{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T18:11:51Z","timestamp":1781806311003,"version":"3.54.5"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>\n                      The growing demand for renewable energy together with the environmental impact of fossil fuels, have intensified global interest in sustainable bioethanol production.\n                      <jats:italic>Saccharomyces cerevisiae<\/jats:italic>\n                      is the preferred microorganism for industrial fermentation due to its productivity and stress tolerance, but cumulative stress during successive cycles reduces process efficiency. Therefore, selecting stress-tolerant strains capable of adapting to fluctuating conditions is crucial. Predictive microbiology (PM), which applies mathematical models to predict and quantify microbial responses to environmental factors, remains a valuable but still limited approach in yeast-based bioethanol production.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      In this study, diverse PM models were applied to evaluate the effects of temperature, pH, sucrose, and ethanol concentrations on\n                      <jats:italic>S. cerevisiae<\/jats:italic>\n                      strains isolated from industrial fermentations in Tucum\u00e1n, Argentina. Thirteen native isolates were compared with a commercial reference strain (Calsa).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The primary and secondary models used achieved excellent fits in all cases (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      &amp;gt; 0.9), effectively describing and anticipating growth responses under stress conditions relevant to industrial fermentations of the evaluated strains, which displayed wide tolerance ranges suggesting potential suitability for cell recycling and high-density fermentation, pending validation under fermentation conditions. Strains T415, Le384, LF84, and SR350 emerged as promising candidates for further validation in industrial bioethanol fermentations to improve the stability and sustainability of industrial bioethanol production, matching or outperforming the control strain.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Overall, this study underscores the usefulness of PM tools for characterizing and selecting native yeast strains with enhanced stress tolerance in local bioethanol production, as they allow for anticipation of physiological behavior in growth-based assays that mimic key industrial stresses, reduction of experimental workload, and strengthen strain selection and evaluation criteria.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmicb.2026.1783848","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T06:57:28Z","timestamp":1773817048000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Selection of highly stress-tolerant yeast strains relevant for bioethanol fermentation using predictive growth models"],"prefix":"10.3389","volume":"17","author":[{"given":"Mar\u00eda Alejandra","family":"Canseco Grellet","sequence":"first","affiliation":[{"name":"Laboratorio de Microbiolog\u00eda, Estaci\u00f3n Experimental Agroindustrial Obispo Colombres (EEAOC)","place":["Las Talitas, Argentina"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joaqu\u00edn","family":"Bautista-Gallego","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias, Universidad de Extremadura (UEx)","place":["Badajoz, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mar\u00eda Francisca","family":"Perera","sequence":"additional","affiliation":[{"name":"Instituto de Tecnolog\u00eda Agroindustrial del Noroeste Argentino (ITANOA), Estaci\u00f3n Experimental Agroindustrial Obispo Colombres (EEAOC) \u2014Consejo Nacional de Investigaciones Cient\u00edficas y T\u00e9cnicas (CONICET)","place":["Las Talitas, Argentina"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karina In\u00e9s","family":"Dantur","sequence":"additional","affiliation":[{"name":"Instituto de Tecnolog\u00eda Agroindustrial del Noroeste Argentino (ITANOA), Estaci\u00f3n Experimental Agroindustrial Obispo Colombres (EEAOC) \u2014Consejo Nacional de Investigaciones Cient\u00edficas y T\u00e9cnicas (CONICET)","place":["Las Talitas, Argentina"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberto Marcelo","family":"Ruiz","sequence":"additional","affiliation":[{"name":"Laboratorio de Microbiolog\u00eda, Estaci\u00f3n Experimental Agroindustrial Obispo Colombres (EEAOC)","place":["Las Talitas, Argentina"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco No\u00e9","family":"Arroyo-L\u00f3pez","sequence":"additional","affiliation":[{"name":"Departamento de Biotecnolog\u00eda de Alimentos, Instituto de la Grasa (CSIC), Campus Universitario Pablo de Olavide","place":["Seville, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","DOI":"10.1002\/9781119962045.ch19","article-title":"Role of predictive microbiology in food preservation","volume-title":"Progress in Food Preservation","author":"Arroyo-L\u00f3pez","year":"2012"},{"key":"B2","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1007\/s10295-009-0535-x","article-title":"Application of a substrate inhibition model to estimate the effect of fructose concentration on the growth of diverse Saccharomyces cerevisiae strains.","volume":"36","author":"Arroyo-L\u00f3pez","year":"2009","journal-title":"J. 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