{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T10:13:09Z","timestamp":1761559989573,"version":"3.38.0"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"18","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":2220,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/2.0\/uk\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation Biochemical reactions in cells are made of several types of biological circuits. In current systems biology, making differential equation (DE) models simulatable in silico has been an appealing, general approach to uncover a complex world of biochemical reaction dynamics. Despite of a need for simulation-aided studies, our research field has yet provided no clear answers: how to specify kinetic values in models that are difficult to measure from experimental\/theoretical analyses on biochemical kinetics.<\/jats:p><jats:p>Results: We present a novel non-parametric Bayesian approach to this problem. The key idea lies in the development of a Dirichlet process (DP) prior distribution, called Bayesian experts, which reflects substantive knowledge on reaction mechanisms inherent in given models and experimentally observable kinetic evidences to the subsequent parameter search. The DP prior identifies significant local regions of unknown parameter space before proceeding to the posterior analyses. This article reports that a Bayesian expert-inducing stochastic search can effectively explore unknown parameters of in silico transcription circuits such that solutions of DEs reproduce transcriptomic time course profiles.<\/jats:p><jats:p>Availability: A sample source code is available at the URL http:\/\/daweb.ism.ac.jp\/\u223cyoshidar\/lisdas\/<\/jats:p><jats:p>Contact: \u00a0yoshidar@ism.ac.jp<\/jats:p>","DOI":"10.1093\/bioinformatics\/btq389","type":"journal-article","created":{"date-parts":[[2010,9,7]],"date-time":"2010-09-07T17:41:46Z","timestamp":1283881306000},"page":"i589-i595","source":"Crossref","is-referenced-by-count":4,"title":["Bayesian experts in exploring reaction kinetics of transcription circuits"],"prefix":"10.1093","volume":"26","author":[{"given":"Ryo","family":"Yoshida","sequence":"first","affiliation":[{"name":"The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo, 190-8562, Japan"}]},{"given":"Masaya M.","family":"Saito","sequence":"additional","affiliation":[{"name":"The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo, 190-8562, Japan"}]},{"given":"Hiromichi","family":"Nagao","sequence":"additional","affiliation":[{"name":"The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo, 190-8562, Japan"}]},{"given":"Tomoyuki","family":"Higuchi","sequence":"additional","affiliation":[{"name":"The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo, 190-8562, Japan"}]}],"member":"286","published-online":{"date-parts":[[2010,9,4]]},"reference":[{"key":"2023012508282937900_B1","doi-asserted-by":"crossref","DOI":"10.1201\/9781420011432","volume-title":"An Introduction to Systems Biology.","author":"Alon","year":"2006"},{"key":"2023012508282937900_B2","doi-asserted-by":"crossref","first-page":"e1000052","DOI":"10.1371\/journal.pbio.1000052","article-title":"Network features of the mammalian circadian clock","volume":"7","author":"Baggs","year":"2009","journal-title":"PLoS Biol."},{"key":"2023012508282937900_B3","first-page":"121","article-title":"Variational inference for Dirichlet process mixtures","volume":"16","author":"Blei","year":"2005","journal-title":"Bayesian Anal."},{"key":"2023012508282937900_B4","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.cell.2009.01.055","article-title":"A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches","volume":"137","author":"Cantone","year":"2009","journal-title":"Cell"},{"key":"2023012508282937900_B5","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1093\/bioinformatics\/btn246","article-title":"Estimating dynamic models for gene regulation networks","volume":"24","author":"Cao","year":"2008","journal-title":"Bioinformatics"},{"key":"2023012508282937900_B6","doi-asserted-by":"crossref","first-page":"3841","DOI":"10.1091\/mbc.e03-11-0794","article-title":"Integrative analysis of cell cycle control in budding yeast","volume":"15","author":"Chen","year":"2004","journal-title":"Mol. 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