{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T12:34:27Z","timestamp":1767962067687,"version":"3.49.0"},"reference-count":14,"publisher":"Oxford University Press (OUP)","issue":"14","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2008,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Transcription regulation is a fundamental process in biology, and it is important to model the dynamic behavior of gene regulation networks. Many approaches have been proposed to specify the network structure. However, finding the network connectivity is not sufficient to understand the network dynamics. Instead, one needs to model the regulation reactions, usually with a set of ordinary differential equations (ODEs). Because some of the parameters involved in these ODEs are unknown, their values need to be inferred from the observed data.<\/jats:p>\n               <jats:p>Results: In this article, we introduce the generalized profiling method to estimate ODE parameters in a gene regulation network from microarray gene expression data which can be rather noisy. Because numerically solving ODEs is computationally expensive, we apply the penalized smoothing technique, a fast and stable computational method to approximate ODE solutions. The ODE solutions with our parameter estimates fit the data well. A goodness-of-fit test of dynamic models is developed to identify gene regulation networks.<\/jats:p>\n               <jats:p>Contact: \u00a0hongyu.zhao@yale.edu<\/jats:p>","DOI":"10.1093\/bioinformatics\/btn246","type":"journal-article","created":{"date-parts":[[2008,5,28]],"date-time":"2008-05-28T03:07:41Z","timestamp":1211944061000},"page":"1619-1624","source":"Crossref","is-referenced-by-count":37,"title":["Estimating dynamic models for gene regulation networks"],"prefix":"10.1093","volume":"24","author":[{"given":"Jiguo","family":"Cao","sequence":"first","affiliation":[{"name":"1 Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, V5A1S6, Canada and 2Department of Epidemiology and Public Health, Yale University, School of Medicine, 60 College Street, New Haven, CT 06520-8034, USA"}]},{"given":"Hongyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"1 Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, V5A1S6, Canada and 2Department of Epidemiology and Public Health, Yale University, School of Medicine, 60 College Street, New Haven, CT 06520-8034, USA"}]}],"member":"286","published-online":{"date-parts":[[2008,5,27]]},"reference":[{"key":"2023020210482932700_B1","article-title":"An introduction to Systems Biology","author":"Alon","year":"2007"},{"key":"2023020210482932700_B2","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1038\/43199","article-title":"Robustness in simple biochemical networks","volume":"387","author":"Barkai","year":"1997","journal-title":"Nature"},{"key":"2023020210482932700_B3","doi-asserted-by":"crossref","DOI":"10.1002\/9780470316757","volume-title":"Nonlinear Regression Analysis and its Applications","author":"Bates","year":"1988"},{"key":"2023020210482932700_B4","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1007\/978-3-642-68220-9_8","article-title":"Numerical treatment of inverse problems in chemical reaction kinetics","volume-title":"Modelling of Chemical Reaction Systems","author":"Bock","year":"1981"},{"key":"2023020210482932700_B5","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s00180-007-0044-1","article-title":"Parameter cascades and profiling in functiona data analysis","volume":"22","author":"Cao","year":"2007","journal-title":"Comput. 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