Microalgal biomass represents a sustainable bioresource for various applications, such as food, nutraceuticals, pharmaceuticals, feed, and other bio-based products. For decades, its mass production has attracted widespread attention and interest. The process of microalgal biomass production involves several techniques, mainly cultivation, harvesting, drying, and pollution control. These techniques are often designed and optimized to meet optimal growth conditions for microalgae and to produce high-quality biomass at acceptable cost. Importantly, mass production techniques are important for producing a commercial product in sufficient amounts. However, it should not be overlooked that microalgal biotechnology still faces challenges, in particular the high cost of production, the lack of knowledge about biological contaminants and the challenge of loss of active ingredients during biomass production. These issues involve the research and development of low-cost, standardized, industrial-scale production equipment and the optimization of production processes, as well as the urgent need to increase the research on biological contaminants and microalgal active ingredients. This review systematically examines the global development of microalgal biotechnology for biomass production, with emphasis on the techniques of cultivation, harvesting, drying and control of biological contaminants, and discusses the challenges and strategies to further improve quality and reduce costs. Moreover, the current status of biomass production of some biotechnologically important species has been summarized, and the importance of improving microalgae-related standards for their commercial applications is noted. These oArthrospira, was harvested in certain environments of alkaline soda lakes in countries, such as Chad or Mexico, and was used as a food supplement [Nostoc sphaeroides (known as Ge-Xian-Mi), was collected for food or traditional Chinese medicine [N. sphaeroides and Arthrospira widely as traditional food crops due to insufficient production technology. Early microalgae work on artificial culture started in Europe in the mid-nineteenth century. A small-scale laboratory culture was started by a German scientist [Microalgal biomass has been used since antiquity Fig. . Initialpplement . During medicine . Howevercientist . Over thcientist , 5.Fig. Chlorella in the laboratory has been skillfully mastered, the bottleneck in outdoor cultivation lies in the lack of facilities and technologies of for large-scale production. The first attempt to translate the biological requirements of photoautotrophic biomass culture into engineering specifications for large-scale cultivation was achieved during 1948–1950 by workers at the Stanford Research Institute, San Francisco, USA [Chlorella pilot plant for the Carnegie Institution  was undertaken by Arthur D. Little, Inc. [Chlorella pilot plant showed that large-scale cultivation of microalgae was technically feasible, although the expenses had to be reduced [Chlorella for developing functional food material in the 1960s [Chlorella producers in Japan and Taiwan attempted to supplement acetate or glucose as carbon and energy sources to heterotrophically cultivate Chlorella in stainless steel tanks [Arthrospira, was used in large-scale outdoor cultivation near the alkaline soda lakes in Mexico, and production reached 1000 kg day−1 in 1974 [Dunaliella was established in the USA and Australia to produce β-carotene [In the early twentieth century, algae researchers considered the large-scale cultivation of microalgae as a food substitute in response to the food crisis , 6, 7. Hhe 1960s . Some prel tanks , 12. Not in 1974 . In the carotene . By the Chlorella species and Scenedesmus acuminatus, which provided an important technical foundation for promoting its industrial application as an alternative high-quality protein source of food and feed [Arthrospira per year through improved cultivation techniques and screening for low-temperature tolerant algae strains [Haematococcus pluvialis in 2000 [Euglena gracilis for the production of health foods; its commercial cultivation began in 2007 after the improvement of the harvesting and drying techniques suitable for Euglena cells [Euglena and Chlorella biomass has been patented in China. In addition to cultivation techniques, other processes, including harvesting, drying, and control of biological contaminants also guarantee the production of microalgal biomass on an industrial scale at acceptable cost. Owing to the continuous contribution of biotechnology, the global output of microalgal biomass is now close to 20,000 metric tons [In the twenty-first century, the global demand for microalgae is dominated by food, health products and feed –18. Howeand feed –21. In r strains . Some sp in 2000 . In Japana cells . More reric tons , 25–30.The commercial production of microalgae driven by biotechnology is considered a new agricultural model, which can provide sustainable raw materials for hundreds of emerging products and become the driving force of global economic growth. However, the fact is that the various processes of microalgal biomass production have a number of economic or technical drawbacks. In particular, high production costs remain an important factor limiting biomass production. In addition, there is a lack of understanding of biological contaminants in the cultivation process and the challenge of active ingredient loss during biomass drying has not been fully addressed. Standards related to microalgae and its products are important to ensure safety and quality. Regrettably, the microalgae industry still lacks sound standards. These issues involve the research and development of low-cost, standardized, industrial-scale microalgal production equipment and the optimization of production processes, as well as the urgent need to increase the research on biological contaminants and microalgal active ingredients. Briefly, the future of microalgal biotechnology is still challenging.Currently, there are a number of excellent reviews on microalgal cultivation and biomass utilization strategies –32. HoweThe original idea of photosynthetic mass cultivation of microalgae was developed in Germany in the early 1940s to produce lipids from diatoms . However2 addition. This system has minimal construction and operation costs, although maintaining of monocultures and controlling of environmental parameters is difficult. In addition, lower cell density means lower productivity and increase in the cost of harvesting. For example, artificial shallow ponds (2000–5000 m2) used for Dunaliella cultivation in Western Australia can only produce 1 g dry weight m−2 day−1 [2 supplement devices, are installed [2 supplement can be provided for microalgal photosynthesis, and stirring enhances the light utilization efficiency of cells. The world's earliest specially modified raceway pond was designed by workers in Germany in the 1950s for evaluating the possibility of biological utilization of CO2 from waste gases [−1, each 9 m long and 70 cm wide [2, because relatively even mixing by the rotating arm is no longer possible in larger ponds. Notably, most of the culture ponds for Chlorella cultivation used in Japan are circular in shape and up to 50 m in diameter. However, the cultures in these intensive open systems are usually grown at biomass concentrations in the range of 0.5 to 1 g L−1, and the light utilization efficiency of cells is the main limiting factor, which depended on the culture depth and stirring.Open ponds are the oldest microalgal cultivation systems. Typically, natural or artificial ponds, raceways, and circular ponds represent open pond systems for microalgae, where algae are cultivated under conditions identical to the external environment , 11. NatChlorella sp. MUR 268 and Scenedesmus obliquus have achieved productivity in excess of 20 g dry weight m−2 day−1 [Circulation cascades  were considered as high cell-density open culture systems for microalgae. The first experimental circulation cascade was designed by Dr. Ivan Šetlík and was built at the Botanical Garden of the Slovak Academy of Sciences in the late 1950s . The sys−2 day−1 , 36. HowArthrospira, Chlorella, and Dunaliella. The main characteristic of these species is that they can only grow in specific environments, which is hostile to most other competitors. For example, D. salina grows in salty water (NaCl concentrations > 20% w/v) and A. platensis grows in highly alkaline environments (pH > 9.2). Other microalgae that can be grown in open ponds are rapidly growing dominant species, such as Chlorella and Scenedesmus. Some other contaminants, including heavy metals and microplastics, are also unacceptable for microalgae cultivation for food and food supplement purpose. Furthermore, massive water loss due to evaporation and low cell concentration and biomass productivity are also the intrinsic disadvantages of open ponds. Therefore, the future techniques for open ponds should address these bottlenecks while maintaining lower production costs. Notably, using open ponds for the production of valuable microalgal products is unlikely to be sustainable or economic, thus, attempts have been made to overcome some of their limitations using closed pond or enclosed photobioreactor systems [A major challenge of open ponds is the sensitivity to pollutants. Thus, only few species can be cultivated in these ponds for biomass production on a commercial scale, such as  systems .2 addition, and cooling facilities, and it can be better optimized according to the biological characteristics of the microalgal species cultivated. Compared to open systems, enclosed photobioreactors have several advantages, mainly including (i) larger surface-to-volume ratio, (ii) low CO2 losses, (iii) reduced risk of contamination, (iv) smaller area requirements, (v) ability to prevent evaporation, and (vi) higher cell productivities. So far, various photobioreactors consisting of glass or transparent plastic tubes, and columns or panels, have been designed using either natural or artificial lighting.Photobioreactors represent sophisticated and flexible systems working either outdoors or indoors, in which a single species is inoculated to keep a clean culture operation. A photobioreactor is usually equipped with lighting, stirring, CO2 bubbling up from the bottom. Vertical-column reactors described by Cook in 1950 were the first real enclosed systems for microalgae culture [−2 day−1 for Chlorella and Nannochloropsis could be achieved in a vertical air-lift photobioreactors [−2 day−1 could be obtained in a vertical glass tube for Monoraphidium [2 are achieved using a pump  or an air-lift (injecting a stream of compressed air into an upward-pointing tube) [Porphyridium cruentum [Arthrospira, which led to a high biomass productivity of 30 g dry weight m−2 day−1. In particular, the largest known