{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T10:33:12Z","timestamp":1768473192527,"version":"3.49.0"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":1682,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/3.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,4,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Systems biology employs mathematical modelling to further our understanding of biochemical pathways. Since the amount of experimental data on which the models are parameterized is often limited, these models exhibit large uncertainty in both parameters and predictions. Statistical methods can be used to select experiments that will reduce such uncertainty in an optimal manner. However, existing methods for optimal experiment design (OED) rely on assumptions that are inappropriate when data are scarce considering model complexity.<\/jats:p>\n               <jats:p>Results: We have developed a novel method to perform OED for models that cope with large parameter uncertainty. We employ a Bayesian approach involving importance sampling of the posterior predictive distribution to predict the efficacy of a new measurement at reducing the uncertainty of a selected prediction. We demonstrate the method by applying it to a case where we show that specific combinations of experiments result in more precise predictions.<\/jats:p>\n               <jats:p>Availability and implementation: Source code is available at: http:\/\/bmi.bmt.tue.nl\/sysbio\/software\/pua.html<\/jats:p>\n               <jats:p>Contact: \u00a0j.vanlier@tue.nl; N.A.W.v.Riel@tue.nl<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/bts092","type":"journal-article","created":{"date-parts":[[2012,2,25]],"date-time":"2012-02-25T03:45:12Z","timestamp":1330141512000},"page":"1136-1142","source":"Crossref","is-referenced-by-count":86,"title":["A Bayesian approach to targeted experiment design"],"prefix":"10.1093","volume":"28","author":[{"given":"J.","family":"Vanlier","sequence":"first","affiliation":[]},{"given":"C. A.","family":"Tiemann","sequence":"additional","affiliation":[]},{"given":"P. A. J.","family":"Hilbers","sequence":"additional","affiliation":[]},{"given":"N. A. W.","family":"van Riel","sequence":"additional","affiliation":[]}],"member":"286","published-online":{"date-parts":[[2012,2,24]]},"reference":[{"key":"2023012711533834400_B1","doi-asserted-by":"crossref","first-page":"20171","DOI":"10.1074\/jbc.M110.106849","article-title":"Mass and information feedbacks through receptor endocytosis govern insulin signaling as revealed using a parameter-free modeling framework","volume":"285","author":"Br\u00e4nnmark","year":"2010","journal-title":"J. Biol. Chem."},{"key":"2023012711533834400_B2","doi-asserted-by":"crossref","first-page":"021904","DOI":"10.1103\/PhysRevE.68.021904","article-title":"Statistical mechanical approaches to models with many poorly known parameters","volume":"68","author":"Brown","year":"2003","journal-title":"Phys. Rev. E"},{"key":"2023012711533834400_B3","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1098\/rsfs.2011.0051","article-title":"Statistical analysis of nonlinear dynamical systems using differential geometric sampling methods","volume":"1","author":"Calderhead","year":"2011","journal-title":"J. R. Soc. Interface Focus"},{"key":"2023012711533834400_B4","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1049\/iet-syb:20060065","article-title":"Optimal experimental design in an epidermal growth factor receptor signalling and down-regulation model","volume":"1","author":"Casey","year":"2007","journal-title":"Syst. Biol. IET"},{"key":"2023012711533834400_B5","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1111\/j.1742-4658.2008.06845.x","article-title":"Systems biology: model based evaluation and comparison of potential explanations for given biological data","volume":"276","author":"Cedersund","year":"2009","journal-title":"FEBS J."},{"key":"2023012711533834400_B6","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1080\/01621459.1996.10476956","article-title":"Markov chain Monte Carlo convergence diagnostics: a comparative review","volume":"91","author":"Cowles","year":"1996","journal-title":"J. Am. Stat. Assoc."},{"key":"2023012711533834400_B7","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1111\/j.1467-9868.2006.00553.x","article-title":"Sequential monte carlo samplers","volume":"68","author":"Del Moral","year":"2006","journal-title":"J. Roy. Stat. Soc. B"},{"key":"2023012711533834400_B8","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1177\/0037549703040937","article-title":"Simulation methods for optimal experimental design in systems biology","volume":"79","author":"Faller","year":"2003","journal-title":"Simulation"},{"key":"2023012711533834400_B9","first-page":"473","article-title":"Practical markov chain monte carlo","volume":"7","author":"Geyer","year":"1992","journal-title":"Stat. Sci."