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Mathematical modeling of population structure in bioreactors seeded with light-controllable microbial stem cells.
Patey, Dane; Mushnikov, Nikolai; Bowman, Grant; Liu, Rongsong.
Afiliação
  • Patey D; Department of Mathematics and Statisitics, University of Wyoming, 1000 E. University, Laramie, WY 82071, USA.
  • Mushnikov N; Department of Molecular Biology, University of Wyoming, 1000 E. University, Laramie, WY 82071, USA.
  • Bowman G; Department of Molecular Biology, University of Wyoming, 1000 E. University, Laramie, WY 82071, USA.
  • Liu R; Department of Mathematics and Statisitics, University of Wyoming, 1000 E. University, Laramie, WY 82071, USA.
Math Biosci Eng ; 17(6): 8182-8201, 2020 11 13.
Article em En | MEDLINE | ID: mdl-33378939
ABSTRACT
Industrial bioreactors use microbial organisms as living factories to produce a wide range of commercial products. For most applications, yields eventually become limited by the proliferation of "escape mutants" that acquire a growth advantage by losing the ability to make product. The goal of this work is to use mathematical models to determine whether this problem could be addressed in continuous flow bioreactors that include a "stem cell" population that multiplies rapidly and could be used to compete against the emergence of cheater mutants. In this system, external stimuli can be used to induce stem cell multiplication through symmetric cell division, or to limit stem cell multiplication and induce higher production through an asymmetric cell division that produces one stem cell and one new product-producing "factory cell". Our results show product yields from bioreactors with microbial stem cells can be increased by 18% to 127% over conventional methods, and sensitivity analysis shows that yields could be improved over a broad range of parameter space.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Reatores Biológicos / Modelos Teóricos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Reatores Biológicos / Modelos Teóricos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article