microalgae plant using tubular reactors has been established in Pataias , with a total-volume of 1300 m3 and occupying one hectare of land, operated by A4F-AlgaFuel at the Secil Cement Company, for producing food-grade C. vulgaris and Nannochloropsis [The most commonly used photobioreactors are vertical-column and tubular. The former is a relatively simple system, in which stirring is achieved by air or high concentration of CO culture . In the reactors , while 2aphidium . Despiteaphidium , severalng tube) . The celcruentum . The twocruentum  in Floreloropsis .2 bio-fixation rates using 1.3 cm thick alveolar flat-panels as light receptor. Gifuni et al. [−2 h−1 was obtained with C. sorokiniana. Furthermore, a hybrid photobioreactor consisting of a bubble column reactor coupled to an illumination platform has recently been reported [−1 was achieved in the reactor with S. obliquus CPCC05 [More recently, several new photobioreactor designs have been reported. For example, Carone et al.  designedi et al.  developereported . The reas CPCC05 . HoweverH. pluvialis; it is foreseeable that these enclosed bioreactors will be continuously used to produce high-value products from microalgae in the future under aseptic conditions. Furthermore, there are several other problems, such as the accumulation of high concentration of O2 in the cultures and difficulties in cleaning. Until these problems are solved, the commercial application of enclosed reactors for microalgae will be challenging.Although higher biomass density can be maintained, the construction and maintenance costs of photobioreactors are ten times higher than those of open ponds, making them uncompetitive for the industrial production of microalgal biomass. Thus, photobioreactors can be used commercially to produce high-value bioactive substances, such as obtaining astaxanthin from Some microalgal species can grow in the dark or under light limitation, using organic carbon  as their sole carbon and energy source, a process known as heterotrophy. Heterotrophic cultivation in fermenters may result in higher cell productivity than that in open ponds and photobioreactors, as this growth mode eliminates the requirement for light. Therefore, this process may provide a cost-effective and large-scale alternative strategy for microalgal biomass production. Fermenters and photobioreactors represent enclosed systems that share many common features, such as pH and temperature control, and the progress in stirring and harvesting. The main differences between fermenters and photobioreactors include their energy source, oxygen supply, and sterility, which may lead to differences in the final biomass production of these two systems. Notably, high cell concentrations also mean the lower downstream process costs. Hence, the focus is now on heterotrophic cultivation of microalgae.Chlorella, and this process was applied to the industrial production of Chlorella in the mid-1990s. Subsequently, Cell Systems  developed a process for the heterotrophic cultivation of T. suecica in 5000 L scale fermenters [Cryptheconidium cohnii was set up at Martek Biosciences in 1990s  [The key to heterotrophic production is that microalgal cultures must be axenic. This issue can be well-solved by drawing on proven techniques in microbial fermentation, for example, sterilization of fermenters and medium can be achieved using steam. Early attempts have been made to develop industrial production processes for microalgal heterotrophic cultivation. For example, studies on heterotrophic production of microalgae began in Japan in the late 1970s for rmenters . In addiia, USA) .Schizochytrium sp. and C. cohnii, respectively, using a two-stage fed-batch process [Chlorella for human nutrition using a proprietary process in which the microalgae are ‘fed’ with carbon source and grown in sterile fermenters; this process is expected to be expanded in the future to produce 1500 tons year−1 of Chlorella biomass. On the other hand, the heterotrophic cultivation processes are also being optimized constantly. Studies have reported increase in the yields of biomass and their by-products in E. gracilis and by C. vulgaris by optimizing complex medium composition and culture conditions, respectively [C. sorokiniana GT-1 and S. acuminatus using a fed-batch strategy; this process has successfully increased the biomass yield of C. sorokiniana GT-1 to 247 g L−1 and S. acuminatus to 283.5 g L−1 in 1000 L pilot-scale fermenters [−1 [C. sorokiniana FZU60, and maximum lutein productivity of 82.50 mg L−1 day−1 was obtained using pulse-feeding with concentrated urea-N medium in a fed-batch culture [During the last two decades, heterotrophic cultivation of microalgae has attracted attention. On one hand, high productivity of heterotrophic production has attracted the interest of microalgae enterprises. This process has been used to produce high-value products. For example, DSM  has commercialized the production of DHA and microalgal oil rich in DHA from two heterotrophic microalga,  process . Duplacoectively , 50. Morrmenters , 20. Not culture . These eChlorella and E. gracilis by Baoshan Zeyuan Co., Ltd .Compared to photoautotrophic culture, heterotrophic production is limited by the lack of intracellular photosynthetically derived compounds. This may lead to the loss of the main advantage and practical application value of microalgae. Few attempts have been made to obtain high cell density with high cellular photosynthetic components. One potential route is mixotrophic culture of microalgae, but the contribution of this process to improving biomass production is limited . OgbonnaCompared to open ponds and photoreactors, heterotrophic production is limited by the number of available heterotrophic algal species. However, as heterotrophic production may further increase productivities in the future after optimization of growth conditions, and owing to the ease in controlling the production process in heterotrophic production systems, heterotrophic cultivation of microalgae is expected to find worldwide application. In addition, the microalgal heterotrophic culture process is very similar to microbial culture technology; hence, it is possible to use proven microbial culture techniques and equipment to achieve heterotrophic mass culture of microalgae, which will considerably accelerate the industrialization of microalgae and their products.Microalgae harvesting is the process of recovering biomass from the culture medium. It represents one of the most important challenges for commercial-scale biomass production. Microalgae grow suspended in water; even the microalgal biomass in heterotrophic culture exceeds 20% dry weight with difficulty. Therefore, harvesting is energy and capital intensive, and can contribute approximately 30% of the total cost of microalgal production. The selection of harvesting methods varies with microalgae, mainly depending on the physiognomies of microalgae, cell density, and the value of the commercialized products from biomass. Currently microalgae harvesting involves mechanical, chemical, electrical, and biological methods. Conventional methods on a commercial scale involve centrifugation, filtration, and flocculation, which can be applied individually or in combination. However, these methods present some economic or technological disadvantages in the actual biomass production process. The comparison of various harvesting techniques is presented in Table Chlorella [Centrifugation is the oldest and most commonly used method of harvesting microalgae from their growth medium. The first known pilot-scale process of harvesting via centrifugation was reported in the early 1950s. Burlew  proposedhlorella . Hydrocyhlorella , 59. Thuhlorella . In addihlorella .Arthrospira sp. and Coelastrum sp. However, microalgae with smaller cell sizes, such as Chlorella sp. and Nannochloropsis sp., tend to clog the filter membrane during filtration, resulting in reduced filtration efficiency. Tangential flow filtration provides a solution for smaller microalgae. It can somewhat alleviate the issue of filter fouling, as the medium flows tangentially across the membrane, maintaining the cells in suspension [Filtration is a physical separation process; this technique allows fluid to flow through a membrane under gravity, pressure, or vacuum force, where microalgae can deposit on the membrane. Compared to other harvesting techniques, filtration can provide high-quality biomass because of low levels of cell disruption and the absence of chemicals in the membrane process. The main drawback of the process is fouling. This phenomenon often increases the flow resistance and reduces the filtration flux. Several filter designs have been developed depending on the hydrodynamic conditions and membrane characteristics . Typicalspension . Filter 3+, Fe3+, Ca2+, and Mg2+) and macromolecule polymers . The process causes aggregation of algal cells due to neutralization or reduction of the negative charge on the surface of the microalgae and/or due to the formation of bridging bonds. A study has shown that cationic polyelectrolytes are more effective than metal salts, with the ability to induce up to 35 times biomass concentrations [Flocculation represents a low-cost harvesting method; this process increases the particle size, reducing the energy requirement in dewatering process , 62. Flotrations . Althougtrations . Bio-flotrations . EPS cantrations . Therefotrations , 69. Thetrations , 71.Other techniques, such as flotation and gravity sedimentation, have also been developed for microalgae harvesting. Flotation involves introduction of air bubbles to transport the suspended matter to the top of the liquid surface, where it can be collected using a skimming process . The tecCurrently, good harvesting methods are lacking, as the major drawbacks of each harvesting technique prevent them from being applied on a large scale in a non-toxic, cost-effective, or energy-efficient manner at the same time. Similarly, no single method or combination of harvesting methods appears to be suitable for all species. Nevertheless, thorough comparative analysis is required to determine the most appropriate harvesting method. These analyses should be based on some of the most critical factors in harvesting technique, such as recovery efficiency, concentration factor, biomass quantity and quality, cost, processing time, toxicity, and suitability for large-scale application , 73. ConA possible approach that may be followed to determine the most appropriate method for each microalga should include the following: (i) the final application of the biomass recovered from microalgae should be clarified; (ii) the most important criteria should be considered for each application; (iii) the most satisfactory harvesting method for each criterion should be considered , 73. SevD. tertiolecta and 245.3 for C. sorokiana were achieved