{"id":11061,"date":"2025-04-26T23:32:39","date_gmt":"2025-04-26T14:32:39","guid":{"rendered":"https:\/\/www.ibs.re.kr\/bimag\/?post_type=tribe_events&#038;p=11061"},"modified":"2025-05-28T12:59:10","modified_gmt":"2025-05-28T03:59:10","slug":"quantifying-and-correcting-bias-in-transcriptional-parameter-inference-from-single-cell-data-kangmin-lee","status":"publish","type":"tribe_events","link":"https:\/\/www.ibs.re.kr\/bimag\/event\/quantifying-and-correcting-bias-in-transcriptional-parameter-inference-from-single-cell-data-kangmin-lee\/","title":{"rendered":"Direct Estimation of Parameters in ODE Models Using WENDy &#8211; Kangmin Lee"},"content":{"rendered":"<p>In this talk, we discuss the paper &#8220;Direct Estimation of Parameters in ODE Models Using WENDy: Weak-Form Estimation of Nonlinear Dynamics&#8221; by David M. Bortz<span style=\"font-size: 1rem;\">, <\/span>Daniel A. Messenger, and Vanja Dukic, Bulletin of Mathematical Biology, 2023.<\/p>\n<p><strong>Abstract<\/strong><\/p>\n<p>We introduce the Weak-form Estimation of Nonlinear Dynamics (WENDy) method for estimating model parameters for non-linear systems of ODEs. Without relying on any numerical differential equation solvers, WENDy computes accurate estimates and is robust to large (biologically relevant) levels of measurement noise. For low dimensional systems with modest amounts of data, WENDy is competitive with conventional forward solver-based nonlinear least squares methods in terms of speed and accuracy. For both higher dimensional systems and stiff systems, WENDy is typically both faster (often by orders of magnitude) and more accurate than forward solver-based approaches. The core mathematical idea involves an efficient conversion of the strong form representation of a model to its weak form, and then solving a regression problem to perform parameter inference. The core statistical idea rests on the Errors-In-Variables framework, which necessitates the use of the iteratively reweighted least squares algorithm. Further improvements are obtained by using orthonormal test functions, created from a set of\u00a0<span class=\"mathjax-tex\"><span id=\"MathJax-Element-1-Frame\" class=\"MathJax_SVG\" style=\"box-sizing: inherit; display: inline-block; font-style: normal; font-weight: normal; line-height: normal; font-size: 18px; font-size-adjust: none; text-indent: 0px; text-align: left; text-transform: none; letter-spacing: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;\" tabindex=\"0\" role=\"presentation\" data-mathml=\"&lt;math xmlns=&quot;http:\/\/www.w3.org\/1998\/Math\/MathML&quot;&gt;&lt;msup&gt;&lt;mi&gt;C&lt;\/mi&gt;&lt;mrow class=&quot;MJX-TeXAtom-ORD&quot;&gt;&lt;mi mathvariant=&quot;normal&quot;&gt;&amp;#x221E;&lt;\/mi&gt;&lt;\/mrow&gt;&lt;\/msup&gt;&lt;\/math&gt;\"><span class=\"MJX_Assistive_MathML\" role=\"presentation\">C\u221e<\/span><\/span><\/span>\u00a0bump functions of varying support sizes.We demonstrate the high robustness and computational efficiency by applying WENDy to estimate parameters in some common models from population biology, neuroscience, and biochemistry, including logistic growth, Lotka-Volterra, FitzHugh-Nagumo, Hindmarsh-Rose, and a Protein Transduction Benchmark model. Software and code for reproducing the examples is available at\u00a0<a href=\"https:\/\/github.com\/MathBioCU\/WENDy\">https:\/\/github.com\/MathBioCU\/WENDy<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this talk, we discuss the paper &#8220;Direct Estimation of Parameters in ODE Models Using WENDy: Weak-Form Estimation of Nonlinear Dynamics&#8221; by David M. Bortz, Daniel A. Messenger, and Vanja &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/www.ibs.re.kr\/bimag\/event\/quantifying-and-correcting-bias-in-transcriptional-parameter-inference-from-single-cell-data-kangmin-lee\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Direct Estimation of Parameters in ODE Models Using WENDy &#8211; Kangmin Lee&#8221;<\/span><\/a><\/p>\n","protected":false},"author":11,"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":[],"tribe_events_cat":[219],"class_list":["post-11061","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-journal-club","cat_journal-club"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Direct Estimation of Parameters in ODE Models Using WENDy - Kangmin Lee - 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\/quantifying-and-correcting-bias-in-transcriptional-parameter-inference-from-single-cell-data-kangmin-lee\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Direct Estimation of Parameters in ODE Models Using WENDy - Kangmin Lee - Biomedical Mathematics Group\" \/>\n<meta property=\"og:description\" content=\"In this talk, we discuss the paper &#8220;Direct Estimation of Parameters in ODE Models Using WENDy: Weak-Form Estimation of Nonlinear Dynamics&#8221; by David M. 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