Docosahexaenoic acid (DHA) is one of the most important long-chain polyunsaturated fatty acids (LC-PUFAs), with numerous health benefits. Crypthecodinium cohnii, a marine heterotrophic dinoflagellate, is successfully used for the industrial production of DHA because it can accumulate DHA at high concentrations within the cells. Glycerol is an interesting renewable substrate for DHA production since it is a by-product of biodiesel production and other industries, and is globally generated in large quantities. The DHA production potential from glycerol, ethanol and glucose is compared by combining fermentation experiments with the pathway-scale kinetic modeling and constraint-based stoichiometric modeling of C. cohnii metabolism. Glycerol has the slowest biomass growth rate among the tested substrates. This is partially compensated by the highest PUFAs fraction, where DHA is dominant. Mathematical modeling reveals that glycerol has the best experimentally observed carbon transformation rate into biomass, reaching the closest values to the theoretical upper limit. In addition to our observations, the published experimental evidence indicates that crude glycerol is readily consumed by C. cohnii, making glycerol an attractive substrate for DHA production.

Biomass residue and waste stream bioconversion is a key pillar for successful transition toward sustainable bioeconomy. Spent microbial biomass (SMB) is a unique type of nutrient-rich residue generated from fermentation. This study addresses the waste–SMB–substrate cycle in fermentation. Data from a range of published fermentation processes using waste and non-waste substrates are analyzed for a variety of fermentation products including alcohols and biofuels, amino acids, polymers (PHA), and organic acids. On average, fermentation of waste substrates produces similar, or up to two–three times higher, amounts of SMB compared to purified substrates. SMB production from waste substrates is further illustrated with data from PHA production. The amino acid composition of SMB from 6 industrially relevant microorganisms is compared and shows relatively low variety (2–8%). The return of SMB as a (co-)substrate in fermentation is then considered by building upon the novel concept of sustainable metabolic engineering (SME). SME incorporates economic, environmental, and social sustainability criteria in its optimization algorithm to select microbial strain designs resulting in the most sustainable products. An example of SME application for SMB amino acid re-use by engineered Escherichia coli is demonstrated and discussed. A design with dual production of succinate and ethanol was found to be the most sustainable.

The sustainable metabolic engineering (SME) concept was defined by Stalidzans and Dace as an approach to the selection of the most sustainable metabolic engineering designs taking the economic, environmental and social components of sustainability into account. At the centre of the sustainability calculations is a genome-scale metabolic model that provides full balance of all incoming and outgoing metabolic fluxes at steady state. Therefore, sustainability indicators are assigned for each exchange reaction, enabling the calculation of sustainability features of consumption or production of each metabolite. The further development of the SME concept depends on its implementation at the computational level to acquire applicable results—sustainable production strain designs. This study proposes for the first time a workflow and tools of SME implementation using constraint-based stoichiometric modelling, genome-scale metabolic models and growth-coupled product synthesis approach. To demonstrate the application of SME, a relatively simple engineering task has been carried out. The most sustainable designs have been identified using Escherichia coli as the chassis organism, glucose as a substrate and gene deletions as a metabolic engineering tool. A growth-coupled production design tool has been used to reduce the variability of sustainability. The 10,000 most sustainable designs are found to produce succinate as the main product with the number of deleted genes ranging from two to seven. Many similar designs were identified due to the combinatorial explosion of different alternative combinations of gene deletion sets that have the same impact on the metabolism.

Finding the best knockout strategy for coupling biomass growth and production of a target metabolite using a metabolic model is a challenge in biotechnology. In this research, a three-step method named OptEnvelope is developed based on finding minimal active reactions for a target point in the feasible solution space using a mixed-integer linear programming formula. The method initially finds the reduced desirable solution space (envelope) in the product versus biomass plot by removing all inactive reactions. Then, with reinsertion of the deleted reactions, OptEnvelope attempts to reduce the number of knockouts so that the desirable envelope is preserved. Additionally, OptEnvelope searches for envelopes with higher minimum production rates or fewer knockouts by evaluating different target points within the desired solution space. It is possible to limit the maximal number of knockouts. The method was implemented on metabolic models of E. coli and S. cerevisiae to test the method benchmarking the capability of these industrial microbes for overproduction of acetate and glycerol under aerobic conditions and succinate and ethanol under anaerobic conditions. The results indicate that E. coli is more appropriate to produce acetate and succinate while S. cerevisiae is a better host for glycerol production. Gene deletions for some of the proposed reaction knockouts have been previously reported to increase the production of these metabolites in experiments. Both organisms are suitable for ethanol production, however, more knockouts for the adaptation of E. coli are required. OptEnvelope is available at https://github.com/lv-csbg/optEnvelope .

There are several ways in which mathematical modeling is used in fermentation control, but mechanistic mathematical genome-scale models of metabolism within the cell have not been applied or implemented so far. As part of the metabolic engineering task setting, we propose that metabolite fluxes and/or biomass growth rate be used to search for a fermentation steady state marker rule. During fermentation, the bioreactor control system can automatically detect the desired steady state using a logical marker rule. The marker rule identification can be also integrated with the production growth coupling approach, as presented in this study. A design of strain with marker rule is demonstrated on genome scale metabolic model iML1515 of Escherichia coli MG1655 proposing two gene deletions enabling a measurable marker rule for succinate production using glucose as a substrate. The marker rule example at glucose consumption 10.0 is: IF (specific growth rate μ is above 0.060 h⁻¹, AND CO₂ production under 1.0, AND ethanol production above 5.5), THEN succinate production is within the range 8.2–10, where all metabolic fluxes units are mmol ∗ gDW⁻¹ ∗ h⁻¹. An objective function for application in metabolic engineering, including productivity features and rule detecting sensor set characterizing parameters, is proposed. Two-phase approach to implementing marker rules in the cultivation control system is presented to avoid the need for a modeler during production.