Advances in metabolic modeling of oleaginous microalgae

Central transcriptional regulator controls photosynthetic growth and carbon storage in response to high light

Carbon capture and biochemical storage are some of the primary drivers of photosynthetic yield and productivity. To elucidate the mechanisms governing carbon allocation, we designed a photosynthetic light response test system for genetic and metabolic carbon assimilation tracking, using microalgae as simplified plant models. The systems biology mapping of high light-responsive photophysiology and carbon utilization dynamics between two variants of the same Picochlorum celeri species, TG1 and TG2 elucidated metabolic bottlenecks and transport rates of intermediates using instationary 13C-fluxomics. Simultaneous global gene expression dynamics showed 73% of the annotated genes responding within one hour, elucidating a singular, diel-responsive transcription factor, closely related to the CCA1/LHY clock genes in plants, with significantly altered expression in TG2. Transgenic P. celeri TG1 cells expressing the TG2 CCA1/LHY gene, showed 15% increase in growth rates and 25% increase in storage carbohydrate content, supporting a coordinating regulatory function for a single transcription factor.

Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale

Metabolic engineering of microalgae offers a promising solution for sustainable biofuel production, and rational design of engineering strategies can be improved by employing metabolic models that integrate enzyme turnover numbers. However, the coverage of turnover numbers for Chlamydomonas reinhardtii, a model eukaryotic microalga accessible to metabolic engineering, is 17-fold smaller compared to the heterotrophic cell factory Saccharomyces cerevisiae. Here we generate quantitative protein abundance data of Chlamydomonas covering 2337 to 3708 proteins in various growth conditions to estimate in vivo maximum apparent turnover numbers. Using constrained-based modeling we provide proxies for in vivo turnover numbers of 568 reactions, representing a 10-fold increase over the in vitro data for Chlamydomonas. Integration of the in vivo estimates instead of in vitro values in a metabolic model of Chlamydomonas improved the accuracy of enzyme usage predictions. Our results help in extending the knowledge on uncharacterized enzymes and improve biotechnological applications of Chlamydomonas.

A state of the art review on the co-cultivation of microalgae-fungi in wastewater for biofuel production

The microalgae have a great potential as the fourth generation biofuel feedstock to deal with energy crisis, but the cost of production and biomass harvest are the major hurdles in terms of large scale production and applications. Using filamentous fungi to culture targeted alga for biomass accumulation and eventually harvesting is a sustainable way to mitigate environmental impacts. Microalgal co-culture method could be an alternative to overcome limitations and increase biomass yield and lipid accumulation. It was found to be the high feasibility for the production of biofuels from fungi and microalgae using wastewater. This article aimed to state the synergistic approaches, their culture protocols, harvesting procedure and their potential biotechnological applications. Additionally, algal-fungal consortia could digest cellulosic biomass, potentially reducing operating costs as part of industrial need. As a result of co-cultivation, biofuel production could be economically feasible owing to its excellent ability to treat wastewater and be eco-friendly. The implications of the innovative co-cultivation technology have demonstrated the potential for further development based on the policies that have been supported and implemented.

Metabolic network reconstruction of Euglena gracilis: Current state, challenges, and applications

A metabolic model, representing all biochemical reactions in a cell, is a prerequisite for several approaches in systems biology used to explore the metabolic phenotype of an organism. Despite the use of Euglena in diverse industrial applications and as a biological model, there is limited understanding of its metabolic network capacity. The unavailability of the completed genome data and the highly complex evolution of Euglena are significant obstacles to the reconstruction and analysis of its genome-scale metabolic model. In this mini-review, we discuss the current state and challenges of metabolic network reconstruction in Euglena gracilis. We have collated and present the available relevant data for the metabolic network reconstruction of E. gracilis, which could be used to improve the quality of the metabolic model of E. gracilis. Furthermore, we deliver the potential applications of the model in metabolic engineering. Altogether, it is supposed that this mini-review would facilitate the investigation of metabolic networks in Euglena and further lay out a direction for model-assisted metabolic engineering.

