Understanding regulation in substrate dependent modulation of growth and production of alcohols in Clostridium sporogenes NCIM 2918 through metabolic network reconstruction and flux balance analysis

Hydrogen production pathways in Clostridia and their improvement by metabolic engineering

Biological production of hydrogen has a tremendous potential as an environmentally sustainable technology to generate a clean fuel. Among the different available methods to produce biohydrogen, dark fermentation features the highest productivity and can be used as a means to dispose of organic waste biomass. Within this approach, Clostridia have the highest theoretical H2 production yield. Nonetheless, most strains show actual yields far lower than the theoretical maximum: improving their efficiency becomes necessary for achieving cost-effective fermentation processes. This review aims at providing a survey of the metabolic network involved in H2 generation in Clostridia and strategies used to improve it through metabolic engineering. Together with current achievements, a number of future perspectives to implement these results will be illustrated.

Towards industrial biological hydrogen production: a review

Increased production of renewable energy sources is becoming increasingly needed. Amidst other strategies, one promising technology that could help achieve this goal is biological hydrogen production. This technology uses micro-organisms to convert organic matter into hydrogen gas, a clean and versatile fuel that can be used in a wide range of applications. While biohydrogen production is in its early stages, several challenges must be addressed for biological hydrogen production to become a viable commercial solution. From an experimental perspective, the need to improve the efficiency of hydrogen production, the optimization strategy of the microbial consortia, and the reduction in costs associated with the process is still required. From a scale-up perspective, novel strategies (such as modelling and experimental validation) need to be discussed to facilitate this hydrogen production process. Hence, this review considers hydrogen production, not within the framework of a particular production method or technique, but rather outlines the work (bioreactor modes and configurations, modelling, and techno-economic and life cycle assessment) that has been done in the field as a whole. This type of analysis allows for the abstraction of the biohydrogen production technology industrially, giving insights into novel applications, cross-pollination of separate lines of inquiry, and giving a reference point for researchers and industrial developers in the field of biohydrogen production.

Machine learning and circular bioeconomy: Building new resource efficiency from diverse waste streams

Biorefinery systems are playing pivotal roles in the technological support of resource efficiency for circular bioeconomy. Meanwhile, artificial intelligence presents great potential in handling scientific tasks of high-dimensional complexity. This review article scrutinizes the status of machine learning (ML) applications in four critical biorefinery systems (i.e. composting, fermentation, anaerobic digestion, and thermochemical conversions) as well as their advancements against traditional modeling techniques of mechanistic approach. The contents cover their algorithm selections, modeling challenges, and prospective improvements. Perspectives are sketched to further inform collective efforts on crucial aspects. The multidisciplinary interchange of modeling knowledge will enable a more progressive digital transformation of sustainability efforts in supporting sustainable development goals.

Optimization and Scale-Up of Fermentation Processes Driven by Models

In the era of sustainable development, the use of cell factories to produce various compounds by fermentation has attracted extensive attention; however, industrial fermentation requires not only efficient production strains, but also suitable extracellular conditions and medium components, as well as scaling-up. In this regard, the use of biological models has received much attention, and this review will provide guidance for the rapid selection of biological models. This paper first introduces two mechanistic modeling methods, kinetic modeling and constraint-based modeling (CBM), and generalizes their applications in practice. Next, we review data-driven modeling based on machine learning (ML), and highlight the application scope of different learning algorithms. The combined use of ML and CBM for constructing hybrid models is further discussed. At the end, we also discuss the recent strategies for predicting bioreactor scale-up and culture behavior through a combination of biological models and computational fluid dynamics (CFD) models.

Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters

Recent noteworthy advances in the development of high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its capabilities of predicting the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters. This article is protected by copyright. All rights reserved.

Effect of dissolved oxygen on L-methionine production from glycerol by Escherichia coli W3110BL using metabolic flux analysis method

L-Methionine is an essential amino acid in humans, which plays an important role in the synthesis of some important amino acids and proteins. In this work, metabolic flux of batch fermentation of L-methionine with recombinant Escherichia coli W3110BL was analyzed using the flux balance analysis method, which estimated the intracellular flux distributions under different dissolved oxygen conditions. The results revealed the producing L-methionine flux of 4.8 mmol/(g cell·h) [based on the glycerol uptake flux of 100 mmol/(g cell·h)] was obtained at 30% dissolved oxygen level which was higher than that of other dissolved oxygen levels. The carbon fluxes for synthesizing L-methionine were mainly obtained from the pathway of phosphoenolpyruvate to oxaloacetic acid [15.6 mmol/(g cell·h)] but not from the TCA cycle. Hence, increasing the flow from phosphoenolpyruvate to oxaloacetic acid by enhancing the enzyme activity of phosphoenolpyruvate carboxylase might be conducive to the production of L-methionine. Additionally, pentose phosphate pathway could provide a large amount of reducing power NADPH for the synthesis of amino acids and the flux could increase from 41 mmol/(g cell·h) to 51 mmol/(g cell·h) when changing the dissolved oxygen levels, thus meeting the requirement of NADPH for L-methionine production and biomass synthesis. Therefore, the following modification of the strains should based on the improvement of the key pathway and the NAD(P)/NAD(P)H metabolism.

2-Phenylethanol and 2-phenylethylacetate production by nonconventional yeasts using tequila vinasses as a substrate

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Yeast species influenced the de novo synthesis of 2-phenylethylacetate.

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Inhibitory compounds showed a strong influence on cell growth and 2-phenylethylacetate production for the evaluated yeasts.

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More than a 50 % reduction in the chemical and biochemical oxygen demand was achieved by yeast fermentation.

Vinasses from the tequila industry are wastewaters with highly elevated organic loads. Therefore, to obtain value-added products by yeast fermentations, such as 2-phenylethanol (2-PE) and 2-phenylethylacetate (2-PEA), could be interesting for industrial applications from tequila vinasses. In this study, four yeasts species (Wickerhamomyces anomalus, Candida glabrata, Candida utilis, and Candida parapsilosis) were evaluated with two different chemically defined media and tequila vinasses. Differences in the aroma compounds production were observed depending on the medium and yeast species used. In tequila vinasses, the highest concentration (65 mg/L) of 2-PEA was reached by C. glabrata, the inhibitory compounds decreased biomass production and synthesis of 2-PEA, and biochemical and chemical oxygen demands were reduced by more than 50 %. Tequila vinasses were suitable for the production of 2-phenylethylacetate by the shikimate pathway. A metabolic network was developed to obtain a guideline to improve 2-PE and 2-PEA production using flux balance analysis (FBA).

Dark fermentation metabolic models to study strategies for hydrogen consumers inhibition

A Flux Balance Analysis (FBA) metabolic model of dark fermentation was developed for anaerobic mixed cultures. In particular, the model was applied to evaluate the effect of a specific inoculum pre-treatment strategy, addition of waste frying oil (WFO) on H₂-producing and H₂-consuming metabolic pathways. Productions of volatile fatty acid (VFAs), CO₂, H₂ and CH₄ measured through triplicate batch experiments, were used as constraints for the FBA model, to compute fluxes trough different metabolic pathways. FBA model could estimate the effect of pre-treatment with WFO on major microbial populations present in the mixed community (H₂ producing bacteria, homoacetogen and methanogens). Results revealed that low concentrations of WFO did not completely inhibited hydrogenotrophic methanogens. FBA showed that acetoclastic methanogens were more sensitive to WFO, in comparison to hydrogenotrophic methanogens. The proposed model can be used to study H₂ production by any other mixed microbial culture with similar substrates.