{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T22:13:24Z","timestamp":1791584004704,"version":"4.3.4"},"reference-count":53,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T00:00:00Z","timestamp":1731024000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Biotech &amp; Bioengineering"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Mathematical modeling and computer simulation are fundamental for optimizing biotechnological processes, enabling cost reduction and scalability, thereby driving advancements in the bioindustry. In this work, mathematical modeling and estimation of fermentative kinetic parameters were carried out to produce 1,3\u2010propanediol (1,3\u2010PDO) from residual glycerol and\n                    <jats:italic>Klebsiella pneumoniae<\/jats:italic>\n                    BLh\u20101. The Markov chain Monte Carlo method, using the Metropolis\u2010Hastings algorithm, was applied to experimental data from a batch bioreactor under aerobic and anaerobic conditions. Sensitivity analysis and parameter evolution studies were conducted. The root\u2010mean\u2010square error (rRMSE) was chosen as the validation and calibration metric for the developed mathematical model. The results indicated that the average tolerance of glycerol was 174.68 and 44.85\u2009g\u2009L\n                    <jats:sup>\u22121<\/jats:sup>\n                    , the inhibitory products was 150.95\u2009g\u2009L\n                    <jats:sup>\u22121<\/jats:sup>\n                    for ethanol and 35.56\u2009g\u2009L\n                    <jats:sup>\u22121<\/jats:sup>\n                    for 1,3\u2010PDO, and the maximum specific rate of cell growth was 0.189 and 0.275\u2009h\n                    <jats:sup>\u22121<\/jats:sup>\n                    , for aerobic and anaerobic cultures, respectively. The model presented excellent fits in both crops, with rRMSE values between 0.09 \u2212 33.74% and 3.58 \u2212 31.82%, for the aerobic and anaerobic environment, respectively. With this, it was possible to evaluate and extract relevant information for a better understanding and control of the bioprocess.\n                  <\/jats:p>","DOI":"10.1002\/bit.28883","type":"journal-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T06:49:42Z","timestamp":1731048582000},"page":"333-343","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Mathematical Modeling and Simulation of 1,3\u2010Propanediol Production by\n                    <i>Klebsiella pneumoniae<\/i>\n                    BLh\u20101 in a Batch Bioreactor Using Bayesian Statistics"],"prefix":"10.1002","volume":"122","author":[{"given":"Nathalia Lobato","family":"Moraes","sequence":"first","affiliation":[{"name":"Faculty of Biotechnology Federal University of Par\u00e1 Bel\u00e9m Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mailson Batista","family":"de Vilhena","sequence":"additional","affiliation":[{"name":"Department of Production Engineering State University of Amap\u00e1\u2010UEAP Macap\u00e1 Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniele Misturini","family":"Rossi","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering Federal University of Rio Grande do Sul Porto Alegre Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2768-652X","authenticated-orcid":false,"given":"Bruno Marques","family":"Viegas","sequence":"additional","affiliation":[{"name":"Faculty of Biotechnology Federal University of Par\u00e1 Bel\u00e9m 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