Strain engineering for efficient conversion of lignocellulosic streams with tolerance to high carbon levels and feedstock impurities
Argonne National Laboratory
Description
This capability enables industrial microbes to covert efficient biomass feedstocks, regardless of quality and variability, using optimized low-temperature conversion processes (both sugar and lignin pathways). Through teamed experimental/ modeling efforts –a validated modeling framework allows stakeholders to minimize the impacts of variation in feedstocks (unique compositions of purpose-grown biomass and their mixtures) to reduce time and resources needed to achieve economic performance at scale. This is accomplished through integrated, model-driven approach that coordinates media modifications and host engineering.
Capability Bounds
Initial tests to validate the performance of optimized processes and engineered strains will be at the 1-ml (initial survey of large parameter spaces of media formulations and engineered strains). 1-L bioreactors will be used selectively to test the scalability of limited number of top-performing combinations of media and strains. Pilot evaluations of a select few will be through a 100-gallon bioreactor available at the MERF (through collaborations with Meltem Urgun Demirtas).
Unique Aspects
High-throughput and miniaturized methods and evaluation protocols lead to efficient survey of large parameter space for both media formulations and genetic modifications that quickly provides a path towards further optimization of feedstock-resilient processes and strains.
Availability
This approach has been utilized for optimization of a range microbial biomanufacturing processes utilizing sugar and lignin streams from agricultural residues. This approach can be readily adapted to new processes and feedstock streams by building on existing modeling and engineering strategies, quickly overcoming barriers that are typically encountered.
Benefit
Through efficient transfer of knowledge and methodologies, biomanufacturing processes can be optimized and made flexible towards conversion of novel feedstock inputs in reduced research and development times.
Capability Expert(s)
Philip Laible, Gyorgy Babnigg, Rosemarie Wilton, and Dion Antonopoulos