using a combination of these two techniques. More recently, Min et al. [C. vulgaris; the system was evaluated and was found to be less energy intensive than conventional systems. Thus, these studies proved the potential benefits of using multiple methods for microalgae harvesting in terms of recovery efficiency, processing time, and process economics.In many cases, the use of a combination of two or more harvesting methods can lead to further improvements in harvesting efficiency, production cost, and processing time . A typicn et al.  used theDrying is usually as the last harvesting step. This process requires the removal of moisture to ≤ 12% to obtain dry microalgal biomass for downstream product production. The dried microalgae are easy to store and transport, as well as to use in bio-refinery and in the food and feed industry. Drying also represents a significant fraction of the total production costs. Since the mass cultivation of microalgae, several drying techniques have been developed. The commercial techniques mainly include (i) solar drying, (ii) convective drying, (iii) spray drying, and (iv) freeze drying. The different processes have their own distinctive features. The selection of drying method is critical for the subsequent processing and quality of the final products.Solar drying, the most traditional and cost-effective method for microalgal powder production, has been used for hundreds of years to stabilize the moist algal biomass. In some open processes, the heat for water evaporation is provided by solar radiation and moisture removal by natural air currents. This may be time-consuming, and a large drying surface and the efficiency of the process is directly dependent on the weather conditions. Moreover, longer processing times and exposure to open environments may increase the risk of spoilage or development of off-flavors. Several strategies and facilities have been developed to address these issues. Some closed solar dryers can reduce the moisture content of the final product to less than 10% within 5 h of drying and remain low energy and exergy efficient , 78. TheC. vulgaris and measured energy consumption using microwave drying. A study indicated the strong influence of process temperature on chlorophyll a content and hue angle (relative to sample color) under the same conditions of convective drying, with a sharp reduction in chlorophyll concentration at drying temperatures up to 40 °C [Arthrospira; results showed that convection drying temperatures above 45 °C could cause phycocyanin degradation. Therefore, the optimization of convective drying conditions is important for pilot-scale applications. Moreover, studies are required to minimize energy consumption.Convective drying is popularly used for drying microalgae. It is performed in a type of convective hot air dryer and is commonly used in small-scale production. It usually includes draft oven drying, convective tray drying, microwave oven drying, convective tunnel drying, and continuous conveyor belt drying –83. Seveto 40 °C . This fato 40 °C  who evalD. salina biomass produces powders with very low degradation rates of β-carotene and its isomers [On a commercial scale, spray drying is the most commonly applied method. This technique was first proposed in the early 1950s for the production of microalgal powder . Spray d isomers . Green d isomers . The mai isomers . Volatil isomers , 91. HigH. pluvialis powder. As expected, the freeze-dried biomass retained higher amount (~ 30%) of astaxanthin than the spray-dried biomass. The stability of freeze-dried algal biomass may be affected by its extremely high porosity [Freeze drying is another common drying technique used in the food industry. This is a two-step process. The algal slurry is first frozen and transferred into a vacuum chamber, which then provides heat for water sublimation (latent heat of sublimation) via radiation or conduction (hot plate) . Similarporosity . This maporosity . For exaporosity . In contDrying is also an energy and capital-intensive process . The degIn summary, further research is still required to improve not only the drying methods, but also to analyze degradation during storage, especially with respect to the sensitivity of dried microalgal biomass to light, heat, and oxygen, which will ensure supply of high-quality products to consumers. Although many new drying techniques have been developed in recent years, which may be based on the same principles with only some modifications to parameters and equipment, studies on quality assessment for drying microalgae are not common in literature. Different drying methods applied to commercial species with interest in evaluating quality characteristics are summarized in Table The ideal state for microalgal mass cultivation is a production system in which only target microalgae are growing. However, biological contaminants will inevitably enter the cultures, both in open ponds or photobioreactors, which are relatively open systems that require the transfer and exchange of gases between the culture system and the external environment, in addition to the water input. In particular, microporous membrane filtration does not remove viruses from the air, and large volumes of water cannot be treated using conventional microbial fermentation with thermal sterilization. These limitations in production systems are the main cause of the spread of biological contaminants. Therefore, responsive strategies must be adopted to avoid them during mass cultivation of microalgae.So far, many biological pollutants have been reported. These include zooplankton, bacteria, viruses, and other microalgae. The common biological contaminant species and their contamination mechanisms are summarized in Table Brachionus rubens reduced the biomass production of C. sorokiniana by up to 99.8%, leading to the collapse of the algal culture [Zooplanktons are the main cause of culture failure. They usually act as predators of microalgae, reducing algal concentration and production to low levels in few days. The common predatory species in the microalgal mass cultivation are ciliate , rotifer culture . Similar culture . Zooplan culture . Factors culture .Micrococcus, Bacillus, and Planomicrobium [Xanthus sp., Saprospira sp., and Pseudoalteromonas sp. Most phytoplankton-lytic bacteria prefer to attack indirectly. For example, Vibrio sp. have been reported to lyse algae by producing extracellular alga-lysing substances such as β-cyano-l-alanine and some unknown non-proteinaceous substances [Bacillus sp. SY-1 has been shown to lyse a harmful dinoflagellate by secreting a novel alginate [B. fusiformis possess algicidal activity against a wide range of microalgae. Although many algicidal bacteria have been identified so far, further research regarding the algicidal mechanisms of new species and the development of appropriate prevention and control strategies are required.Some bacteria, called phytoplankton-lytic bacteria, can also inhibit the growth of microalgae. Most known algicidal bacteria belong to Bacteroidetes/Cytophaga/Flavobacterium (55%) and γ-Proteobacteria (45%), and others (5%) belong to the Gram-positive genera, icrobium . They lybstances , 133. Moalginate . A similalginate ; the resLynbya sp., Phormidium sp., and Plectonema sp. Subsequently, the first eukaryotic algal virus, CCV virus, was also identified, with specificity for Chara coralline [Viral infection also significantly reduces the concentration of microalgal cells in open ponds within a few days, and their mechanism for infecting living cells is well-known. The short replication cycle and host specificity of viruses suggest that they can rapidly reduce microalgal amounts or cause exceptionally low growth rates. Both prokaryotic cyanobacteria and eukaryotic algae can be infected by viruses , 136. A oralline . Howeveroralline , 126. OwC. vulgaris, may significantly inhibit the growth of Pseudokirchneriella subcapitata [In addition to the above biological contaminants, other microalgae can also inhibit the growth of target microalgae , 122. Thcapitata . These scapitata . ContamiContamination of microalgal cultures is one of the main obstacles currently hindering the development of microalgal biotechnology. Cost-effective strategies must, therefore, be developed to control unpredictable biological contaminants, as the loss of even a single algal reactor in an array can significantly affect the productivity and yield of the entire facility. Several measures have been developed, including filtration, the use of chemical additives, and changes in the environmental conditions . The useChlorella spp. and Tetreselmis spp. [Filtration represents a physical method. Unlike microalgal harvesting, this method for controlling contaminants allows the microalgae to flow through the netting, while the larger biological contaminants remain on the netting. Filtration is considered an effective method of removing larger organisms, such as rotifers and copepods, but not smaller rotifer eggs and developing young individuals . Therefomis spp. . A poten−1 quinine was effective in killing ciliates, with less damage to D. salina cells. In addition, the use of ammonium bicarbonate in culture can control rotifers and cladocerans, and can also provide an additional source of nitrogen and carbon at low cost [Chemical control is a potentially viable method for eliminating biological contaminants. Many studies have reported chemical treatments for controlling biological contamination in open microalgal cultures. For example, Moreno-Garrido and Cañavate  reportedlow cost . Howeverlow cost . An effelow cost . Variouslow cost . For exalow cost , 144. CoGymnodinium catenatum and Alexandrium catenella can be easily killed using temperatures as low as 35 °C and 38 °C, respectively. This has potential application in the treatment of input water for algal cultivation. Adjustment of the pH of cultures is commonly used for killing or removing of biological contaminants [Changes in certain environmental conditions, such as temperature and pH, can also be used to control biological contamination. This strategy depends on the survival conditions of the target microalgae and/or contaminants. Several studies have confirmed the effectiveness of this approach. For example, Hallegraeff et al.  confirmeaminants , 146. Beaminants  recommenaminants .The control of biological contaminants is essential for the production of sufficient and high-quality microalgal biomass. However, a number of key issues still need to be addressed. For example, the application of control strategies also requires comprehensive understanding of the range of adaptation of target microalgae and biological contaminants to ecological factors. Bacterial contamination in open ponds is inevitable; hence, methods of reducing harmful bacteria and increasing beneficial bacteria  is the focus of future research. Moreover, research