Predicting stress response and improved protein overproduction in Bacillus subtilis

Bacillus subtilis is a well-characterized microorganism and a model for the study of Gram-positive bacteria. The bacterium can produce proteins at high densities and yields, which has made it valuable for industrial bioproduction. Like other cell factories, metabolic modeling of B. subtilis has discovered ways to optimize its metabolism toward various applications. The first genome-scale metabolic model (M-model) of B. subtilis was published more than a decade ago and has been applied extensively to understand metabolism, to predict growth phenotypes, and served as a template to reconstruct models for other Gram-positive bacteria. However, M-models are ill-suited to simulate the production and secretion of proteins as well as their proteomic response to stress. Thus, a new generation of metabolic models, known as metabolism and gene expression models (ME-models), has been initiated. Here, we describe the reconstruction and validation of a ME model of B. subtilis, iJT964-ME. This model achieved higher performance scores on the prediction of gene essentiality as compared to the M-model. We successfully validated the model by integrating physiological and omics data associated with gene expression responses to ethanol and salt stress. The model further identified the mechanism by which tryptophan synthesis is upregulated under ethanol stress. Further, we employed iJT964-ME to predict amylase production rates under two different growth conditions. We analyzed these flux distributions and identified key metabolic pathways that permitted the increase in amylase production. Models like iJT964-ME enable the study of proteomic response to stress and the illustrate the potential for optimizing protein production in bacteria.

The paradoxes hidden behind the Droop model highlighted by a metabolic approach

We propose metabolic models for the haptophyte microalgae Tisochrysis lutea with different possible organic carbon excretion mechanisms. These models-based on the DRUM (Dynamic Reduction of Unbalanced Metabolism) methodology-are calibrated with an experiment of nitrogen starvation under day/night cycles, and then validated with nitrogen-limited chemostat culture under continuous light. We show that models including exopolysaccharide excretion offer a better prediction capability. It also gives an alternative mechanistic interpretation to the Droop model for nitrogen limitation, which can be understood as an accumulation of carbon storage during nitrogen stress, rather than the common belief of a nitrogen pool driving growth. Excretion of organic carbon limits its accumulation, which leads to a maximal C/N ratio (corresponding to the minimum Droop N/C quota). Although others phenomena-including metabolic regulations and dissipation of energy-are possibly at stake, excretion appears as a key component in our metabolic model, that we propose to include in the Droop model.

Recent advances in biofuel production through metabolic engineering

Rising global energy demands and climate crisis has created an unprecedented need for the bio-based circular economy to ensure sustainable development with the minimized carbon footprint. Along with conventional biofuels such as ethanol, microbes can be used to produce advanced biofuels which are equivalent to traditional fuels in their energy efficiencies and are compatible with already established infrastructure and hence can be directly blended in higher proportions without overhauling of the pre-existing setup. Metabolic engineering is at the frontiers to develop microbial chassis for biofuel bio-foundries to meet the industrial needs for clean energy. This review does a thorough inquiry of recent developments in metabolic engineering for increasing titers, rates, and yields (TRY) of biofuel production by engineered microorganisms.

Production of polyunsaturated fatty acids by Schizochytrium (Aurantiochytrium) spp

Diverse health benefits are associated with dietary consumption of omega-3 long-chain polyunsaturated fatty acids (ω-3 LC-PUFA), particularly docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA). Traditionally, these fatty acids have been obtained from fish oil, but limited supply, variably quality, and an inability to sustainably increase production for a rapidly growing market, are driving the quest for alternative sources. DHA derived from certain marine protists (heterotrophic thraustochytrids) already has an established history of commercial production for high-value dietary use, but is too expensive for use in aquaculture feeds, a much larger potential market for ω-3 LC-PUFA. Sustainable expansion of aquaculture is prevented by its current dependence on wild-caught fish oil as the source of ω-3 LC-PUFA nutrients required in the diet of aquacultured animals. Although several thraustochytrids have been shown to produce DHA and EPA, there is a particular interest in Schizochytrium spp. (now Aurantiochytrium spp.), as some of the better producers. The need for larger scale production has resulted in development of many strategies for improving productivity and production economics of ω-3 PUFA in Schizochytrium spp. Developments in fermentation technology and metabolic engineering for enhancing LC-PUFA production in Schizochytrium spp. are reviewed.

