{"id":4057,"date":"2021-02-27T17:50:40","date_gmt":"2021-02-27T08:50:40","guid":{"rendered":"https:\/\/www.ibs.re.kr\/bimag\/?page_id=4057"},"modified":"2023-10-27T17:41:26","modified_gmt":"2023-10-27T08:41:26","slug":"software","status":"publish","type":"page","link":"https:\/\/www.ibs.re.kr\/bimag\/software\/","title":{"rendered":"Software"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Codes for software programs and mathematical models developed by our research.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Mathematical models for biological oscillators<\/h2>\n\n\n\n<ul class=\"is-style-default wp-block-list\">\n<li><a href=\"https:\/\/github.com\/Mathbiomed\/SA_Drosophila_Clock\" class=\"ek-link\"><strong>SA_Drosophila_Clock<br><\/strong><\/a>Matlab codes for the mathematical model of the Drosophila circadian clock and its parameter estimation using the simulated annealing (SA) method. See Jeong et al, <a href=\"https:\/\/www.pnas.org\/doi\/10.1073\/pnas.2113403119\" class=\"ek-link\">Systematic modeling-driven experiments identify distinct molecular clockworks underlying hierarchically organized pacemaker neurons<\/a>, <em>PNAS <\/em>(2022) for details.<\/li>\n\n\n\n<li><strong><a href=\"https:\/\/github.com\/Mathbiomed\/24h_model2019\" class=\"ek-link\">24h_model2019<\/a><\/strong><br>A systems pharmacological model for mammalian circadian clock of monkeys used to simulate the effect of the circadian clock modulator (Mathematica). See Kim <em>et al<\/em>., <a href=\"https:\/\/doi.org\/10.15252\/msb.20198838\" class=\"ek-link\">Systems approach reveals photosensitivity and PER2 level as determinants of clock\u2010modulator efficacy<\/a>, <em>Mol Syst Biol<\/em> (2019) for details.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\" id=\"block-f64e0608-2661-4adc-a822-fa0148f539e9\">\n<li><strong><a href=\"https:\/\/github.com\/Mathbiomed\/Phosphoswitch_Clock\" class=\"ek-link\">Phosphoswitch_Clock<\/a><\/strong><br>Mathematical models for mammalian circadian clock with the phosphoswitch (Mathematica). See Zhou, Kim et al, Mol Cell (2015) and Narasimamurthy R et al, PNAS (2018) for details.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\" id=\"block-3d872993-21f3-48f1-8408-619c5eaaeb8e\">\n<li><strong><a href=\"https:\/\/senselab.med.yale.edu\/modeldb\/ShowModel?model=145801#tabs-1\" class=\"ek-link\">Systems_pharmacology<\/a><\/strong><br>A systems pharmacology model for mammalian circadian clock of mice (Mathematica).  See Kim JK, et al, <a href=\"https:\/\/doi.org\/10.1038\/psp.2013.34\" class=\"ek-link\">Validating Chronic Pharmacological Manipulation of Circadian Rhythms, CPT:Pharmacometrics &amp; Systems Pharmacology<\/a>  (2013) for details.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\" id=\"block-e977759b-ac19-4804-bb35-3d1917aeaa72\">\n<li><strong><a href=\"https:\/\/senselab.med.yale.edu\/modeldb\/ShowModel?model=145801#tabs-1\" class=\"ek-link\">Kim-Forger model<\/a><\/strong><br>The detailed mathematical model for mammalian circadian clock (Mathematica, Matlab and XPPAUT). See Kim JK and Forger DB, A <a href=\"https:\/\/doi.org\/10.1038\/msb.2012.62\" class=\"ek-link\">Mechanism for Robust Circadian Timekeeping via stoichiometric balance<\/a>, Mol Syst Biol (2012) for details.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\" id=\"block-f64e0608-2661-4adc-a822-fa0148f539e9\">\n<li><strong><a href=\"https:\/\/github.com\/Mathbiomed\/PER_p53\" class=\"ek-link\">PER_p53<\/a><\/strong><br>A mathematical model describing molecular interactions between PER and p53 (Mathematica). See Gotoh and Kim <em>et al.<\/em>, <a href=\"https:\/\/doi.org\/10.1073\/pnas.1607984113\" class=\"ek-link\">Model-driven experimental approach reveals the complex regulatory distribution of p53 by the circadian factor Period 2<\/a>, <em>PNAS<\/em> (2016) for details.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\" id=\"block-f64e0608-2661-4adc-a822-fa0148f539e9\">\n<li><strong><a href=\"https:\/\/github.com\/Mathbiomed\/dual_strain_synthetic_oscillator\" class=\"ek-link\">dual_strain_synthetic_oscillator<\/a><\/strong><br>Mathematical model of dual strain synthetic oscillator (Mathematica). See Ye and Kim <em>et al.<\/em>, <a href=\"https:\/\/doi.org\/10.1126\/science.aaa3794\" class=\"ek-link\">Emergent genetic oscillations in a synthetic microbial consortium<\/a>, <em>Science<\/em> (2015) for details.<br><\/li>\n\n\n\n<li><strong><a href=\"https:\/\/github.com\/Mathbiomed\/Collective_phospho_circadian\" class=\"ek-link\">Collective_phospho_circadian<\/a><\/strong><br>The code simulates the system describing the transcriptional-translational feedback loop (TTFL) of PER protein by using delayed Gillespie algorithm. See &#8220;Spatially coordinated collective phosphorylation filters spatiotemporal noises for precise circadian timekeeping &#8220;, Chae et al. (2023), i<em>Science<\/em> for details.