on algal lysing viruses should be intensified, as current knowledge regarding algal viruses is limited. The other caveats regarding the control of algal toxins in biomass production include the lack of standards for acceptable levels of toxicity in algal biomass or compound feeds. In addition, the development of sensors for monitoring various biological contaminants is also necessary.Arthrospira and Nostoc (cultivated only in China), the green microalgae, Chlorella, Dunaliella, and Haematococcus, and the flagellate, Euglena. Table In microalgal biotechnology, suitable species can be grown as production strains in aquaculture. Although tens of thousands of microalgae exist in nature, only a few gifted strains are used for commercial biomass production. In particular, over the past decade, the bulk of annual biomass production is dominated by six species, namely, the cyanobacteria, Arthrospira (Cyanophyta) is a multicellular filamentous cyanobacterium that grows naturally in subtropical alkaline lakes with an optimum temperature of approximately 35 °C. It represents the most successful commercially available microalga. In productive cultures, two species, A. platensis and A. maxima, were widely cultivated in open raceways or tubular photobioreactors. The first commercial production started in the 1970s in Mexico. Currently, Arthrospira is produced in over twenty countries. The mass cultivation of this cyanobacterium contributes to over 50% of the global microalgal production, with total annual production estimated at about 12,000 metric tons [Arthrospira biomass, thanks to improvements in raceway ponds , a genus of unicellular green microalgae living in freshwater, seawater, and terrestrial habitats [C. vulgaris and C. pyrenoidosa. Currently, it is usually cultivated phototrophically in open ponds, cascades, and enclosed tubulars, as its high growth rate prevents contamination by other microalgae. A circular pond is the most common device for commercial biomass production of Chlorella , mainland China , China Taiwan , Korea (Daesang), Germany (Algomed), and Portugal, with total annual biomass production of about 5000 metric tons [habitats , was thella Fig. c. It canlla Fig. d. Countrric tons .Chlorella industry for human nutrition and animal feed due to its high nutrient content. In recent years, the mass cultivation of Chlorella has also shown its potential for applications, such as bioremediation, biofuel production, and as a raw material for biofertilizers. However, the current production systems and processes of Chlorella are neither cost-effective nor energy-efficient, rendering these potential applications impractical. In particular, the cell processing requires both effective and efficient harvesting and mechanical disruption of cellulose cell walls. Breakthroughs and innovations in the next generation of production technology are, therefore, urgently required.The success of mass cultivation of this microalga photoautotrophically, heterotrophically, and mixotrophically has led to a stable D. salina (Chlorophyta), a unicellular biflagellate green microalga, represents the most salt-tolerant eukaryotic organism. It is one of the most industrially important species of microalgae because of its extremely high β-carotene content, which accounts for up to 16% of the dry matter. The first outdoor pilot of this microalga was attempted in the USSR in 1966; however, mass cultivation on a commercial scale was first achieved in the USA and Australia in the 1980s. Currently, the large production plants are mainly established in Australia  and Israel (Nature Beta Technologies). The biomass production systems mainly include open natural/artificial ponds  is a freshwater unicellular green microalga. The high content of astaxanthin (up to 4% of the dry weight) makes H. pluvialis attractive to biotechnologists for large-scale production in raceway ponds , Japan , Israel (Algatech), China , and India (Bioprex). The total annual worldwide commercial production is estimated to be at 800 metric tons [nds Fig. g or enclnds Fig. h at arouric tons .H. pluvialis is the main producer of natural astaxanthin. This pigment can be used as an anti-oxidant for human nutrition or as a natural colorant for the aquaculture of salmonoid fish. However, the production of astaxanthin is still restricted to that of a few hundred kilos. In fact, the H. pluvialis industry is still in the early stages of industrialization. This is reflected in the small number of companies capable of large-scale production and the lack of derivative products. Further expansion of biomass production will depend on the development of superior strains and a significant increase in the cells’ resistance to environmental stresses, especially fungal diseases.Euglena (Euglenophyta) is the protist genus consisting of unicellular freshwater flagellates. These species can be grown photoautotrophically, heterotrophically, or photoheterotrophically, and have been studied extensively. In particular, E. gracilis has long been used as a model organism. This microalga has attracted the attention of cultivators as it is able to accumulate more than 50% of the dry weight as polysaccharides. The first outdoor pilot of E. gracilis was attempted in Japan in 2005, and the commercial cultivation was started in 2007 by Euglena Co., Ltd. (Japan) [E. gracilis commercially, with an annual production of about 70 metric tons [Euglena have been developed as commercial products. However, the widespread cultivation and commercial application require further optimization of biomass production systems and methods. (Japan) . Circularic tons , 30. TheN. sphaeroides (Cyanophyta) is an edible cyanobacterium with high nutritional value. Wild N. sphaeroides can grow naturally both in terrestrial and aquatic environments. It has been consumed as food in China (Ge-Xian-Mi) and Peru (cushuro) for many years. The first artificial cultivation was successfully achieved in China in 2001 after some progress in breeding, and the commercial indoor production began in Changde  in 2007. Currently, this microalga is cultivated only in China  2015/2283 and the New Resource Food Regulation (EU) 2018/102. EU has established standards for using Arthrospira, Chlorella, H. pluvialis, and D. salina as novel food or additives. Other relevant detections and quality standards are also being proposed. The standards of microalgae products in the USA are mainly in the category of colorants, such as Arthrospira, a colorant, H. pluvialis, a feed colorant, and β-carotene extracted from D. salina, a food colorant. In addition, the standard of β-carotene as an additive of pharmaceuticals and cosmetics has also been established in the USA. It is expected that in the next 5–10 years, a comprehensive standard system including safety, quality, and production technology specifications will be built gradually.As the world’s largest producer of microalgae, China is gradually establishing complete standards for microalgae and related products. Among them, the quality and safety standards for the use of microalgal powder as food or feed, such as those from As microalgal biotechnology continues to improve and algal bioproducts become more widely used, existing standards can no longer meet the needs of the rapidly developing algal industry. Therefore, the establishment and improvement of microalgae-related standards is essential. According to the current status of standardization in the microalgae industry, the construction of the standardization system should focus on the following aspects: (i) research and development of rapid detection technologies for algal active compounds should be intensified to provide microalgae producers with simple and reliable testing methods; (ii) Timely revision of relevant industry standards to meet the development needs of new species and products of microalgae. (iii) Continuous strengthening of the function of industry associations, highlighting the importance of group standards, and stimulating the vitality of market players; (iv) International cooperation should be strengthened to promote the development of international common standards.Microalgal biotechnology is limited by a few algal strains available, which indirectly reduces the diversity of commercial products. Therefore, breeding techniques should be developed to screen for high-quality algal strains that can be used for mass production.High cell-density culture techniques are key for reducing costs, although there is scarcity of available production facilities. Similarly, culture conditions also have to be optimized to further increase productivity.Quality control is an important direction for the future development of biomass production technology, in particular, the need to avoid the loss of active ingredients from microalgae under inappropriate conditions.The main obstacle impeding the control of biological contaminants is the lack of information regarding the biochemical pathways of contamination. The prevention of biological contaminants and development of control technologies that are both economically efficient and environmentally friendly are top priorities. The development of techniques for the detection of contaminants or toxicity factors in cultures and standards for determining the acceptable levels of toxicity in algal biomass are also necessary.The functional and molecular mechanisms of action of the microalgal active ingredients are not completely understood, which to some extent, affects the market positioning of microalgae and development of new applications.Innovative strategies are obligatory, which would help realize some potential applications of microalgae, such as genetic modification , to increase oil content and render microalgal biofuels commercially viable.The untapped bioeconomic potential of microalgae should guide the exploration of the vast undiscovered possibilities of these biotechnologically important species, such as microalgal power generation.This review systematically summarizes current biomass production technologies for commercial microalgae. We concluded that high cell-density cultivation process is important for producing biomass on commercial scale in the future, and that cost-effective processes and strategies are required for the development of microalgal harvesting. Moreover, microalgal drying should not only be cost-effective, but should also consider the quality of the product, while basic research into the control of biological contaminants should be strengthened. The article then briefly reviews the current status of commercial production of some biotechnologically important microalgae and highlights the importance of improving microalgal industry standards. In summary, it is clear that before the wider application of microalgal biomass can be achieved, significant investments in technology development and technical expertise will be required.