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

Insights on the Advancements of In Silico Metabolic Studies of Succinic Acid Producing Microorganisms: A Review with Emphasis on Actinobacillus succinogenes

Succinic acid (SA) is one of the top candidate value-added chemicals that can be produced from biomass via microbial fermentation. A considerable number of cell factories have been proposed in the past two decades as native as well as non-native SA producers. Actinobacillus succinogenes is among the best and earliest known natural SA producers. However, its industrial application has not yet been realized due to various underlying challenges. Previous studies revealed that the optimization of environmental conditions alone could not entirely resolve these critical problems. On the other hand, microbial in silico metabolic modeling approaches have lately been the center of attention and have been applied for the efficient production of valuable commodities including SA. Then again, literature survey results indicated the absence of up-to-date reviews assessing this issue, specifically concerning SA production. Hence, this review was designed to discuss accomplishments and future perspectives of in silico studies on the metabolic capabilities of SA producers. Herein, research progress on SA and A. succinogenes, pathways involved in SA production, metabolic models of SA-producing microorganisms, and status, limitations and prospects on in silico studies of A. succinogenes were elaborated. All in all, this review is believed to provide insights to understand the current scenario and to develop efficient mathematical models for designing robust SA-producing microbial strains.

Examination of Photo-, Mixo-, and Heterotrophic Cultivation Conditions on Haematococcus pluvialis Cyst Cell Germination

The microalgae Haematococcus pluvialis is used for the biotechnological production of astaxanthin. The red carotenoid accumulates in the cytoplasm under unfavorable conditions. Astaxanthin synthesis is associated with the transformation of motile vegetative cells into non-motile cyst cells. In the industrial process, after harvesting, the cyst cells are mechanically disrupted, dried, and finally, astaxanthin is extracted with supercritical CO2. The germination of the cyst cells represents an interesting alternative, replacing the mechanical cyst cell wall disruption. When cyst cells are exposed to favorable growth conditions, germination of the cyst cells occurs and zoospores are released after a certain time. These zoospores show a much weaker cell matrix compared to cyst cells. In this study, germination under phototrophic, mixotrophic, and heterotrophic conditions was examined. Glucose was used as the carbon source for mixotrophic and heterotrophic germination. Applying heterotrophic conditions, up to 80% of the cells were in the zoospore stage 49 h after the start of germination, and extraction yields of up to 50% were achieved using the solvent ethyl acetate for the extraction of astaxanthin from the algal broth containing zoospores. An extraction yield of up to 64% could be achieved by doubling the nitrate concentration and combining mixotrophic and heterotrophic cultivation.

Lipids from Microalgae for Cosmetic Applications

In recent years, there has been considerable interest in using microalgal lipids in the food, chemical, pharmaceutical, and cosmetic industries. Several microalgal species can accumulate appreciable lipid quantities and therefore are characterized as oleaginous. In cosmetic formulations, lipids and their derivatives are one of the main ingredients. Different lipid classes are great moisturizing, emollient, and softening agents, work as surfactants and emulsifiers, give consistence to products, are color and fragrance carriers, act as preservatives to maintain products integrity, and can be part of the molecules delivery system. In the past, chemicals have been widely used but today’s market and customers’ demands are oriented towards natural products. Microalgae are an extraordinary source of lipids and other many bioactive molecules. Scientists’ attention to microalgae cultivation for their industrial application is increasing. For the high costs associated, commercialization of microalgae and their products is still not very widespread. The possibility to use biomass for various industrial purposes could make microalgae more economically competitive.

Current perspectives on integrated approaches to enhance lipid accumulation in microalgae

In recent years, research initiatives on renewable bioenergy or biofuels have been gaining momentum, not only due to fast depletion of finite reserves of fossil fuels but also because of the associated concerns for the environment and future energy security. In the last few decades, interest is growing concerning microalgae as the third-generation biofuel feedstock. The CO₂ fixation ability and conversion of it into value-added compounds, devoid of challenging food and feed crops, make these photosynthetic microorganisms an optimistic producer of biofuel from an environmental point of view. Microalgal-derived fuels are currently being considered as clean, renewable, and promising sustainable biofuel. Therefore, most research targets to obtain strains with the highest lipid productivity and a high growth rate at the lowest cultivation costs. Different methods and strategies to attain higher biomass and lipid accumulation in microalgae have been extensively reported in the previous research, but there are fewer inclusive reports that summarize the conventional methods with the modern techniques for lipid enhancement and biodiesel production from microalgae. Therefore, the current review focuses on the latest techniques and advances in different cultivation conditions, the effect of different abiotic and heavy metal stress, and the role of nanoparticles (NPs) in the stimulation of lipid accumulation in microalgae. Techniques such as genetic engineering, where particular genes associated with lipid metabolism, are modified to boost lipid synthesis within the microalgae, the contribution of "Omics" in metabolic pathway studies. Further, the contribution of CRISPR/Cas9 system technique to the production of microalgae biofuel is also briefly described.

