Growth-coupled overproduction is feasible for almost all metabolites in five major production organisms

Kamp, Axel von; Klamt, Steffen

doi:10.1038/ncomms15956

Cited by 120 publications

(141 citation statements)

References 47 publications

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“…In our study, we assumed that product synthesis is coupled with growth (OSF) and with ATP synthesis (OSF and second stage of TSF), a scenario relevant and feasible for many production processes . However, coupling might not always be possible or desirable.…”

Section: Discussionmentioning

confidence: 99%

When Do Two‐Stage Processes Outperform One‐Stage Processes?

Klamt

Mahadevan

Hädicke

2017

Biotechnology Journal

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Apart from product yield and titer, volumetric productivity is a key performance indicator for many biotechnological processes. Due to the inherent trade-off between the production of biomass as catalyst and of the actual target product, yield and volumetric productivity cannot be optimized simultaneously. Therefore, in combination with genetic techniques for dynamic regulation of metabolic fluxes, two-stage fermentations (TSFs) with separated growth and production phase have recently gained much interest because of their potential to improve the productivity of bioprocesses while still allowing high product yields. However, despite some successful case studies, so far it has not been discussed and analyzed systematically whether or under which conditions a TSF guarantees superior productivity compared to one-stage fermentation (OSF). In this study, we use mathematical models to demonstrate that the volumetric productivity of a TSF is not automatically better than of a corresponding OSF. Our analysis reveals that the sharp decrease of the specific substrate uptake rate usually observed in (non-growth) production phases severely impacts the volumetric productivity and thus raises a big challenge for designing competitive TSF processes. We discuss possible approaches such as enforced ATP wasting to improve substrate utilization rates in the production phase by which TSF processes can become superior to OSF. We also analyze additional factors influencing the relative performance of OSF and TSF and show that OSF processes can be more appropriate if a high product yield is an economic constraint. In conclusion, a careful assessment of the trade-offs between substrate uptake rates, yields, and productivity is necessary when deciding for OSF vs. TSF processes.

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Section: Discussionmentioning

confidence: 99%

When Do Two‐Stage Processes Outperform One‐Stage Processes?

Klamt

Mahadevan

Hädicke

2017

Biotechnology Journal

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“…When using the genome-scale model iML1515 to simulate wGCP designs, only the commonly observed fermentative products (acetate, CO 2 , ethanol, formate, lactate, succinate) were allowed for secretion as described elsewhere. 35…”

Section: Methodsmentioning

confidence: 99%

Harnessing natural modularity of cellular metabolism to design a modular chassis cell for a diverse class of products by using goal attainment optimization

Garcia¹,

Trinh²

2019

Preprint

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13Living cells optimize their fitness against constantly changing environments to sur-14 vive. Goal attainment optimization is a mathematical framework to describe the si-15 multaneous optimization of multiple conflicting objectives that must all reach a perfor-16 mance above a threshold or goal. In this study, we applied goal attainment optimization 17 to harness natural modularity of cellular metabolism to design a modular chassis cell for 18 optimal production of a diverse class of products, where each goal corresponds to the 19 minimum biosynthesis requirements (e.g., yields and rates) of a target product. This 20 modular cell design approach enables rapid generation of optimal production strains 21 that can be assembled from a modular cell and various exchangeable production mod-22 ules and hence accelerates the prohibitively slow and costly strain design process. We 23 formulated the modular cell design problem as a blended or goal attainment mixed 24 integer linear program, using mass-balance metabolic models as biological constraints. 25 By applying the modular cell design framework for a genome-scale metabolic model 26 of Escherichia coli, we demonstrated that a library of biochemically diverse products 27 could be effectively synthesized at high yields and rates from a modular (chassis) cell 28 with only a few genetic manipulations. Flux analysis revealed this broad modular-29 ity phenotype is supported by the natural modularity and flexible flux capacity of core 30 metabolic pathways. Overall, we envision the developed modular cell design framework 31 provides a powerful tool for synthetic biology and metabolic engineering applications 32 such as industrial biocatalysis to effectively produce fuels, chemicals, and therapeutics 33 from renewable and sustainable feedstocks, bioremediation, and biosensing. 34 Keywords-Biocatalysis, modular cell, ModCell, modular design, metabolic network mod-35 eling, constraint-based modeling, multi-objective optimization, mixed integer linear programming, 36 goal programming, Benders decomposition. 37 1 Introduction 38Microbial metabolism can be engineered to produce a large space of molecules from renewable 39 and sustainable feedstocks. 1 Currently, only a handful of fuels and chemicals out of the many 40 possible molecules offered by nature are industrially produced by microbial conversion, mainly 41 because the strain engineering process is too laborious and expensive. 2 Thus, innovative technologies 42 enabling rapid and economically-feasible strain engineering are needed to harness a large space of 43 industrially-relevant biochemicals. 1-3 To tackle this challenge, the principles of modular design that 44 have shown great success in traditional engineering disciplines can be adapted to construct modular 45 cell biocatalysts in a plug-and-play fashion with minimal strain optimization cycles. 4 46Multi-objective optimization is a powerful mathematical framework widely applied in engi-47 neering disciplines to tackle the optimal design of a complex system wi...

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“…Anaerobic conditions were imposed by setting oxygen exchange fluxes to be 0, and the glucose uptake rate was constrained to be at most 10 mmol/gCDW/h, as experimentally observed for E. coli . When using the genome-scale model iML1515 to simulate wGCP designs, the commonly observed fermentative products (acetate, CO2, ethanol, formate, lactate, succinate) were allowed for secretion as described elsewhere (50). For simulation of sGCP and NGP designs, the glucose uptake rate was fixed (i.e., −10 mmol/gCDW/h); otherwise, the flux is not active during the no-growth phases, resulting in the product synthesis rate of 0 regardless of genetic manipulations.…”

Section: Methodsmentioning

confidence: 99%