The metabolism of all living organisms is dependent on temperature, and therefore, having a good method to predict temperature effects at a system level is of importance. A recently developed Bayesian computational framework for enzyme and temperature constrained genome-scale models (etcGEM) predicts the temperature dependence of an organism’s metabolic network from thermodynamic properties of the metabolic enzymes, markedly expanding the scope and applicability of constraint-based metabolic modelling. Here, we show that the Bayesian calculation method for inferring parameters for an etcGEM is unstable and unable to estimate the posterior distribution. The Bayesian calculation method assumes that the posterior distribution is unimodal, and thus fails due to the multimodality of the problem. To remedy this problem, we developed an evolutionary algorithm which is able to obtain a diversity of solutions in this multimodal parameter space. We quantified the phenotypic consequences on six metabolic network signature reactions of the different parameter solutions resulting from use of the evolutionary algorithm. While two of these reactions showed little phenotypic variation between the solutions, the remainder displayed huge variation in flux-carrying capacity. This result indicates that the model is under-determined given current experimental data and that more data is required to narrow down the model predictions. Finally, we made improvements to the software to reduce the running time of the parameter set evaluations by a factor of 8.5, allowing for obtaining results faster and with less computational resources. The effect of temperature on the behaviour of microorganisms as a whole is evident. Freezing food stops spoilage by inactivating microorganisms, whereas cooking kills them. In between these temperature extremes, there are observable effects which can be utilized commercially. One example of this is yeast production of aroma compounds, which has been shown to depend on temperature4, a finding with potentially great impact on wine and beer brewing.Temperature is a key effector of life, which is partially due to the consequence that temperature has on catalytic properties of enzymes. For a long time, it has been known that enzymatic reactions slow down at low temperatures, whereas high temperatures destroy the enzymes, rendering them non-functional. In recent research5 came up with an extension of an enzyme-constrained genome-scale metabolic model (ecGEM) which can capture the temperature dependence of metabolism. This model is thus called an enzyme and temperature constrained GEM (etcGEM). As most other models, the one by Li et al.5 is based on sets of assumptions and parameters. In particular, this model is based on model ecYeast7.66 (Saccharomyces cerevisiae strain S288C) and contains 764 metabolic enzymes and 2, 292 parameters associated with enzymatic activity. For each of the enzymes, the following parameters must be determined: (1) Until recently, no attempt has been made to computationally explain the temperature dependence of microorganismal phenotypes by propagating the temperature dependence of metabolic enzymes to the entire metabolic network of an organism. However, Gang Li et al.T could be estimated5. These temperature-dependent maximal catalytic rates were then fed into the enzyme-constrained genome-scale model7, and the metabolic flux rates were predicted using Flux Balance Analysis (FBA)9. Furthermore, Li and coworkers5 used a Bayesian approach to infer the enzymatic parameters mentioned above from sets of training data: (1) Maximal growth rates in aerobic batch cultivations10; (2) Maximal growth rates in anaerobic batch cultivations11; (3) Chemostat cultivations which include measurements of exchange fluxes of carbon dioxide, ethanol and glucose12.Given these parameters, it was demonstrated how an enzyme’s maximum catalytic rate In the training data, the experimentally determined exchange fluxes were recorded for a range of temperatures, thus generating a set of growth scenarios. The performance of a parameter set was assessed by predicting the flux rates in these growth scenarios and comparing these fluxes rates with the experimental results. Hence, the 5 did this through a custom heuristic which was partially based on measured temperature optima and denaturation temperatures for enzymes. By training the model with the Sequential Monte Carlo based Approximate Bayesian calculation method, an estimate of the posterior distribution of parameter sets was found5. However, Li et al. did not systematically investigate the stability of this calculation method, nor whether it suffered from identifiability issues. Thus, it is unclear how metabolic flux results from the etcGEM can be interpreted.For defining the Bayesian model, prior parameters have to be chosen for the enzymes. Li et al.In this paper, we investigated the stability of the Approximate Bayesian calculation method algorithm by choosing multiple different random seeds over different priors. We found that the Bayesian calculation method is inherently numerically unstable, and thus, is unable to provide reliable results given its model assumptions. To rectify this problem, we implemented an evolutionary search algorithm that is not built upon any assumption of structure of the underlying data. Finally, we improved the execution time of the software package by a factor 8.5, making it feasible to execute on smaller-scale computational infrastructures. However, for the available data there is an identifiability problem, in which solutions that equally match the experimental data still differ in terms of fluxes through key metabolic reactions. We believe that the evolutionary algorithm will resolve the identifiability problem if more experimental data, in particular data regarding internal fluxes, are included.13 internal routines to modify metabolic models prior to solving them. Hence, preparing the models for optimization consumed far more time than the optimization proper. For this reason, we modified the implementation to use the ReFramed package (https://github.com/cdanielmachado/reframed) for handling the genome-scale model. We benchmarked the two versions on a computer running Intel Core i7-8565U using a single core  using the implementation from Li et al. Profiling showed that the particle evaluation procedure (see Methods) was the performance bottleneck, and excess time consumption was caused by COBRApy’s5, we created three randomized priors by permutation (see Methods for details) and repeated the process of assessing stability given these priors. Thus in total, we ran the Bayesian calculation method eight times, yielding eight different populations of estimated posterior distributions. The permuted priors yielded approximately the same rate of fitness convergence as the unpermuted priors  plot of the parameter sets obtained under estimation Growth: The growth (biomass) reaction is included for comparison with the other reactions. The calculated growth should ideally be identical to the experimental ones, but some deviations occurred because the posterior particles did not in general provide a perfect fit to the data.We chose to focus on three simulations; Bayesian simulation 1 and 2 with original priors and Bayesian simulation 1 with permuted prior set 1 no oxygen available to be consumed. We also ran FVA on the results omitting the chemostat dataset when running the Bayesian calculation method . Our choice of an evolutionary method was motivated by how an evolutionary algorithm searches the parameter space and its ability to combine existing solutions to create improved solutions23. CrowdingDE has two major hyperparameters, the scaling factor F and the crossover probability CR which both determine how crossover between individuals is done.Given the instability of Bayesian calculation method and its inability to cope with multimodal fitness landscapes, we constructed an evolutionary algorithmFor testing the performance of the evolutionary algorithm, we used the previously mentioned toy example with the same priors. As for the Bayesian calculation method, the population size was set to 32 and four replicate simulations were run for 200 generations. The scaling factor was set to 0.5, the crossover probability was 0.5 and 16 new children were born per generation  was also adjusted according to the temperature. Further details are published in Li et al.5.A parameter set, referred to as a particle, is a collection of the three parameters For the aerobic and anaerobic datasets, the model’s temperature-dependent parameters were tuned, and fluxes were predicted by Flux Balance Analysis (FBA) calculations at the specific temperatures, and the biomass (growth) function was set as the objective. For each of the aerobic and anaerobic datasets, the growth rates predicted by the model were compared with the experimental growth rates, and an For the chemostat dataset, the procedure was somewhat different. Here, the temperature-dependent parameters were tuned (as with the other datasets) before the growth rate of the model was locked to the dilution rate of the chemostat. Thereafter, the model was optimized for minimum glucose uptake, and this uptake flux value was set as a constraint for the model. Finally, the model was optimized for minimum protein pool usage, and exchange fluxes of ethanol, carbon dioxide, and glucose were recorded. Once the three fluxes for all temperatures in the chemostat dataset were recorded, these values were compared to the experimental ones, and The overall 13 in order to reduce overhead related to modifying models. However, the results generated by our modified particle evaluation approach should be identical to the results generated by the original code by Li et al. for all simulations.We optimized the evaluation procedure to use the ReFramed package instead of COBRApy5, and we used the same hyperparameters as in the original publication. For seeding the calculation method, priors were needed for the values of The framework and code for the Sequential Monte Carlo based Approximate Bayesian calculation method was taken directly from Li et al.21 algorithm and was written from the ground up. Individuals in the evolutionary process were the parameter set particles discussed earlier. The population size for each iteration (generation) was set to 128. The initial 128 individuals of the population were generated by sampling from the same priors as those used by Li et al. The algorithm was run for 1000 generations. Each generation consisted of the following steps carried out in sequence:Generation of children: At the beginning of each generation, 64 children were created as a weighted difference of parent individuals. For each child, three parents M denotes the total number of enzyme parameters. For crossover, a random integer j was thereafter initiated to zero and the following procedure was repeated: A random number CR is referred to as the crossover probability, crossover was cancelled and the algorithm advanced to commence the generation of the next child. Otherwise, crossover was performed on parameter andj was incremented by one and the procedure was repeated for the next enzyme parameter. Updates to enzyme parameters which violated the constraints Evaluation of children: Each child particle was evaluated through the same procedure as mentioned above and their respective Replacement: For each child a generated in the same generation, the normalized parameter-space distance from the child to the individuals in the current population was calculated: k for the two individuals, M is the total number of enzyme parameters and k in the population at the end of the previous generation. The closest individual b to the child a was then chosen as: a had a higher fitness than b, i.e. b was discarded from the population and replaced with a. Otherwise, b was kept in the population and the child a was discarded.The two tuning parameters F  and CR (crossover probability) were tested with values The evolutionary algorithm used in this paper for fitting enzyme parameters is based on the existing CrowdingDE35 to create ordinations of particles. The values for each parameter were standardized, subtracting the mean and dividing by the population standard deviation before ordination. The means and standard deviations were computed across all particles present in the ordination in question. As a result, the presented ordinations in Fig. We used Principal Component Analyses (PCA) from scikit-learn version 1.0).035 to c15 was conducted on particles having Flux variability analysis (FVA)All flux ranges and flux midpoints with the same combination of simulation, reaction, dataset, and temperature were aggregated to give the mean, minimum and maximum values. Usually, there were 20 such values for each combination, as 20 particles were sampled for each of these combinations. However, this number could be smaller due to removal of missing and infinite values.36 was conducted on the particles from the evolutionary algorithm with Agglomerative hierarchical clusteringSupplementary Information.