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Bayesian inference algorithms<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/github.com\/Mathbiomed\/MBI\" class=\"ek-link\"><strong>MBI<\/strong><\/a><br>Moment-based Bayesian inference method (MBI) for inferring cell-to-cell heterogeneity in the non-Markovian signaling process. See Kim <em>et al<\/em>., <a href=\"https:\/\/www.science.org\/doi\/10.1126\/sciadv.abl4598\" class=\"ek-link\">Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: the rate-limiting step number<\/a>,&nbsp;<em>Science Advances <\/em>(2022) for details.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\" id=\"block-f64e0608-2661-4adc-a822-fa0148f539e9\">\n<li><strong><a href=\"https:\/\/github.com\/Mathbiomed\/delay\" class=\"ek-link\">Delay<\/a><\/strong><br>Bayesian inference algorithm (R code) that estimates reaction rates and delay distribution of Birth-Death process with time delay.  See Choi <em>et al.<\/em>, <a href=\"https:\/\/doi.org\/10.1093\/bioinformatics\/btz574\" class=\"ek-link\">Bayesian inference of distributed time delay in transcriptional and translational regulation<\/a>, <em>Bioinformatics<\/em> (2019) for details<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\" id=\"block-f64e0608-2661-4adc-a822-fa0148f539e9\">\n<li><strong><a href=\"https:\/\/cran.r-project.org\/web\/packages\/EKMCMC\/index.html\" class=\"ek-link\">EKMCMC <\/a><\/strong><br>R package that performs the Bayesian inference for enzyme kinetics with the total quasi-steady-state approximation model. See Choi <em>et al.<\/em>, <a href=\"https:\/\/doi.org\/10.1038\/s41598-017-17072-z\" class=\"ek-link\">Beyond the Michaelis-Menten equation: Accurate and efficient estimation of enzyme kinetic parameters<\/a>, Scientific Reports (2017) for details.<\/li>\n\n\n\n<li><strong><a href=\"https:\/\/github.com\/mvcortez\/Bayesian-Inference\" class=\"ek-link\">Hierarchical Bayesian inference for systems with delay<\/a><\/strong><br>Hierarchical Bayesian inference algorithm (Python code) that estimates reaction rates and delay distribution of Birth-Death process with time delay over heterogeneous population.  See Cortez <em>et al.<\/em>, <a href=\"https:\/\/doi.org\/10.1093\/bioinformatics\/btab618\" class=\"ek-link\">Hierarchical Bayesian models of transcriptional and translational regulation processes with delays<\/a>, <em>Bioinformatics<\/em> (2021) for details.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Stochastic analysis<\/h2>\n\n\n\n<ul class=\"wp-block-list\" id=\"block-f64e0608-2661-4adc-a822-fa0148f539e9\">\n<li><strong><a href=\"https:\/\/github.com\/Mathbiomed\/Feedme\" class=\"ek-link\">Feedme<\/a><\/strong><br>Matlab code for the calculation of exact moments of biochemical reaction networks with feed-forward structures. See Kim and Sontag, Reduction of Multiscale Stochastic Biochemical Reaction Networks using Exact Moment Derivation, <em>PLoS Com Biol<\/em> (2017) for details.<\/li>\n\n\n\n<li><a href=\"https:\/\/github.com\/Mathbiomed\/ASSISTER\" class=\"ek-link\"><strong>ASSISTER<\/strong><\/a><br>Matlab code for efficient and accurate simulations of stochastic biochemical systems containing rapid reversible binding reactions. See Song <em>et al.<\/em>, Universally valid reduction of multiscale stochastic biochemical systems using simple non-elementary propensities, <em><em>PLoS Com Biol<\/em> (2021)<\/em> for details.<\/li>\n\n\n\n<li><strong><a href=\"https:\/\/github.com\/Mathbiomed\/CASTANET\" class=\"ek-link\">CASTANET<\/a><\/strong><br>Matlab code to analytically derive stationary distributions for a given stochastic biochemical reaction networks using network translation and propensity factorization based on chemical reaction network theory. See Hong <em>et al.<\/em>, <a href=\"https:\/\/doi.org\/10.1038\/s42003-021-02117-x\" class=\"ek-link\">Derivation of stationary distributions of biochemical reaction networks via structure transformation<\/a>, <em><em>Commun Biol<\/em> (2021)<\/em> for details.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Causality Inference<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/github.com\/Mathbiomed\/ION\" class=\"ek-link\"><strong>ION<\/strong><\/a><br>MATLAB code for inferring networks of biochemical systems from oscillatory data. See Tyler <em>et al.<\/em>, <a href=\"https:\/\/doi.org\/10.1093\/bioinformatics\/btab623\" class=\"ek-link\">Inferring causality in biological oscillators<\/a>, <em><em>Bioinformatics<\/em> (2021)<\/em> for details.<\/li>\n\n\n\n<li><a href=\"https:\/\/github.com\/Mathbiomed\/GOBI\" class=\"ek-link\"><strong>GOBI<\/strong><\/a><br>MATLAB code for accurate and broadly applicable causal inference method for time-series data. See Park <em>et al.