High-quality genome-scale metabolic model of Aurantiochytrium sp. T66

The long-chain, ω-3 polyunsaturated fatty acids (PUFAs) (e.g., eicosapentaenoic acid [EPA] and docosahexaenoic acid [DHA]), are essential for humans and animals, including marine fish species. Presently, the primary source of these PUFAs is fish oils. As the global production of fish oils appears to be reaching its limits, alternative sources of high-quality ω-3 PUFAs is paramount to support the growing aquaculture industry. Thraustochytrids are a group of heterotrophic protists with the capability to synthesize and accrue large amounts of DHA. Thus, the thraustochytrids are prime candidates to solve the increasing demand for ω-3 PUFAs using microbial cell factories. However, a systems-level understanding of their metabolic shift from cellular growth into lipid accumulation is, to a large extent, unclear. Here, we reconstructed a high-quality genome-scale metabolic model of the thraustochytrid Aurantiochytrium sp. T66 termed iVS1191. Through iterative rounds of model refinement and extensive manual curation, we significantly enhanced the metabolic scope and coverage of the reconstruction from that of previously published models, making considerable improvements with stoichiometric consistency, metabolic connectivity, and model annotations. We show that iVS1191 is highly consistent with experimental growth data, reproducing in vivo growth phenotypes as well as specific growth rates on minimal carbon media. The availability of iVS1191 provides a solid framework for further developing our understanding of T66's metabolic properties, as well as exploring metabolic engineering and process-optimization strategies in silico for increased ω-3 PUFA production.

High-throughput proteomics and metabolomic studies guide re-engineering of metabolic pathways in eukaryotic microalgae: A review

Eukaryotic microalgae are a rich source of commercially important metabolites including lipids, pigments, sugars, amino acids and enzymes. However, their inherent genetic potential is usually not enough to support high level production of metabolites of interest. In order to move on from the traditional approach of improving product yields by modification of the cultivation conditions, understanding the metabolic pathways leading to the synthesis of the bioproducts of interest is crucial. Identification of new targets for strain engineering has been greatly facilitated by the rapid development of high-throughput sequencing and spectroscopic techniques discussed in this review. Despite the availability of high throughput analytical tools, examples of gathering and application of proteomic and metabolomic data for metabolic engineering of microalgae are few and mainly limited to lipid production. The present review highlights the application of contemporary proteomic and metabolomic techniques in eukaryotic microalgae for redesigning pathways for enhanced production of algal metabolites.

MetaNetX/MNXref: unified namespace for metabolites and biochemical reactions in the context of metabolic models

MetaNetX/MNXref is a reconciliation of metabolites and biochemical reactions providing cross-links between major public biochemistry and Genome-Scale Metabolic Network (GSMN) databases. The new release brings several improvements with respect to the quality of the reconciliation, with particular attention dedicated to preserving the intrinsic properties of GSMN models. The MetaNetX website (https://www.metanetx.org/) provides access to the full database and online services. A major improvement is for mapping of user-provided GSMNs to MXNref, which now provides diagnostic messages about model content. In addition to the website and flat files, the resource can now be accessed through a SPARQL endpoint (https://rdf.metanetx.org).

A review on photobioreactor design and modelling for microalgae production

From the cell to the photobioreactor and to the industrial exploitation of microalgae, through the controlled experiments and modelling.

