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Population collapse and adaptive rescue during long-term chemostat fermentation.
Rai, Navneet; Huynh, Linh; Kim, Minseung; Tagkopoulos, Ilias.
Afiliação
  • Rai N; UC Davis Genome Center, University of California, Davis, California.
  • Huynh L; Department of Computer Science, University of California, Davis, California.
  • Kim M; UC Davis Genome Center, University of California, Davis, California.
  • Tagkopoulos I; Department of Computer Science, University of California, Davis, California.
Biotechnol Bioeng ; 116(3): 693-703, 2019 03.
Article em En | MEDLINE | ID: mdl-30536368
ABSTRACT
Microbial fermentation is an essential process for research and industrial applications, yet our understanding of cellular dynamics during long-term fermentation is limited. Here, we report a reproducible phenomenon of abrupt population collapse followed by a rapid population rescue that was observed during long-term chemostat cultivations, for various strains of Escherichia coli in minimal media. Through genome resequencing and whole-genome transcriptional profiling of replicate runs over time, we identified that changes in the tRNA and carbon catabolic genes are the genetic basis of this phenomenon. Since current fermentation models are unable to capture the observed dynamics, we present an extended model that takes into account critical biological processes during fermentation, and we further validated carbon source predictions through forward experimentation. This study extends the predictability of current models for microbial fermentation and adds to our system-level knowledge of cellular adaptation during this crucial biotechnological process.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biotecnologia / Perfilação da Expressão Gênica / Fermentação / Modelos Biológicos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biotecnologia / Perfilação da Expressão Gênica / Fermentação / Modelos Biológicos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article