Clostridium ljungdahlii that correlate with the production of isopropanol. To this extent, we integrated a metabolic model in comparison with proteomics measurements and quantified the uncertainty for a variety of pathway targets needed to improve the bioproduction of isopropanol. Based on in silico thermodynamic optimization, minimal protein requirement analysis, and ensemble modeling-based robustness analysis, we identified the top two significant flux control sites, i.e., acetoacetyl-coenzyme A (CoA) transferase (AACT) and acetoacetate decarboxylase (AADC), overexpression of which could lead to increased isopropanol production. Our predictions directed iterative pathway construction, which enabled a 2.8-fold increase in isopropanol production compared to the initial version. The engineered strain was further tested under gas-fermenting mixotrophic conditions, where more than 4 g/L isopropanol was produced when CO, CO2, and fructose were provided as the substrates. In a bioreactor environment sparging with CO, CO2, and H2 only, the strain produced 2.4 g/L isopropanol. Our work highlighted that the gas-fermenting chasses can be fine-tuned for high-yield bioproduction by directed and elaborative pathway engineering.Rational engineering of gas-fermenting bacteria for high yields of bioproducts is vital for a sustainable bioeconomy. It will allow the microbial chassis to renewably valorize natural resources from carbon oxides, hydrogen, and/or lignocellulosic feedstocks more efficiently. To date, rational design of gas-fermenting bacteria such as changing the expression levels of individual enzymes to obtain the desired pathway flux is challenging, because pathway design must follow a verifiable metabolic blueprint indicating where interventions should be executed. Based on recent advances in constraint-based thermodynamic and kinetic models, we identify key enzymes in the gas-fermenting acetogen IMPORTANCE Highly efficient bioproduction from gaseous substrates  will require systematic optimization of the host microbes. To date, the rational redesign of gas-fermenting bacteria is still in its infancy, due in part to the lack of quantitative and precise metabolic knowledge that can direct strain engineering. Here, we provide a case study by engineering isopropanol production in gas-fermenting Clostridium ljungdahlii. We demonstrate that a modeling approach based on the thermodynamic and kinetic analysis at the pathway level can provide actionable insights into strain engineering for optimal bioproduction. This approach may pave the way for iterative microbe redesign for the conversion of renewable gaseous feedstocks. This , 2. Thide (CO2) . Couplinneration , 5 will neration .2/CO/CO2 to acetyl-coenzyme A (CoA), the primary building block for the biosynthesis of acetate and other fermentation products. The WLP in acetogens can work individually fixing CO2, leading to carbon-negative bioconversion, or it can coordinate with the glycolytic pathway for syngas-sugar coutilization with minimal CO2 release (9–Acetogenic bacteria are being developed as capable chassis organisms for syngas utilization due to their innate Wood-Ljungdahl pathway (WLP), which converts H release , 8. Thus release , 9–12. Nelease 9–. To addrState-of-the-art rational engineering assisted by constraint-based metabolic modeling has enabled the development of a variety of microbial chasses that host engineered pathways for novel bioproducts , 15. MetClostridium ljungdahlii’s metabolism. The focus of this research is to apply a proof-of-concept application of PathParser to quantify the uncertainties in pathway control that are needed to improve IPA production. We leveraged the proteomics data collected in strain design experiments and tested if our methodology can indeed predict the outcome of strain engineering. The modeling prediction as well as systems biology analysis enabled iterative construction of Clostridium ljungdahlii strains which eventually produced 4.4 and 2.4 g/L IPA under mixotrophic and autotrophic conditions, respectively. It is evidenced that our approach can distill key information in kinetic and thermodynamic parameters and provide executable insights into guiding strain engineering. Given that IPA is among the top blendstocks for turbocharged gasoline engines and has been known to be a drop-in gasoline additive, our work explicitly highlighted the potential and feasibility of rational metabolic redesign and optimization in syngas-fermenting bacteria for biofuel overproduction.In this study, we used a previously developed computational tool  to evalude novo IPA synthesis from syngas with the standard Gibbs energy changes, ΔG′0  derived from the eQuilibrator database (2/CO/CO2). The strongest driving force in IPA synthesis is generated by methylenetetrahydrofolate reductase of the WLP (blue box), whereas the weakest driving force lies in the reactions catalyzed by ACAT and AACT (yellow boxes), which need to be engineered for IPA production. It is noteworthy that net pathway flux is fundamentally controlled by metabolite concentrations according to the second law of thermodynamics: ΔG′ = ΔG′0 + RT ln Q, where Q is the reaction quotient . During the optimization of overall pathway driving force, we found that the concentrations of acetyl-CoA and acetate reached the preset upper bound of 10 mM , acetoacetyl-CoA transferase (AACT), acetoacetate decarboxylase (AADC), and secondary alcohol dehydrogenase (SADH), acting in sequence see  19). Ho. Ho19). database . The thedatabase  shows mu10.1128/msystems.01274-22.1TABLE S1C. ljungdahlii. Download Table S1, PDF file, 0.03 MB.Solution of metabolite concentrations obtained in the max-min driving force (MDF) optimization of the Wood-Ljungdahl pathway (WLP) for IPA biosynthesis in Copyright © 2023 Lo et al.2023Lo et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the In silico thermodynamic analysis of the IPA pathway rationalized in vivo implementation of pathway construction. To express the pathway, we constructed a plasmid pIPAv1 which contained a codon-optimized atoB from Escherichia coli and the ctfAB and aadc genes from Clostridium acetobutylicum  as it converts 5-FOA to a toxic 5-fluorouridine monophosphate (5-FUMP). Disruption of pyrE will lead to 5-FOA resistance and thus can be used for selection of genome integration . 5-FOA selection further allowed us to obtain several 5-FOA-resistant mutants, indicating that the IPA pathway has been integrated into the genome. This was further confirmed by PCR analysis and DNA resequencing using primers that flanked the pyrE locus  were searched in the eQuilibrator database; Michaelis constants (mK), catalytic rate constants (kcat), and enzyme molecular weight (MW) were collected from the BRENDA database. If no data were available, default values of 200 s−1, 0.2 mM, and 40 kDa were assigned for kcat, mK, and MW, respectively. Note 1: The mk of AACT to acetate is quoted for ctfAB  and atoAD (53.1 mM) as shown in the brackets. Download Table S2, PDF file, 0.2 MB.Parameters for thermodynamics and enzyme protein cost analysis of the WLP-based IPA biosynthesis pathway in Copyright © 2023 Lo et al.2023Lo et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/msystems.01274-22.3TABLE S3C. ljungdahlii. Standard Gibbs free energies (ΔG′m) were searched in the eQuilibrator database; Michaelis constants (mK), catalytic rate constants (kcat) and enzyme molecular weight (MW) were collected from the BRENDA database. If no data were available, default values of 200 s−1, 0.2 mM, and 40 kDa were assigned for kcat, mK, and MW, respectively. Download Table S3, PDF file, 0.1 MB.Parameters for thermodynamics and enzyme protein cost analysis of the Embden-Meyerhof-Parnas (EMP) pathway-based IPA biosynthesis pathway in Copyright © 2023 Lo et al.2023Lo et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/msystems.01274-22.4TABLE S42 to IPA through the Wood-Ljungdahl pathway. The enzyme amounts (g) are normalized to IPA flux (mol/s). Shown in columns 2 and 3 are the outcomes when ctfAB (C. acetobutylicum) and atoAD (E. coli source) are selected for aact, respectively. Other input of enzyme kinetics is as same as shown in Table S4, PDF file, 0.09 MB.Predicted enzyme protein cost for the conversion of COCopyright © 2023 Lo et al.2023Lo et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 2-fixing capabilities but produce less energetic currency ATP as a coproduct. The minimal enzyme protein requirement per unit IPA flux corelates with the ratio of WLP to glycolytic flux. Specifically, the larger amounts of enzymes will be needed when IPA is derived more from the WLP. This outcome aligns with the fact that rate-determining steps in the WLP would need higher enzyme quantities to compensate for the less efficient catalysis. Such a case was mirrored in AcsB and Fhs, where input kcat 2.1 and 1.4, respectively, led to 2.0 × 106 g/(mol s−1) of protein requirement, the top two among the WLP enzymes when the WLP dominates the flux for IPA synthesis . Interestingly, this protein cost analysis corroborated the proteome of C. ljungdahlii, in which AcsB and Fhs were also shown to be the most abundant proteins in the WLP  of protein, in which AACT accounts for 18.7 to 32.9% of total pathway protein cost, while AADC accounts for 18.4 to 32.4% of the pathway enzyme protein cost, followed by another heterologous enzyme (ACAT). The lower requirement of the ACAT enzyme than AACT and AADC is probably due to its better kinetic properties. We further compared the outcomes with the proteome data collected from the pathway host  for acetate than ctfAB  and has been shown to yield higher IPA titer in other microbial chasses   the atoAD overexpression is inferior to the ctfAB overexpression. Despite a lower mK of atoAD for acetate, which may lead to a lower enzyme amount for catalysis, the acetogenic host can maintain acetate at a high level, thus satisfying optimal working conditions for ctfAB. A much higher acetate concentration presumably resulted in competitive substrate inhibition as reported for atoAD (The fermentation data showed that the construct with (<1 g/L) . These ror atoAD .C. ljungdahlii can be directed by thermodynamic and kinetic models.To verify the dosage increase of pathway enzymes, another set of proteomics data was generated on the baseline IPA strain with the overexpression of CtfAB and AADC , IPA_ctfC. ljungdahlii IPA v3-2 strain with 20 g/L fructose supplemented with a syngas mixture containing 50%/30%/20% CO/CO2/H2 in the headspace. Cells were grown for 7 days, and products were analyzed. Mixotrophic growth produced a good number of cells, with the highest optical density of 3.48 achieved at day 3 .Next, we found that the rationally engineered strain (IPAv3-2) is metabolically flexible in various environments and may have exceptional IPA production potentials, for example, under the mixotrophic and autotrophic growth condition. Mixotrophic growth by acetogens allows for improved carbon conversion due to their ability to coutilize both sugars and gaseous carbon simultaneously . We inveat day 3 . Also, mC. ljungdahlii (2/H2: 70%/20%/10%), the fermentation results were recorded daily and are shown in max) of 39.7 mM (2.39 g/L). In terms of carbon partitioning, IPA