Microalgae with artificial intelligence: A digitalized perspective on genetics, systems and products

With recent advances in novel gene-editing tools such as RNAi, ZFNs, TALENs, and CRISPR-Cas9, the possibility of altering microalgae toward designed properties for various application is becoming a reality. Alteration of microalgae genomes can modify metabolic pathways to give elevated yields in lipids, biomass, and other components. The potential of such genetically optimized microalgae can give a "domino effect" in further providing optimization leverages down the supply chain, in aspects such as cultivation, processing, system design, process integration, and revolutionary products. However, the current level of understanding the functional information of various microalgae gene sequences is still primitive and insufficient as microalgae genome sequences are long and complex. From this perspective, this work proposes to link up this knowledge gap between microalgae genetic information and optimized bioproducts using Artificial Intelligence (AI). With the recent acceleration of AI research, large and complex data from microalgae research can be properly analyzed by combining the cutting-edge of both fields. In this work, the most suitable class of AI algorithms (such as active learning, semi-supervised learning, and meta-learning) are discussed for different cases of microalgae applications. This work concisely reviews the current state of the research milestones and highlight some of the state-of-art that has been carried out, providing insightful future pathways. The utilization of AI algorithms in microalgae cultivation, system optimization, and other aspects of the supply chain is also discussed. This work opens the pathway to a digitalized future for microalgae research and applications.

Linking metabolic phenotypes to pathogenic traits among "Candidatus Liberibacter asiaticus" and its hosts

Candidatus Liberibacter asiaticus (CLas) has been associated with Huanglongbing, a lethal vector-borne disease affecting citrus crops worldwide. While comparative genomics has provided preliminary insights into the metabolic capabilities of this uncultured microorganism, a comprehensive functional characterization is currently lacking. Here, we reconstructed and manually curated genome-scale metabolic models for the six CLas strains A4, FL17, gxpsy, Ishi-1, psy62, and YCPsy, in addition to a model of the closest related culturable microorganism, L. crescens BT-1. Predictions about nutrient requirements and changes in growth phenotypes of CLas were confirmed using in vitro hairy root-based assays, while the L. crescens BT-1 model was validated using cultivation assays. Host-dependent metabolic phenotypes were revealed using expression data obtained from CLas-infected citrus trees and from the CLas-harboring psyllid Diaphorina citri Kuwayama. These results identified conserved and unique metabolic traits, as well as strain-specific interactions between CLas and its hosts, laying the foundation for the development of model-driven Huanglongbing management strategies.

Synthetic microbial communities of heterotrophs and phototrophs facilitate sustainable growth

Microbial communities comprised of phototrophs and heterotrophs hold great promise for sustainable biotechnology. Successful application of these communities relies on the selection of appropriate partners. Here we construct four community metabolic models to guide strain selection, pairing phototrophic, sucrose-secreting Synechococcus elongatus with heterotrophic Escherichia coli K-12, Escherichia coli W, Yarrowia lipolytica, or Bacillus subtilis. Model simulations reveae metabolic exchanges that sustain the heterotrophs in minimal media devoid of any organic carbon source, pointing to S. elongatus-E. coli K-12 as the most active community. Experimental validation of flux predictions for this pair confirms metabolic interactions and potential production capabilities. Synthetic communities bypass member-specific metabolic bottlenecks (e.g. histidine- and transport-related reactions) and compensate for lethal genetic traits, achieving up to 27% recovery from lethal knockouts. The study provides a robust modelling framework for the rational design of synthetic communities with optimized growth sustainability using phototrophic partners.

Dynamic resource allocation drives growth under nitrogen starvation in eukaryotes

Cells can sense changes in their extracellular environment and subsequently adapt their biomass composition. Nutrient abundance defines the capability of the cell to produce biomass components. Under nutrient-limited conditions, resource allocation dramatically shifts to carbon-rich molecules. Here, we used dynamic biomass composition data to predict changes in growth and reaction flux distributions using the available genome-scale metabolic models of five eukaryotic organisms (three heterotrophs and two phototrophs). We identified temporal profiles of metabolic fluxes that indicate long-term trends in pathway and organelle function in response to nitrogen depletion. Surprisingly, our calculations of model sensitivity and biosynthetic cost showed that free energy of biomass metabolites is the main driver of biosynthetic cost and not molecular weight, thus explaining the high costs of arginine and histidine. We demonstrated how metabolic models can accurately predict the complexity of interwoven mechanisms in response to stress over the course of growth.

