(317d) Simultaneous Synthesis of Metabolic and Process Engineering for the Production of Muconic Acid

Recent advances in metabolic pathway reconstruction and in silico modelling have enabled the development of microbial strains suitable for efficient cell-factories. Emerging infrastructures of Design-Build-Test-Learn paradigms hold a tremendous promise to innovate with new products, also novel integrated processes that can be intensified for the highest efficiency¹. Still, the current Design-Build-Test-Learn (DBTL) cycle approach in biofoundries and biorefineries remains a disconnected sequence of tasks with an undisclosed potential to integrate. Specifically, the genetic modifications of strains are usually driven by the overproduction of target metabolites without considering downstream processing which can account for up to 80% of the overall bio-production cost². Therefore, there is a need for an inclusive methodical approach that incorporates separation processes in metabolic engineering upstream design.

The paper presents a systems integration approach for the computational strain design workflow for the identification of reaction eliminations that reshape network connectivity in way that both biomass production and revenue are simultaneously maximized by utilizing a bilevel optimization framework. The research expands the outer problem of the bilevel approach that is established in the literature with the use of a superstructure scheme that addresses several design options simultaneously with the several options to select the pathways. The superstructure scheme is laid out to reduce model and optimization complexities. The existence of each process is denoted by binary variables. Our method entails the identification and the categorization of these technologies as well as the insertion of the model equations and their economic parameters into the optimization problem. Separation steps include pretreatment, cell removal, product isolation, concentration, purification and refinement. The superstructure’s input stream is a variable determined by the optimization problem and is separated into five components: product, liquid by-product, water, cells, and solid by-product. Each process redistributes the stream’s components through linearized model equations. The problem can be constrained based on the specific bio-process characteristics and objectives. The model has been developed in GAMS environment and solved using the BARON global optimization solver.

We assess the effect of the new optimization goal, for varying number of metabolic interventions to the downstream separation network and the bioprocess revenue. We then compare the enriched model to the previous analysis, which only aims to maximize the production of a target metabolite. To showcase the functionality and effectiveness of the developed model we applied the workflow to a muconic acid producing strain of S.cerevisiae (iMM904 GEM) that includes the necessary heterologous pathways. Additionally, by utilizing different metabolic engineering tools, it is possible to acquire a variety of promising metabolic interventions that could lead to profitable biorefineries. Overall, this computational framework could be an important step to bridge the gap between strain design and process engineering.

References:

¹ IBISBA, www.ibisba.eu

² Kiss, A. A., & Rito-palomares, M. (2014). A systems engineering perspective on process integration in industrial biotechnology. October. https://doi.org/10.1002/jctb.4584