<\/em>, <a href=\"https:\/\/www.nature.com\/articles\/s41467-023-39983-4\" class=\"ek-link\">A general model-based causal inference method overcomes the curse of synchrony and indirect effect<\/a>, <em>Nature Communications<\/em> (2023) for details.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Diagnosis for sleep disorders<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><a href=\"https:\/\/github.com\/Mathbiomed\/OSA-phenotyping\" class=\"ek-link\">OSA-phenotyping<\/a><\/strong><br>A computational package (Python) that can phenotype obstructive&nbsp;sleep&nbsp;apnea (OSA) patients based on their polysomnography (PSG) data. See Ma <em>et al<\/em>., <a href=\"https:\/\/doi.org\/10.1038\/s41598-021-84003-4\" class=\"ek-link\">Combined unsupervised\u2011supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea<\/a>, <em>Scientific Reports<\/em> (2021) for details.<\/li>\n\n\n\n<li><a href=\"https:\/\/github.com\/Mathbiomed\/MCQ_I\" class=\"ek-link\"><strong>MCQ_I&nbsp;<\/strong><\/a><br>The computational code for predicting the classification via RF based on either the MCQI-6 or the MCQI-14.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Sleep data imputation<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><a href=\"https:\/\/github.com\/kien286\/sleep_data_imputation_handling#sleep_data_imputation_handling\" class=\"ek-link\">SOMNI<\/a><\/strong><br>A computational package to handle missing data problems in sleep diary. See Lee <em>et al<\/em>., <a href=\"https:\/\/academic.oup.com\/sleep\/advance-article-abstract\/doi\/10.1093\/sleep\/zsad266\/7306801?utm_source=advanceaccess&amp;utm_campaign=sleep&amp;utm_medium=email\" class=\"ek-link\">Imputing missing sleep data from wearables with neural networks in real-world settings<\/a>, <em>SLEEP<\/em> (2023) for details.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Codes for software programs and mathematical models developed by our research. Mathematical models for biological oscillators Bayesian inference algorithms Stochastic analysis Causality Inference Diagnosis for sleep disorders Sleep data imputation<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_editorskit_title_hidden":false,"_editorskit_reading_time":2,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","_uag_custom_page_level_css":"","footnotes":""},"class_list":["post-4057","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Software - 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\/software\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Software - Biomedical Mathematics Group\" \/>\n<meta property=\"og:description\" content=\"Codes for software programs and mathematical models developed by our research. Mathematical models for biological oscillators Bayesian inference algorithms Stochastic analysis Causality Inference Diagnosis for sleep disorders Sleep data imputation\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.ibs.re.kr\/bimag\/software\/\" \/>\n<meta property=\"og:site_name\" content=\"Biomedical Mathematics Group\" \/>\n<meta property=\"article:modified_time\" content=\"2023-10-27T08:41:26+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=\"4 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\\\/software\\\/\",\"url\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/software\\\/\",\"name\":\"Software - Biomedical Mathematics Group\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/#website\"},\"datePublished\":\"2021-02-27T08:50:40+00:00\",\"dateModified\":\"2023-10-27T08:41:26+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/software\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/software\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/software\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Software\"}]},{\"@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":"Software - 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\/software\/","og_locale":"en_US","og_type":"article","og_title":"Software - Biomedical Mathematics Group","og_description":"Codes for software programs and mathematical models developed by our research. 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Mathematical models for biological oscillators Bayesian inference algorithms Stochastic analysis Causality Inference Diagnosis for sleep disorders Sleep data imputation","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/pages\/4057","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/comments?post=4057"}],"version-history":[{"count":34,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/pages\/4057\/revisions"}],"predecessor-version":[{"id":8642,"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/pages\/4057\/revisions\/8642"}],"wp:attachment":[{"href":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/wp\/v2\/media?parent=4057"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}