The conversion of lower valued carbon sources into higher valued food proteins by microbial means could become a sustainable contribution in the future and it is therefore relevant to explore its economic feasibility. The created model utilizes the E. coli (iAF1260) base model and adds the option to shift a part of the metabolites designated for growth into recombinant protein production. This is achieved by adding, uptake rates of relevant metabolites, metabolic cost of protein production and ribosomal elongation rates, while the feasibility is evaluated by assigning cost to substrate materials and gain from produced α-La - as described in the method section. Process simulations were obtained by exhaustively maximizing productivity within the hypothetical outcome of cell growth and recombinant protein production at different substrate compositions. The results from the simulations are depictured as 3 dimensional surface planes where the individual coordinates represent different simulated processes with the global maximum being the best process. The model does not impose any time requirements and the different processes vary in time – as described in the method section.

Productivity of α-La

Results from the simulations where productivity were optimized are presented in Fig. 1 as a plot with yield per substrate, substrate compositions and productivity on the X, Y and Z axis respectively. A color layer representing the economic return based on the substrate price is furthermore applied and utilizes a per hour unit in order to compare simulations with different process times. The plot shows that the highest productivity occurs at a process with an average of 0.2 g produced protein per gram substrate (20%) with a substrate composition of 95% sugar and 5% soybean – indicating that some amino acid supplements in the form of soybean meal are favorable compared to media based on sugar exclusively. This demonstrates that a more expensive and less energy dense substrate like soybean meal can be applicable, as a greater flux of metabolites matters when maximizing productivity. The upstream process takes 11 hours and converts the initial substrate into α-La worth 21.8 times the value of the starting material. The underlying growth profile for the process is exponential with a specific growth rate that starts of as a plateau at a high rate, then declines over the course of the process and ends at a plateau of lower specific growth rate towards the end. The substrates are fully utilized during the process (which is not the case for all the simulations) and the ribosomes are at maximum capacity throughout the simulations. See Supporting Data - 5 for supporting figures. The economic return in the model is derived from the market price of 60% pure α-La, which includes all related costs e.g. expenditures for purification and distribution. The simulations potential economic return is thereby lower as additional cost needs to be applied to account for the further processing.

Figure 1 Simulation of economic productivity. The plot depictures α-La fermentations at maximized productivity with yield per substrate, substrate compositions and economic return based on the substrate price per hour on the X, Y and Z axis respectively. The global maximum represents a fermentation, which converts the initial substrate into α-La worth 21.8 times the value of the starting material. The fermentation is simulated to take 11 hours and require the substrate to compose of 5% soybean and 95% sugar. Full size image

Maximizing productivity provides simulated processes where the balance between biomass and protein production are shifted towards biomass, as increased biomass facilitates larger fluxes of protein over time compared to yield maximized processes. For situations where time is less of a concern and process yield is more important, the shift towards protein production can be utilized to increase the feasibility per cost of substrate. By recognizing that productivity always will be a desirable process parameter, the surface plane of the productivity plot was analyzed and a plot with the final process yield was created based on the productivity simulations. The plot is presented in Fig. 2 with yield per substrate, substrate compositions and process yield on the X, Y and Z axis respectively. The color over layer represents the economic return based on the substrate price. The plot demonstrates that higher process yields can be obtained at simulations with higher average produced α-La per gram substrate. The maximum process yield is present in a process with a 52 fold economic return per substrate at 0.5 gram produced protein per gram substrate at 100% sugar media composition. The process takes approximately 300 hours and is therefore likely to be associated with costs that would decrease the actual gain of running the process (labor and overhead) and technical issues (protein stability and cell death). Figure 3 presents the total fermentation time utilized in the simulation of economic productivity (Fig. 1) and simulation of economic yield (Fig. 2).

Figure 2 Simulation of economic yield. The plot depictures α-La fermentations at maximized productivity with yield per substrate, substrate compositions and economic return based on the substrate price on the X, Y and Z axis respectively. The global maximum represents a fermentation, which converts the initial substrate into α-La worth 52 times the value of the starting material. The fermentation is simulated to take 300 hours and require the substrate to compose of 100% sugar. Full size image

Figure 3 Total fermentation time. The plot depictures α-La fermentations at maximized productivity with yield per substrate, substrate compositions and time on the X, Y and Z axis respectively. The simulations vary greatly in total time, from a few hours to almost two weeks. Simulations with high yield per substrate do in general take longer. The rapid decline along the 60% yield (g/g) simulations seen in Figs 2 and 3 is a result of the cell growth and recombinant protein production being unable to maintain suitable productivity for prolonged fermentations. Full size image

The process yield and the productivity represent two bases of the production strategy, namely cost-efficiency and time-efficiency. The solutions existing in between are compromises and can be applied for complex processes where neither of the two parameters is completely dominating the economic feasibility. A combination of the two objectives can be maximized with the parameter α depending on the demand or growth of the market, see equation 1.

where 0 < α < 1.

The simulated upstream production of α-La demonstrates the potential of recombinant protein production in microorganisms and the cellular machinery that supports it. Processes based on microorganisms could become an additional food source and is worth considering when discussing the possible solutions for future supply of high quality proteins for human consumption10. The further downstream process of a theoretical α-La production would determine the overall feasibility, as purification costs can be significant in biotech processes.

Processing

E. coli is a prokaryote industrial platform which has been widely used for recombinant protein production in biotech and pharmaceutical applications and could be a candidate for recombinant food protein production. While there are a variety of different technical obstacles that makes recombinant protein production challenging, it is important to recognize that the underlying biological system is capable of converting biomass into specific products at a rapid pace, which could be utilized to secure adequate amounts of quality food proteins. Different strategies employed in recombinant protein production enable streamlined downstream processes and recombinant products can in general be rendered available for processing by multiple initial strategies, including intracellular recovery, extracellular secretion or intracellular recovery from inclusion bodies11. The cost of purification is a significant factor that affects the overall feasibility, though the current market of GMO food graded vitamins, preservatives and enzymes suggests that cost effective processes are available.

