Automated Systems of Plasmid-Based Functional Proteomics to Improve Microbes for Biofuel Production

Part of the Microbiology Monographs book series (MICROMONO, volume 22)


Plasmid-based functional proteomics is an important technology for rapidly obtaining large quantities of protein and determining protein function across an entire genome. It centers on production of full-length cDNA libraries as a source of plasmid-based clones to express the desired proteins in active form to determine their function. Because plasmid libraries are composed of several thousand unique genes, automation of the process is essential. High-throughput platforms that can rapidly clone and express heterologous gene open reading frames (ORFs) in bacteria and yeast and can screen large numbers of expressed proteins for optimized function are important for improving microbial strains for biofuel production. Combined with rapid gene assembly and mutagenesis strategies, gene ORFs can be synthesized, cloned, transformed into yeast strains, and screened to identify those that will give increased ethanol production, allow coproduction of biodiesel, enable use of biomass as a feedstock, and express valuable coproducts. The approach for the past 10 years has been to overexpress proteins that enable microbes to perform functions allowing improved production of biofuels. The next step will be to generate stable strains containing the genes that overexpress these proteins. This will need to be coupled with technologies such as Western blot analysis, high-throughput microscopy, mass spectrometry, gas chromatography, Raman spectroscopy, and microarray analysis to identify critical pathways and metabolites. These techniques adapted to an automated systems biology platform will allow tailoring of microbial strains to use renewable feedstocks for production of biofuels, bioderived chemicals, fertilizers, and other coproducts for profitable and sustainable biorefineries.


Ethanol Production Robotic Platform Xylose Isomerase Fatty Acid Ethyl Ester Gene ORFs 
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-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.United States Department of AgricultureAgricultural Research Service, National Center for Agricultural Utilization Research, Renewable Product Technology Research UnitPeoriaUSA
  2. 2.LifeSensors, Inc.MalvernUSA
  3. 3.Robotic Software, Robotic Vision, and Robotic Scheduling UnitsBartolett Enterprises, Inc.BridgewaterUSA
  4. 4.Laboratory Workcell Robotics and Integration Design DivisionBlackland Manufacturing, Inc.Round RockUSA

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