Joshua Claypool, PhD Candidate, Biological Systems Engineering, University of California, Davis
Understanding microbial community dynamics in high‐solids lignocellulolytic systems using bioinformatics tools
High‐solids lignocellulosic systems are prevalent throughout many agricultural systems and are relevant to next generation biofuel production. Deconstructing the lignocellulosic material is often accomplished by an intricate set of enzymes expressed by a microbial community. These microbial communities are potentially comprised of thousands of microorganisms each with their own deconstructive enzymes and niches. Understanding how the microorganisms within the microbial community are interacting can form the basis for improving the deconstruction process. Network analysis is one approach to characterize and quantify these interactions. In this work, we studied the deconstruction process of a model lignocellulosic food‐processing waste, tomato pomace, under high‐solids conditions relevant to industrial bioprocessing and agricultural soil systems. Additionally, biosolarization was studied as system to utilize high solids microbial degradation of tomato pomace in soil. Network analysis was performed to determine microbial interactions in biosolarized soils and identify subcommunities that may be responsible for biomass deconstruction and VFA fermentation. Using an artificial
neural network trained on data from bioreactors and soil microcosms capable of deconstructing tomato pomace and other biomass, the structures of soil sub‐communities were predicted in biosolarized soils. These results highlight the possibility for predicting the structure of lignocellulolytic microbial communities based on environmental variables that may ultimately be used to optimize the deconstructive and fermentative activities of these communities.
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