{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T13:30:07Z","timestamp":1768743007735,"version":"3.49.0"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"13","license":[{"start":{"date-parts":[[2018,6,27]],"date-time":"2018-06-27T00:00:00Z","timestamp":1530057600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020","award":["686282"],"award-info":[{"award-number":["686282"]}]},{"name":"Post-doctoral Fellowship Program"},{"DOI":"10.13039\/100003270","name":"PFP","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100003270","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013295","name":"Helmholtz Zentrum M\u00fcnchen","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100013295","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Mathematical models have become standard tools for the investigation of cellular processes and the unraveling of signal processing mechanisms. The parameters of these models are usually derived from the available data using optimization and sampling methods. However, the efficiency of these methods is limited by the properties of the mathematical model, e.g. non-identifiabilities, and the resulting posterior distribution. In particular, multi-modal distributions with long valleys or pronounced tails are difficult to optimize and sample. Thus, the developement or improvement of optimization and sampling methods is subject to ongoing research.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We suggest a region-based adaptive parallel tempering algorithm which adapts to the problem-specific posterior distributions, i.e. modes and valleys. The algorithm combines several established algorithms to overcome their individual shortcomings and to improve sampling efficiency. We assessed its properties for established benchmark problems and two ordinary differential equation models of biochemical reaction networks. The proposed algorithm outperformed state-of-the-art methods in terms of calculation efficiency and mixing. Since the algorithm does not rely on a specific problem structure, but adapts to the posterior distribution, it is suitable for a variety of model classes.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The code is available both as Supplementary Material and in a Git repository written in MATLAB.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty229","type":"journal-article","created":{"date-parts":[[2018,4,12]],"date-time":"2018-04-12T19:32:51Z","timestamp":1523561571000},"page":"i494-i501","source":"Crossref","is-referenced-by-count":18,"title":["Bayesian parameter estimation for biochemical reaction networks using region-based adaptive parallel tempering"],"prefix":"10.1093","volume":"34","author":[{"given":"Benjamin","family":"Ballnus","sequence":"first","affiliation":[{"name":"Institute of Computational Biology, Helmholtz Zentrum M\u00fcnchen\u2013German Research Center for Environmental Health, Neuherberg, Germany"},{"name":"Technische Universit\u00e4t M\u00fcnchen, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Garching, Germany"}]},{"given":"Steffen","family":"Schaper","sequence":"additional","affiliation":[{"name":"Bayer AG, Engineering and Technologies, Applied Mathematics, Leverkusen, Germany"}]},{"given":"Fabian J","family":"Theis","sequence":"additional","affiliation":[{"name":"Institute of Computational Biology, Helmholtz Zentrum M\u00fcnchen\u2013German Research Center for Environmental Health, Neuherberg, Germany"},{"name":"Technische Universit\u00e4t M\u00fcnchen, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Garching, Germany"}]},{"given":"Jan","family":"Hasenauer","sequence":"additional","affiliation":[{"name":"Institute of Computational Biology, Helmholtz Zentrum M\u00fcnchen\u2013German Research Center for Environmental Health, Neuherberg, Germany"},{"name":"Technische Universit\u00e4t M\u00fcnchen, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Garching, Germany"}]}],"member":"286","published-online":{"date-parts":[[2018,6,27]]},"reference":[{"key":"2023051605125938700_bty229-B1","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s11222-008-9110-y","article-title":"A tutorial on adaptive MCMC","volume":"18","author":"Andrieu","year":"2008","journal-title":"Stat. 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