Solar-Powered Carbon Fixation for Food and Feed Production Using Microorganisms-A Comparative Techno-Economic Analysis.

This study evaluates the techno-economic feasibility of five solar-powered concepts for the production of autotrophic microorganisms for food and feed production; the main focus is on three concepts based on hydrogen-oxidizing bacteria (HOB), which are further compared to two microalgae-related concepts. Two locations with markedly different solar conditions are considered (Finland and Morocco), in which Morocco was found to be the most economically competitive for the cultivation of microalgae in open ponds and closed systems (1.4 and 1.9 € kg-1, respectively). Biomass production by combined water electrolysis and HOB cultivation results in higher costs for all three considered concepts. Among these, the lowest production cost of 5.3 € kg-1 is associated with grid-assisted electricity use in Finland, while the highest production cost of >9.1 € kg-1 is determined for concepts using solely photovoltaics and/or photoelectrochemical technology for on-site electricity production and solar-energy conversion to H2 by water electrolysis. All assessed concepts are capital intensive. Furthermore, a sensitivity analysis suggests that the production costs of HOB biomass can be lowered down to 2.1 € kg-1 by optimization of the process parameters among which volumetric productivity, electricity strategy, and electricity costs have the highest cost-saving potentials. The study reveals that continuously available electricity and H2 supply are essential for the development of a viable HOB concept due to the capital intensity of the needed technologies. In addition, volumetric productivity is the key parameter that needs to be optimized to increase the economic competitiveness of HOB production.

Evaluation of synthetic formaldehyde and methanol assimilation pathways in Yarrowia lipolytica.

BACKGROUND: Crude glycerol coming from biodiesel production is an attractive carbon source for biological production of chemicals. The major impurity in preparations of crude glycerol is methanol, which is toxic for most microbes. Development of microbes, which would not only tolerate the methanol, but also use it as co-substrate, would increase the feasibility of bioprocesses using crude glycerol as substrate. RESULTS: To prevent methanol conversion to CO2 via formaldehyde and formate, the formaldehyde dehydrogenase (FLD) gene was identified in and deleted from Yarrowia lipolytica. The deletion strain was able to convert methanol to formaldehyde without expression of heterologous methanol dehydrogenases. Further, it was shown that expression of heterologous formaldehyde assimilating enzymes could complement the deletion of FLD. The expression of either 3-hexulose-6-phosphate synthase (HPS) enzyme of ribulose monosphosphate pathway or dihydroxyacetone synthase (DHAS) enzyme of xylulose monosphosphate pathway restored the formaldehyde tolerance of the formaldehyde sensitive Δfld1 strain. CONCLUSIONS: In silico, the expression of heterologous formaldehyde assimilation pathways enable Y. lipolytica to use methanol as substrate for growth and metabolite production. In vivo, methanol was shown to be converted to formaldehyde and the enzymes of formaldehyde assimilation were actively expressed in this yeast. However, further development is required to enable Y. lipolytica to efficiently use methanol as co-substrate with glycerol.

Principal metabolic flux mode analysis.

Motivation: In the analysis of metabolism, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods, such as Flux Balance Analysis (FBA) and Elementary Mode Analysis, on the other hand, are able to capture the metabolic flux modes, however, they are primarily designed for the analysis of single samples at a time, and not best suited for exploratory analysis on a large sets of samples. Results: We propose a new methodology for the analysis of metabolism, called Principal Metabolic Flux Mode Analysis (PMFA), which marries the PCA and stoichiometric flux analysis approaches in an elegant regularized optimization framework. In short, the method incorporates a variance maximization objective form PCA coupled with a stoichiometric regularizer, which penalizes projections that are far from any flux modes of the network. For interpretability, we also introduce a sparse variant of PMFA that favours flux modes that contain a small number of reactions. Our experiments demonstrate the versatility and capabilities of our methodology. The proposed method can be applied to genome-scale metabolic network in efficient way as PMFA does not enumerate elementary modes. In addition, the method is more robust on out-of-steady steady-state experimental data than competing flux mode analysis approaches. Availability and implementation: Matlab software for PMFA and SPMFA and dataset used for experiments are available in https://github.com/aalto-ics-kepaco/PMFA. Supplementary information: Supplementary data are available at Bioinformatics online.

Whole-genome metabolic model of Trichoderma reesei built by comparative reconstruction.

BACKGROUND: Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis. RESULTS: A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model. CONCLUSIONS: The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes.

Comparative genome-scale reconstruction of gapless metabolic networks for present and ancestral species.

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/.

The combination of transformed and constrained Gibbs energies.

Gibbs free energy is the thermodynamic potential representing the fundamental equation at constant temperature, pressure, and molar amounts. Transformed Gibbs energies are important for biochemical systems because the local concentrations within cell compartments cannot yet be determined accurately. The method of Constrained Gibbs Energies adds kinetic reaction extent limitations to the internal constraints of the system thus extending the range of applicability of equilibrium thermodynamics from predefined constraints to dynamic constraints, e.g., adding time-dependent constraints of irreversible chemical change. In this article, the implementation and use of Transformed Gibbs Energies in the Gibbs energy minimization framework is demonstrated with educational examples. The combined method has the advantage of being able to calculate transient thermodynamic properties during dynamic simulation.