accounted for up to 29% of carbon molarity of all products. This value was defined as the maximum carbon proportion during the fermentation (C-Proportionmax). In addition, acetate production continued over time and reached its highest level  at the end of fermentation. This result is in line with the fact that acetogenic bacteria require steady acetate production to compensate for the bioenergetics limit. Interestingly, we also observed a significant amount of ethanol being produced in the process . This phenotype was not typically consistent with a native C. ljungdahlii strain in which ethanol was only moderately generated as a secondary product. Our results imply that the redox status might have been changed substantially in the engineered strain. Ethanol production may have been selected to balance electron flow and carbon metabolism. Conclusively speaking, although strain development will be further pursued, our rational design has provided a solid platform for converting syngas to value-added alcohols.To further investigate the rationally engineered strain in a syngas-fermenting environment, we inoculated the IPA v3-2 into YT medium (containing Yeast extract and Tryptone but without fructose feeding) and performed a fermentation experiment in a 2-L bioreactor. This gas fermentation system has recently achieved a high-titer production of nonnative 3-hydroxybutyrate from ngdahlii . SpargedClostridium autoethanogenum, to achieve carbon-negative production of acetone and IPA at industrially relevant selectivity and scale (in vitro prototyping and rapid optimization of biosynthetic enzymes), which were particularly useful in screening candidate enzymes for pathway optimization. In comparison, we adopted a model-driven strain redesign approach for rational engineering, which allowed for significant improvements in our iterative IPA-producing strains. In this study, we used thermodynamics analysis, enzyme protein cost analysis, and proteomics to inform strain redesigns and optimizations for increasing IPA production in the gas-fermenting organism C. ljungdahlii. A key finding in this contribution is that we predicted that AACT and AADC are the two key IPA rate-limiting reactions along the central metabolism and the IPA pathway. Overexpression of these enzymes indeed leads to an increase in the downstream IPA flux. These studies indicate that our developed computational workflow for the integration of thermodynamics, enzyme kinetics, and ensemble modeling for C. ljungdahlii is a valuable method to pinpoint key enzyme expressions for the optimization of the IPA flux. Currently, this work solely considered IPA and the primary pathways in the central metabolism of the acetogen . Given a simplified central metabolic pathway, we can obtain reasonable flux control coefficients (FCCs) that actively increase the flux of the product IPA. Probing how they all interact at the genome-scale level will provide more information for realistic future strain designs that are physically capable of growing under real conditions. The insights gained from this relatively smaller model provide us with the ability and understanding to expand further onto a genome-scale metabolic model for a much broader analysis of how FCCs could affect the production of IPA in the context of the entire genome.Gas fermentation for the production of nonnative chemicals has received significant attention in recent years. This has led to remarkable progress in strain development , 12, butAlso, as a next step, we are interested in developing more effective genome engineering tools that can precisely and quantitatively modify the target pathway enzyme levels. Comparing the optimization goal provided by computational modeling and realistic enzyme expression levels in the -omics data sets  problem which seeks to maximize the Gibbs energy change, ΔG′, of the most thermodynamically unfavorable reaction by tuning the concentrations of intermediates. The problem is defined as maxlvin denotes influx to a pathway, MW is the enzyme molecular weight, and E is the expression of the reaction enzyme level derived from a common modular rate law rate law  and Haldrate law .m) were searched in the eQuilibrator database (m of the fructose phosphotransferase (PTS) system was estimated using equilibrator-api (mK), catalytic rate constants (kcat), and enzyme molecular weights (MW) of C. ljungdahlii, Moorella thermoacetica, Acetobacterium woodii, Clostridium formicoaceticum  with respect to E equals zero too, which yields The above-described differential equations are solved along with up- and downregulation of enzyme levels until system failure happens, and the number of models is counted to calculate the probability of system failure for each enzyme at various enzyme levels. When solving the equations, metabolite concentrations will be updated, and pathway fluxes can be reestimated accordingly, responding to enzyme perturbations. Robustness and flux response analysis of native WLP and the IPA pathway were also performed using PathParser  with an Strains, media, and chemicals.C. ljungdahlii DSM 13528 was purchased from the Leibniz Institute DSMZ . The YTF rich medium, consisting of 10 g L−1 Bacto yeast extract, 16 g L−1 Bacto tryptone, 4 g L−1 NaCl, 5 g L−1 fructose, and 0.5 g L−1 cysteine-HCl, and the defined PETC medium  were utilized for growing C. ljungdahlii. Cell growth was monitored at 600 nm with a DU 800 spectrophotometer . All chemical reagents used in the growth studies were purchased from Sigma-Aldrich, except Bacto yeast extract and tryptone, which were purchased from Becton, Dickinson.Escherichia coli NEB 10-beta were from New England Biolabs (NEB). Plasmid pMTL80000 series modular plasmids were from Chain Biotech  and were used to generate the constructs transformed into C. ljungdahlii. Plasmids were generated using previously established protocols  and then resuspended in SMP buffer with 10% dimethyl sulfoxide (DMSO) and stored at −80°C until being used for transformation. Electroporation was performed in a Coy anaerobic chamber with a Gene Pulser Xcell Bio-Rad electroporator  with the following settings: in a 1-mm cuvette, 25 μL of cells were mixed with 2 to 5 μg of DNA, pulsed at 625 kV with resistance at 600 Ω and a capacitance of 25 μF. Cells were then resuspended in 5 mL of YTF and recovered overnight at 37°C. Cells were plated in 1.5% agar YTF with thiamphenicol (Tm) at a concentration of 10 μg/mL. Integration of the IPA pathway into the pyrE locus was based on previously established methods (Transformation was based on previously reported protocols . Briefly methods . Briefly3 (for rough pH maintenance) using a 250-mL Duran pressure plus bottle  as the culture vessel. A syngas mixture containing 50%/30%/20% CO/CO2/H2 was added at 6 lb/in2 to mixotrophic bottles. Optical density and high-performance liquid chromatography (HPLC) samples were taken daily. The syngas mixture was refilled as necessary every day to maintain 6 lb/in2 for the mixotrophic conditions.Our rationally engineered strain was initially grown in 50 mL YTF plus Tm seed cultures overnight. After overnight growth, cells were inoculated into 50 mL YTF plus Tm plus 1 g/L CaCO2 with a syngas mix of 70%/20%/10% CO/CO2/H2. Seed cultures were left to grow at 37°C with shaking at 250 rpm until all the syngas was consumed. IPA production was verified during this growth by high-performance liquid chromatography (HPLC). Once sufficient syngas had been consumed, the seed cultures were transferred into the 2-L bioreactor. The 2-L bioreactor contained 1.7 L YT medium with thiamphenicol and was sparged with a constant syngas mix (70%/20%/10% CO/CO2/H2) at a rate of 300 sccm (standard cubic centimeter per minute). The pH was controlled to 5.2 with an initial stirring speed of 300 rpm. This was increased gradually to 900 rpm as the OD increased during growth. The fermentation results were recorded daily by OD at 600 nm (OD600) measurement and HPLC analysis.Seed cultures were prepared for the bioreactor by adding 10 mL overnight growth of the IPA v3.2 strain to 50 mL YT medium containing thiamphenicol in a 250-mL pressure plus bottle and pressurized to 8 lb/in2SO4 mobile phase.Fermentation liquid samples of 150 μL were extracted by syringe, filtered using Costar Spin-X 0.45-μm filters , and stored at −20°C until the experiments were completed. Fermentation products in the liquid phase  were measured by HPLC on a 1,200 series Agilent device  with an Aminex HPX-87H column using a Micro Guard Cation H cartridge at 55°C with 4 mM H18 capillary column coupled to a Thermo LTQ Orbitrap mass spectrometer . The peptide identity was analyzed at a resolution of 30,000. Dynamic exclusion was enabled in this case with the setup of a repeat count of 1, repeat duration of 30 s, and exclusion duration of 90 s. The peptide identity was obtained by searching the tandem MS spectra using PatternLab for proteomics (The proteomic analysis of wild-type and IPA strains was conducted following the same method as in our previous report . First, oteomics . Proteinoteomics  was usedoteomics  with the


Red blood cells (RBCs) are abundant , yet relatively simple, as they lack nuclei and organelles, including mitochondria. Since the earliest days of biochemistry, the accessibility of blood and RBCs made them an ideal matrix for the characterization of metabolism. Because of this, investigations into RBC metabolism are of extreme relevance for research and diagnostic purposes in scientific and clinical endeavors. The relative simplicity of RBCs has made them an eligible model for the development of reconstruction maps of eukaryotic cell metabolism since the early days of systems biology. Computational models hold the potential to deepen knowledge of RBC metabolism, but also and foremost to predict in silico RBC metabolic behaviors in response to environmental stimuli. Here, we review now classic concepts on RBC metabolism, prior work in systems biology of unicellular organisms, and how this work paved the way for the development of reconstruction models of RBC metabolism. Translationally, we discuss how the fields of metabolomics and systems biology have generated evidence to advance our understanding of the RBC storage lesion, a process of decline in storage quality that impacts over a hundred million blood units transfused every year. Approximately 25 out of 30 trillion cells in the human body are red blood cells (RBCs), which make up for >99% of the corpuscular blood fraction . RBCs caWhile the primary purpose of an RBC is to carry oxygen, RBCs contain more than 77 active transporters that can carry metabolites to and from tissues, making them a vehicle for studying diverse physiological processes ,6. As suL. lactis) [Despite the apparent simplicity, proteomics studies of RBCs have determined that the residual <10% of the RBC dry weight beyond hemoglobin is made up of up to 3000 proteins  , with ma lactis) . These oUnderstanding RBC metabolism has also significant applications in transfusion medicine. Over 110 million units of packed RBCs are transfused each year into patients worldwide, making blood transfusion the most common iatrogenic intervention after vaccination . During This review will proceed by discussing relevant RBC