},{"key":"2023012711533834400_B10","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1111\/j.1467-9868.2010.00765.x","article-title":"Riemann manifold Langevin and Hamiltonian Monte Carlo methods","volume":"73","author":"Girolami","year":"2011","journal-title":"J. Roy. Stat. Soc. B"},{"key":"2023012711533834400_B11","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1098\/rsfs.2011.0015","article-title":"Workflow for generating competing hypothesis from models with parameter uncertainty","volume":"1","author":"Gomez-Cabrero","year":"2011","journal-title":"J. R. Soc. Interface Focus"},{"key":"2023012711533834400_B12","doi-asserted-by":"crossref","first-page":"e189","DOI":"10.1371\/journal.pcbi.0030189","article-title":"Universally sloppy parameter sensitivities in systems biology models","volume":"3","author":"Gutenkunst","year":"2007","journal-title":"PLoS Comput. Biol."},{"key":"2023012711533834400_B13","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1049\/iet-syb.2009.0030","article-title":"Parameter identification, experimental design and model falsification for biological network models using semidefinite programming","volume":"4","author":"Hasenauer","year":"2010","journal-title":"Syst. Biol. IET"},{"key":"2023012711533834400_B14","doi-asserted-by":"crossref","first-page":"3067","DOI":"10.1093\/bioinformatics\/btl485","article-title":"Copasia complex pathway simulator","volume":"22","author":"Hoops","year":"2006","journal-title":"Bioinformatics"},{"key":"2023012711533834400_B15","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1186\/1471-2105-10-371","article-title":"An empirical Bayesian approach for model-based inference of cellular signaling networks","volume":"10","author":"Klinke","year":"2009","journal-title":"BMC Bioinformatics"},{"key":"2023012711533834400_B16","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1111\/j.1742-4658.2008.06843.x","article-title":"Systems biology: experimental design","volume":"276","author":"Kreutz","year":"2009","journal-title":"FEBS J."},{"key":"2023012711533834400_B17","doi-asserted-by":"crossref","first-page":"2747","DOI":"10.1093\/bioinformatics\/btm397","article-title":"An error model for protein quantification","volume":"23","author":"Kreutz","year":"2007","journal-title":"Bioinformatics"},{"key":"2023012711533834400_B18","author":"Kreutz","year":"2011","journal-title":"Likelihood based observability analysis and confidence intervals for predictions of dynamic models."},{"key":"2023012711533834400_B19","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1093\/bioinformatics\/btq278","article-title":"ABC-SysBio approximate Bayesian computation in Python with GPU support","volume":"26","author":"Liepe","year":"2010","journal-title":"Bioinformatics"},{"key":"2023012711533834400_B20","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1093\/bioinformatics\/btn350","article-title":"Dynamical modeling and multi-experiment fitting with potterswheel","volume":"24","author":"Maiwald","year":"2008","journal-title":"Bioinformatics"},{"key":"2023012711533834400_B21","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/BF00143556","article-title":"Sampling from multimodal distributions using tempered transitions","volume":"6","author":"Neal","year":"1996","journal-title":"Stat. Comput."},{"key":"2023012711533834400_B22","doi-asserted-by":"crossref","first-page":"1923","DOI":"10.1093\/bioinformatics\/btp358","article-title":"Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood","volume":"25","author":"Raue","year":"2009","journal-title":"Bioinformatics"},{"key":"2023012711533834400_B23","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.biosystems.2005.06.016","article-title":"A hybrid approach for efficient and robust parameter estimation in biochemical pathways","volume":"83","author":"Rodriguez-Fernandez","year":"2006","journal-title":"Biosystems"},{"key":"2023012711533834400_B24","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1093\/bioinformatics\/btq074","article-title":"An optimal experimental design approach to model discrimination in dynamic biochemical systems","volume":"26","author":"Skanda","year":"2010","journal-title":"Bioinformatics"},{"key":"2023012711533834400_B25","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1073\/pnas.0237333100","article-title":"Identification of nucleocytoplasmic cycling as a remote sensor in cellular signaling by databased modeling","volume":"100","author":"Swameye","year":"2003","journal-title":"Proc. Natl Acad. Sci."},{"key":"2023012711533834400_B26","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1186\/1752-0509-5-174","article-title":"Parameter adaptations during phenotype transitions in progressive diseases","volume":"5","author":"Tiemann","year":"2011","journal-title":"BMC Syst. Biol."},{"key":"2023012711533834400_B27","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1098\/rsif.2008.0172","article-title":"Approximate bayesian computation scheme for parameter inference and model selection in dynamical systems","volume":"6","author":"Toni","year":"2009","journal-title":"J. Roy. Soc. Interface"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/28\/8\/1136\/48930394\/bioinformatics_28_8_1136.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/28\/8\/1136\/48930394\/bioinformatics_28_8_1136.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T12:25:37Z","timestamp":1674822337000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/28\/8\/1136\/195497"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,2,24]]},"references-count":27,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2012,4,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bts092","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2012,4]]},"published":{"date-parts":[[2012,2,24]]}}}