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Tolerance of Microbial Biocatalysts to Feedstocks, Products, and Environmental Conditions

  • Mian Huang
  • George Peabody
  • Katy C. Kao
Chapter

Abstract

Bioreactor conditions and environmental stressors present during fermentation can negatively impact the productivity of industrial biocatalysts. Robustness of biocatalysts in fermentation conditions is thus important for the economical viability of bio-based production. Temperature, pH, and osmotic pressure inside the bioreactor are often not optimal for cell growth. Feedstocks (particularly sustainably sourced) and products (desired or side) often contain toxic components that further reduce biocatalyst performance. The physiological effects of many industrially relevant environmental stressors have been studied extensively. However, due to the complexity of cellular processes and the significant knowledge gap in genotype-phenotype relationships associated with these complex phenotypes, the rational engineering of robust biocatalysts is currently limited. Traditional strain developments rely on random approaches, and have been successful at generating more robust biocatalysts. Random approaches combined with new genomic technologies will start to fill the genotype-phenotype knowledge gap, making the rational engineering of robust biocatalysts for industrial applications more readily achievable. This chapter will focus on the common environmental stressors present in industrial fermentation; the stressors will be divided into three sections: feedstock toxicity, fermentation conditions, and product toxicity. Each section will describe the known mechanisms of toxicity associated with each stressor followed by examples of successful development of strains with enhanced tolerance, with a focus on the tools used, and discussions of the known molecular mechanisms associated with tolerance.

Keywords

Glycine Betaine Levulinic Acid Hyperosmotic Stress Lignocellulosic Hydrolysate Genome Shuffling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Chemical EngineeringTexas A&M UniversityCollege StationUSA

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