dc.description.abstract |
Fossil fuels have the finite reservation, which necessitated developing renewable energy sources. Bioenergy became one of powerful renewable energy sources with recent advances in synthetic biology encompassing systems biology and metabolic engineering. This enable us to engineer and/or create tailor made microorganisms to produce alternative biofuels or biomaterials for the future bio-era. The efficient transformation of biomass to bioenergy/biomaterial requires maximum performance of cellular metabolism to be designed and engineered. Toward this end, investigation of bacterial metabolism with in silico bacteria with systems biology became one of powerful tools. To investigate new microorganism for metabolic engineering, having detailed genome annotation and knowledge on transcriptional regulatory network of metabolism is cruicial. To assist with finding possible transcription factor (TF) candidates, machine learning-based pipeline, which is coupled with experimental validation, was proposed and was used for E. coli K-12 MG1655, resulting in confirming its validity, and confirmation of multiple less-characterized TFs including yieP. In another study, yieP was found to involved in resistance mechanisms against 3-hydroxypropionate (3-HP), which is one of very promising platform chemical to produce value-added materials. Integration of genome-scale experimental measurements with RNA-seq and ChIP-exo for yieP resulted in a number of target gene candidates. Further functional analysis found yohJK, another less-studied transmembrane protein, confer 3-HP specific resistance in multiple E. coli. Thus, systems biology approaches with genome-wide experiments and in silico analysis combined can contribute in understanding cellcular mechanism of microorganisms and in engineering them to produce chemicals for value-added biomaterials. |
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