metabolic processes, prior work in systems biology in unicellular organisms and RBCs, prior work in deep learning on RBC morphology and metabolic network reconstruction, personalized medicine and systems biology, and potential directions for future work around the area of systems biology of RBCs and personalized transfusion medicine .Ten main metabolic pathways work in concert within a RBC . A colleThe Rapoport–Luebering (RL) shunt is a pathway branching off glycolysis at the triose phosphate step to generate 2,3-diphosphoglycerate (DPG) . By stabThe pentose phosphate pathway  producesMost importantly, NADPH is critical for the conversion of oxidized glutathione to its reduced form  values in each system. In turn, these parameters help dictate the physiological behavior of a system. However, the concept of “one-model-fits-all” is now being refined in light of the results emerging from high-throughput omics data, pushing systems biology into the arena of personalized medicine. Personalized medicine aims to tailor treatments based on individual patients’ biology. By accounting for interpatient variability, personalized medicine aims to improve the outcomes in complex clinical situations [Systems biology combines mathematical models of biological systems executed with computational techniques to create models of biological systems executed in silico . Models tuations . ThroughSystems biology approaches have been previously leveraged to map and analyze metabolic networks in both unicellular organisms and RBCs, demonstrating the versatility of systems biology techniques used to study relatively simple systems ,68,69,70L. lactis produces lactic acid from glucose or lactose via the Embden–Meyerhof glycolytic pathway. One finds glucose, phosphoenolpyruvate (PEP), glucose 6-phosphate (G6P), pyruvate, fructose 1,6-bisphosphate (FBP), 3-phosphoglycerate (3-PGA), and lactate on this pathway [L. lactis produces lactic acid during periods of glucose abundance and, crucially, can survive periods of glucose absence before restarting lactate production. Voit et al. asked how L. lactis does this. They approached this question from two angles. First, using in vivo nuclear magnetic resonance (NMR) spectroscopy of non-growing cells, they monitored the accumulations and depletions of glucose, lactic acid, and intermediates at 30 s intervals after adding a pulse of carbon-13-labeled glucose. Second, they simulated the system with a generalized mass action (GMA) representation of all metabolites along the kinetic pathway. L. lactis uses PEP to phosphorylate glucose to G6P. Hence, they reasoned that if the PEP pool was wholly depleted, lactic acid production could not restart when glucose became available again. Consistent with this reasoning, the NMR observations and simulations showed a pool of PEP built up after glucose depletion. G6P could be produced when glucose was re-introduced to the cells. They performed two more tests (one each with the model and NMR) to test whether this PEP pool found in the experiment and simulation would restart glycolysis. First, they added a second pulse of carbon-13 glucose to the NMR experiment 23 min after the first. Second, they added a second pulse of carbon-13 glucose in the simulation. In both cases, they observed that lactate would accumulate again after the re-introduction of glucose to the system. Taken together, the experiments and simulations pointed to a regulatory system managing the pool of PEP. pathway . L. lactTo explain the accumulation of PEP during periods of glucose starvation, they hypothesized that pyruvate kinase (PK), an enzyme on the pathway, would no longer be activated by FBP and that phosphate accumulation would inhibit PK also. Therefore, PK could no longer turnover PEP to pyruvate. Thus, they reasoned that PEP would accumulate in the pathway. To validate this reasoning, they simulated another model incorporating regulatory feedback  of glucose to pyruvate. The advantage of the simpler model is that it was optimized to study regulation in metabolic networks. When they executed this simulation, they found that PEP would accumulate when the supply of glucose was interrupted. Thus, by using simulation alongside experiments, they tested and validated their hypothesis.L. lactis example above is an example of a simple model of a simple biochemical network in a prokaryotic organism, models can be expanded in terms of scale of metabolic reconstruction and the complexity of the organisms they model. In terms of model complexity, commonly used genome-scale metabolic model models (GEMs) are based on knowledge bases incorporating information about genes, reactions, and proteins within organisms, provide a platform for analysis of omics data [E. coli, yeast, and humans [S. cerevisae, which has recently seen the reconstruction of the GEM Yeast8 [While the ics data , and enhics data . GEMs ard humans . In termM Yeast8 .S. cerevisae have many applications, ranging from uses in bioengineering (where yeasts are used as a cell factory) to uses as a model organism in biomedical research [Studies of research . The recSignificant work has been carried out by many teams to make open-source genome-scale metabolic models (GEMs) of metabolism in humans and other organisms. GEMs provide a framework to study the genotype–phenotype relationship and place the analysis of high-throughput metabolomics data into a larger context . Many ofAs an example of what GEMs include and can accomplish, we will consider the Recon series of models: Recon 1 (published in 2007) , Recon 2Classic studies in the field have originally proposed a three-step approach to systems biology in RBCs. First, they suggested untargeted data collection on RBCs using integrated omics techniques. Next, they suggested using computational models from systems biology to generate hypotheses. They suggested testing these hypotheses with targeted omics approaches as the third step. Finally, these steps could be repeated, and the hypotheses successively refined in subsequent iterations .This iterative approach of combining untargeted data collection, computational modeling and analysis, and experimental verification is essential to ensure that computational models produce the correct output and model the system as intended. Downs and others use the simile of a “three-legged stool” to advance knowledge of biochemical pathways with computational models of metabolism. In their model, the stool consists of standard experimental techniques with mass data collection of data with omics techniques and computational analysis. Taken together, all three approaches lead to robust scientific understanding. However, when any one piece is taken out, they argue that the quality of the science will suffer . SimilarRecognizing the need to provide a framework for the analysis of massive datasets from high-throughput omics studies, Bordbar and colleagues developed iAB-RBC-283, a knowledge base of the metabolic network of the RBC . SignifiIn the iAB-RBC-283 study, the team found that their knowledge base predicted that erythrocytes contained at least 142 known targets of approved small-molecule medications . Later, Nemkov and colleagues demonstrated that a xenometabolite identified in that knowledge base, ranitidine affects RBC resting in blood storage units. In this case, Nemkov et al. posited and observed that, if RBCs were supplemented with ranitidine during storage, the quality of the RBCs would be improved through the up-regulation of glycolysis .Yurkovich and colleagues have proposed using omics data generated by investigations of RBC storage lesions as a platform for systems biology modeling and study in a way that contributes to the body of knowledge in transfusion medicine . In an iTaken together, these studies show how initial observations from machine learning and data analysis can be used to create a computational model which later can inform further targeted experimental work. However, such models do not capture the heterogeneity of RBC metabolism as a function of genetics and non-genetics factors, an aspect that the introduction of high-throughput omics strategies has shed light on over the last five years.According to Redekop and Mldasi, there are three major definitions of personalized medicine . The fir0, Km, and kcat), thereby circumventing parameterization challenges typically associated with traditional kinetic parameters. Km and kcat depend on the exact protein-coding exon sequence among individuals. Hence individualized changes to the coding sequence potentially change the PERC associated with those values for that protein. Indeed, the Bordbar team found a significant correlation between PERCs and minor allele frequencies (MAFs). In further support of their model’s biological relevance, they found timescales of the dynamics on the order of milliseconds to seconds, which are relevant to fast reactions near equilibrium and key parts of RBC pathways. They found that most inter-individual variation was within dynamics on the circulation time scale. Finally, they studied the effects of the antiviral drug ribavirin in RBC metabolic pathways by adding ribavirin kinetic expressions to the individualized models. Using classification trees based on PERCs as predictors, they separated the individuals into RBV responder and non-responder groups. The classification revealed that PERCs for phosphoglycerate kinase and adenine transport could potentially be important mechanisms in RBV-induced hemolytic anemia.As an example of personalized systems biology, Bordbar et al. created personalized kinetic models of RBCs based on metabolomic data from 24 individuals . Their mWhile this review generally focuses on high throughput omics techniques that generate data for large values of metabolites, another interesting direction is studying a small number of metabolites to predict the future trend of values for a larger number of metabolites. There are two advantages of using a smaller number of metabolites as predictors. First, targeted measurements of a small number of metabolites can yield more detailed insights about the system those metabolites are found in. Second, targeting fewer metabolites makes cost-effective measurement techniques more accessible in a wider variety of settings . In anotRBC as a model of all cells can potentially move systems biology into personalized medicine. As a simplified eukaryotic cell, RBCs can serve as a tractable system of study for creating systems biology models. As a cell that travels throughout the body, RBCs can serve as windows onto many systems of clinical relevance .0, Km, and kcat, vary among individuals because of genetic variation that affects the enzymes in question. They suggested a follow-up question necessitating conducting a GWAS study: how does it affect the PERCs [L. lactis with a generalized mass action (GMA) model [Building on the prior work mentioned here, future research paths can potentially involve (1) connecting donor genotypes to the phenotype of markers of storage lesion and (2) modeling the kinetic response of RBC reactions to hypoxic stress. First, as mentioned above, Bordbar et al. found that pseudo-elementary rate constants (PERCs), which encapsulate Ehe PERCs ? BuildinA) model . An analA) model . Multipl