661.jpeg\",\"contentUrl\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/cms\\\/wp-content\\\/uploads\\\/2026\\\/08\\\/images-4-e1787995840661.jpeg\",\"width\":300,\"height\":300},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/design-of-single-cell-experiments-to-efficiently-infer-predictive-mechanistic-models-for-stochastic-gene-expression-brian-munsky\\\/#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\":\"Design of Single-Cell Experiments to Efficiently Infer Predictive Mechanistic Models for Stochastic Gene Expression &#8211; Brian Munsky\"}]},{\"@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":"Design of Single-Cell Experiments to Efficiently Infer Predictive Mechanistic Models for Stochastic Gene Expression - Brian Munsky - Biomedical Mathematics Group","description":"Biochemical assays have made outstanding progress to elucidate how cells sense and respond to stimuli, but mechanistic and parametric uncertainties preclude quantitative predictions for the full spatial, temporal, and heterogeneous responses of signal-activated gene expression. Stochastic models use random noise as an abstraction to account for these unknown or uncertain dynamics. When inferred from appropriate single-cell experiments, such as smFISH or immunocytochemistry (ICC), these models can quantitatively predict complex biological responses in new environments. However, many smFISH\/ICC experiments are possible for different induction levels, measurement times, or observables, and each may be time-consuming, expensive, or subject to labeling, imaging, or data processing errors. We introduce the Finite State Projection based Fisher Information Matrix (FSP-FIM) as a rigorous guide for the design of single-cell experiments. We extend the FSP-FIM with empirical probabilistic distortion operators to account for unavoidable measurement errors. By analyzing different combinations of models, experiment designs, and data distortions, we discover practical working principles to simplify single-cell experiments while allowing for the use of inexpensive (or \u2018crappy\u2019) imaging conditions. We validate the FSP-FIM approach in HeLa cells using ICC data for glucocorticoid receptor transport and smFISH data for DUSP1 gene regulation upon stimulation with a synthetic corticosteroid.","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\/design-of-single-cell-experiments-to-efficiently-infer-predictive-mechanistic-models-for-stochastic-gene-expression-brian-munsky\/","og_locale":"en_US","og_type":"article","og_title":"Design of Single-Cell Experiments to Efficiently Infer Predictive Mechanistic Models for Stochastic Gene Expression - Brian Munsky - Biomedical Mathematics Group","og_description":"Biochemical assays have made outstanding progress to elucidate how cells sense and respond to stimuli, but mechanistic and parametric uncertainties preclude quantitative predictions for the full spatial, temporal, and heterogeneous responses of signal-activated gene expression. 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By analyzing different combinations of models, experiment designs, and data distortions, we discover practical working principles to simplify single-cell experiments while allowing for the use of inexpensive (or \u2018crappy\u2019) imaging conditions. 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By analyzing different combinations of models, experiment designs, and data distortions, we discover practical working principles to simplify single-cell experiments while allowing for the use of inexpensive (or \u2018crappy\u2019) imaging conditions. 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Continue reading \"Design of Single-Cell Experiments to Efficiently Infer Predictive Mechanistic Models for Stochastic Gene Expression &#8211; Brian Munsky\"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tribe_events\/12998","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\/13"}],"version-history":[{"count":1,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tribe_events\/12998\/revisions"}],"predecessor-version":[{"id":13000,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tribe_events\/12998\/revisions\/13000"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/media\/13023"}],"wp:attachment":[{"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/media?parent=12998"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tags?post=12998"},{"taxonomy":"tribe_events_cat","embeddable":true,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/tribe_events_cat?post=12998"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}