Biobased and CO2 free production of platform chemicals using substrate multiplexing
The transition to a sustainable, circular economy will require the use of renewable feedstocks and bio-based processes. Engineered microorganisms using renewable feedstocks like sugars from agricultural waste or other renewable carbon sources can operate at low process temperatures and no toxic waste is generated. Currently, industrial microorganisms use mainly sugar based feedstocks; while microbes do grow efficiently on sugars, sugars are not optimal for the production of platform chemicals with a high degree of reduction. For many reduced chemicals, there is a low energy-to-carbon ratio or inefficient metabolic pathways starting from sugars. Consequently, only low carbon yields can be obtained and there are significant CO2 emissions.
The combination of different feedstocks with complementary properties could overcome this hurdle. Methane obtained from the anaerobic breakdown of biomass (biogas) is abundant, and methane is the C1 molecule with the highest degree of reduction. These characteristics render methane and its derivatives such as methanol as great candidates for combination with sugar feedstock to enable full carbon and energy efficiency and zero CO2 emissions.
To make such zero-CO2 process possible, we combine:
In silico design: We use models including all metabolic pathways involved in the incorporation of sugars and methanol to evaluate all possible feedstock combinations. Using different algorithms such as Flux Balance Analysis (FBA) or Min-Max Driving Force (MDF), we evaluate the different pathway combinations including their yields, carbon and energy efficiency and thermodynamic feasibility. These simulations can be performed in a matter of minutes, instead of years required for experimental testing.
Experimental validation: The best pathway combinations will be implemented in E. coli or C. glutamicum and the strains are cultivated in bioreactors ranging from 0.5-2 L under different static and dynamic feeding strategies. The performance is evaluated based on both extracellular and intracellular (metabolome) metabolites and rates. These are measured using HPLC, GC and HPLC-MSMS. We also analyse the changes in protein expression induced by metabolic engineering and changes in culture conditions. Together, we generate massive datasets for a time series of the metabolome and the proteome of the cell.
Analysis, learning & refined design: We will integrate all this heterogeneous data and feed them back to a model of the whole organism. This will deliver a comprehensive model of both the cell and the production process which will allow us to understand the dynamics and behaviour of the cells under different conditions, identify bottlenecks, and further optimize production. and achieve maximum product yield and process efficiency.
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