Cultivation of Oily Microalgae for the Production of Third-Generation Biofuels

Biofuel production by oleaginous microalgae is a promising alternative to the conventional fossil fuels. Many microalgae species have been investigated and deemed as potential renewable sources for the production of biofuel, biogas, food supplements and other products. Oleaginous microalgae, named for their ability to produce oil, are reported to store 30–70% of lipid content due to its metabolic properties under nutrient starvation conditions. This review presents the assortment of the research studies focused on biofuel production from oleaginous microalgae. The new methods and technologies developed for oleaginous microalgae cultivation to improve their biomass content and lipid accumulation capacity were reviewed. The production of renewable, carbon neutral, bio-based or microalgae-based transport fuels are necessary for environmental protection and economic sustainability. Microalgae are a significant source of renewable biodiesel because of their ability to produce oils in the presence of sunlight more efficiently than that of crop oils. This review will provide the background to understanding the bottlenecks and the need for improvement in the cultivation or harvesting process for oleaginous microalgae.

Utilizing genome-scale models to optimize nutrient supply for sustained algal growth and lipid productivity

Nutrient availability is critical for growth of algae and other microbes used for generating valuable biochemical products. Determining the optimal levels of nutrient supplies to cultures can eliminate feeding of excess nutrients, lowering production costs and reducing nutrient pollution into the environment. With the advent of omics and bioinformatics methods, it is now possible to construct genome-scale models that accurately describe the metabolism of microorganisms. In this study, a genome-scale model of the green alga Chlorella vulgaris (iCZ946) was applied to predict feeding of multiple nutrients, including nitrate and glucose, under both autotrophic and heterotrophic conditions. The objective function was changed from optimizing growth to instead minimizing nitrate and glucose uptake rates, enabling predictions of feed rates for these nutrients. The metabolic model control (MMC) algorithm was validated for autotrophic growth, saving 18% nitrate while sustaining algal growth. Additionally, we obtained similar growth profiles by simultaneously controlling glucose and nitrate supplies under heterotrophic conditions for both high and low levels of glucose and nitrate. Finally, the nitrate supply was controlled in order to retain protein and chlorophyll synthesis, albeit at a lower rate, under nitrogen-limiting conditions. This model-driven cultivation strategy doubled the total volumetric yield of biomass, increased fatty acid methyl ester (FAME) yield by 61%, and enhanced lutein yield nearly 3 fold compared to nitrogen starvation. This study introduces a control methodology that integrates omics data and genome-scale models in order to optimize nutrient supplies based on the metabolic state of algal cells in different nutrient environments. This approach could transform bioprocessing control into a systems biology-based paradigm suitable for a wide range of species in order to limit nutrient inputs, reduce processing costs, and optimize biomanufacturing for the next generation of desirable biotechnology products.

Production of Amphidinols and Other Bioproducts of Interest by the Marine Microalga Amphidinium carterae Unraveled by Nuclear Magnetic Resonance Metabolomics Approach Coupled to Multivariate Data Analysis

This study assessed the feasibility of an NMR metabolomics approach coupled to multivariate data analysis to monitor the naturally present or stresses-elicited metabolites from a long-term (>170 days) culture of the dinoflagellate marine microalgae Amphidinium carterae grown in a fiberglass paddlewheel-driven raceway photobioreactor. Metabolic contents, in particular, in two members of the amphidinol family, amphidinol A and its 7-sulfate derivative amphidinol B (referred as APDs), and other compounds of interest (fatty acids, carotenoids, oxylipins, etc.) were evaluated by altering concentration levels of the f/2 medium nutrients and daily mean irradiance. Operating with a 24 h sinusoidal light cycle allowed a 3-fold increase in APD production, which was also detected by an increase in hemolytic activity of the methanolic extract of A. carterae biomass. The presence of APDs was consistent with the antitumoral activity measured in the methanolic extracts of the biomass. Increased daily irradiance was accompanied by a general decrease in pigments and an increase in SFAs (saturated fatty acids), MUFAs (monounsaturated fatty acids), and DHA (docosahexaenoic acid), while increased nutrient availability lead to an increase in sugar, amino acid, and PUFA ω-3 contents and pigments and a decrease in SFAs and MUFAs. NMR-based metabolomics is shown to be a fast and suitable method to accompany the production of APD and bioactive compounds without the need of tedious isolation methods and bioassays. The two APD compounds were chemically identified by spectroscopic NMR and spectrometric ESI-IT MS (electrospray ionization ion trap mass spectrometry) and ESI-TOF MS (ESI time-of-flight mass spectrometry) methods.

Current status and applications of genome-scale metabolic models

Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.