The cost of handling and terminating GMO material is an additional expenditure for any recombinant process, though the cost can be mitigated by recycling the microbial biomass produced. One approach is to seek authorization to use specific terminated GMO as feed products. Terminated GMOs used as feed material for animals can create value associated with sales and reduced cost in waste management, providing a sustainable alternative. Insulin producer Novo Nordisk and amino acid producer Ajinomoto Eurolysine SAS are examples of companies with different types of end-products which have sought such an authorization for some of their terminated GMO by-products (http://ec.europa.eu/food/dyna/gm_register/index_en.cfm).

Legislative concerns

The use of GMO derived ingredients intended for human consumption is under legislative control. Approval from governmental authorities is granted if sufficient documentation is presented in regards to production methodology and consumer safety. The probability of achieving usage authorization for α-La isolates from E. coli can be evaluated based on prior cases. The US FDA GRAS Notice Inventory, lists food ingredients that are regarded as safe to use and includes protein isolates from milk and whey (See GRN No. 37 and GRN No. 444 (http://www.accessdata.fda.gov/scripts/fdcc/). E. coli strain K-12 is among other used in the production of the recombinant enzyme, Chymosin, applied in cheese manufacturing. Chymosin is regarded as safe to use, when produced in certain non-pathogen microorganisms, including E. coli strain K-12 - according to § 184.1685 (http://www.gpo.gov/fdsys/pkg/CFR-2012-title21-vol3/pdf/CFR-2012-title21-vol3-sec184-1685.pdf). Chymosin is an example of a food ingredient that is dominating the market, as the natural source is not available in sufficient amounts nor at attractive prices. For theoretical α-La protein isolates from E. coli K-12, both compound and organism have been approved separately in prior cases for food formulation, which indicate that authorization could be obtained.

Production

The formation of amino acids followed by their subsequent polymerization into proteins is a notable expenditure in metabolites, as it requires both substrate derived building blocks and energy in the form of ATP12,13. The increased cost of amino acid compared to sugar does not decrease the feasibility of using amino acids in the model, as higher amino acid fluxes results in higher productivity of the recombinant protein. The capacity of the TCA cycle has been reported as a limiting factor during recombinant protein production in E. coli on minimal medium as the formation of intermediates for amino acids and intermediates designated for oxidative phosphorylation competes for substrates14. Media composition is an important parameter for industrial processes. The shift between protein production and growth is only limited in the model by the metabolites required for mRNA and subsequently protein synthesis, which implies two things: (1) the translation initiation of recombinant protein is not limited by physical constraints and (2) the utilized strain is capable of mimicking the required production profile through a controllable system. Elaborate algorithms and models exist that can overcome limitation at translation initiation by rational design15,16, while several controllable systems for protein production are available in bacteria at industrial scale17. Protein translation involves four phases: initiation, elongation, termination and ribosome turnover, where initiation usually is the limiting factor in recombinant production11,16 implying that the applied genetic material is limiting its own protein synthesis (inadequate transcription levels, mRNA instability, unfavorable ribosomal-binding-site complex). In the case where initiation limitations are overcome, the native genetic blueprint of the organism becomes the limiting factor for recombinant protein synthesis. In such cases the protein elongation rate in conjunction with the amount of ribosomes becomes the limiting rate - as detailed in the model. In vivo studies additionally suggest that the maximum elongation rate per ribosome is higher than the experimental observed18.

Perspective

The data used in the simulations are sensitive to fluctuations in cost of substrates and protein prices. A sensitivity analysis could be incorporated to predict feasibility based on projected trends in the market. To compete with traditional production the conversion of input material into α-La needs to be economically feasible, though the initial cost associated with acquiring production capacities at large scale, developing strain(s) and acquiring legislative approval, determines if it is an attractive investment. Alternative production methods utilizing recombinant protein production could become an important source for ingredients that would otherwise become scarce in the future or unsustainable to produce. A food industry integrated with recombinant production capabilities would be less susceptible to traditional negative events and could create novel products not currently available. Recombinant α-La production and other milk proteins could be incorporated into the dairy industry by utilizing their existing pipeline and infrastructure, while strains able to utilize the available material in the dairy industry could be further integrated into the process.

Bovine α-La is used as a substitution to Human α-La in infant formulas, as there are no feasible sources of Human α-La. Recombinant human α-La production could be envisioned, though controversies could arise due to the human origin. The controversy surrounding the use of GMOs for food production needs to be addressed and superior products, both in terms of quality and cost could help alleviate the negative image surrounding recombinant proteins for food. This in addition to rigorous safety tests would be needed to make recombinant products become marketable.

Assumptions and limitations

The data used for substrate uptake and protein chain elongation rate are from different E. coli strains obtained at slightly different experimental setups than that of the base model. We estimated the data to be sufficiently accurate to describe and limit protein production in conjunction with the GSMN, and to evaluate the initial feasibility of recombinant α-La production. The model does furthermore assume that the only burden imposed on the cells is the metabolic cost derived from the recombinant production, which is a simplification. Effects such as, ATP-dependent proteolytic degradation by the proteasome, increased chaperone requirements or protein instability are therefore neglected19. The biomass accumulation and the derived volume for optimal growth have not been taken into consideration and different fermentation modes have not been explored. The price of α-La is derived from its purity and functional properties and the model assumes that these properties are kept.