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.


GEnome-scale Metabolic models (GEMs) are powerful tools to predict cellular metabolism and physiological states in living organisms. However, due to our imperfect knowledge of metabolic processes, even highly curated GEMs have knowledge gaps . Existing gap-filling methods typically require phenotypic data as input to tease out missing reactions. We still lack a computational method for rapid and accurate gap-filling of metabolic networks before experimental data is available. Here we present a deep learning-based method — CHEbyshev Spectral HyperlInk pREdictor (CHESHIRE) — to predict missing reactions in GEMs purely from metabolic network topology. We demonstrate that CHESHIRE outperforms other topology-based methods in predicting artificially removed reactions over 926 high- and intermediate-quality GEMs. Furthermore, CHESHIRE is able to improve the phenotypic predictions of 49 draft GEMs for fermentation products and amino acids secretions. Both types of validation suggest that CHESHIRE is a powerful tool for GEM curation to reveal unknown links between reactions and observed metabolic phenotypes. A computational method for rapid and accurate gap-filling of metabolic networks without using phenotypic data is unavailable. Here, the authors address this problem by developing a deep learning based method that can predict missing reactions using topological features of the metabolic networks. GEMs are powerful computational tools to predict metabolic fluxes in living organisms3. Used alone or integrated with high-throughput data, GEMs can produce mechanistic insights and falsifiable predictions that progressively advance various disciplines in biomedical sciences5, including metabolic engineering7, microbial ecology8, and drug discovery9. Recently, the rapid growth in whole-genome sequencing data10 has triggered a surge in draft GEMs generated by automatic reconstruction pipelines12. Yet, these draft models contain knowledge gaps and thus require comprehensive manual curation14, e.g., finding missing reactions due to incomplete genomic and functional annotations. Therefore, the quality of initial draft GEMs has a profound impact on the time spent on the manual curation, the refined model quality, and ultimately its utility in biomedical applications.As a mathematical representation of the metabolism for an organism, the GEnome-scale Metabolic model (GEM) offers a comprehensive gene-reaction-metabolite connectivity through two matrices: the stoichiometric matrix associating metabolites with their reactions; and the reaction-gene matrix associating reactions with their corresponding enzymes and genes18. Despite wide differences in their input data types, objectives, and algorithms, they generally follow two steps: (1) find dead-end metabolites that cannot be produced or consumed and/or some inconsistencies between the draft model prediction and experimental data ; and (2) add a set of reactions to resolve the dead-end blocks and/or inconsistencies16. Optimization-based methods often require data as input to identify model simulation-data inconsistencies16. However, experimental data is not readily available for non-model organisms, thus limiting the utility of those tools. For example, most intestinal organisms are considered “uncultivable” and their functions remain unknown19. Even for cultivable organisms, high-throughput phenotypic screening, i.e., searching for organisms with desired phenotypes, relies on the analysis of microbial extracts or genetic modifications, which can become complicated, time-consuming, and expensive. Given the increasing availability of cultivable organisms and their genomes, there is a pressing need for rapid and accurate in silico predictions of metabolic phenotypes solely from genomic sequences. Even though the predictions are theoretical, downstream experimental validations could be mush less resource-demanding.Numerous optimization-based gap-filling methods have been designed to tease out missing reactions in draft GEMs20 and FastGapFill21; and (2) state-of-the-art machine learning methods that exploit advanced machine learning techniques, such as Neural Hyperlink Predictor (NHP)22 and Clique Closure-based Coordinated Matrix Minimization (C3MM)23 . Notably, metabolic networks or any biochemical reaction networks have a very natural hypergraph representation: each molecular species is a node and each reaction is a hyperlink connecting all the molecular species involved in it.A few gap-filling methods that are entirely topology-based and do not require phenotypic data as input include 1) classical methods that restore the network connectivity based on flux consistency, such as GapFind/GapFill classicaThere are notable limitations associated with existing topology-based machine learning methods. C3MM has an integrated training-prediction process, which includes all candidate reactions (obtained from a reaction pool) during training. Hence, it has limited scalability , and the model has to be re-trained for each new reaction pool. While the neural network-based method NHP separates candidate reactions from training, it approximates hypergraphs using graphs in generating node features, which results in the loss of higher-order information. More importantly, both methods were benchmarked against a handful of GEMs (lacking a comprehensive test) and were only internally validated using artificial gaps introduced by randomly deleting reactions from input GEMs .29 models and 818 AGORA8 models. For external validation, we assess the ability of CHESHIRE to predict metabolic phenotypes. Using 49 draft GEMs reconstructed from commonly used pipelines (CarveMe11 and ModelSEED30), we show that CHESHIRE improves the theoretical predictions of whether fermentation metabolites and amino acids are produced by these GEMs.Here we develop a method called CHESHIRE  to overcome the limitations of existing machine learning methods. CHESHIRE only requires a metabolic network for training and outputs confidence scores for candidate reactions from a reaction pool. For internal validation, we show that CHESHIRE outperforms NHP and C3MM in systematic tests of recovering artificially removed reactions from 108 BiGGCHESHIRE is a deep learning-based method that predicts missing reactions in GEMs using topological features of their metabolic networks without any inputs from experimental data (other than genomic sequences). For each metabolic network Fig. a, we use31 to generate a feature vector for each metabolite from the incidence matrix . This initial feature vector encodes the crude information of topological relationship of a metabolite with all reactions in the metabolic network. For feature refinement, to capture the metabolite-metabolite interactions, we use Chebyshev spectral graph convolutional network (CSGCN)32 on the decomposed graph to refine the feature vector of each metabolite by incorporating the features of other metabolites from the same reaction (see “Feature refinement in Methods”). For pooling , we utilize graph coarsening methods to compute a feature vector for each reaction (represented by a fully connected subgraph in the decomposed graph) from the feature vectors of its metabolites. We combine two pooling functions, a maximum minimum-based function (as used in NHP22) and a Frobenius norm-based function33 to provide complementary information of metabolite features. Finally, for scoring, we feed the feature vector of each reaction into a one-layer neural network to produce a probabilistic score for the reaction that indicates the confidence of its existence (see “Pooling and scoring in Methods”). In the training phase, the resulting scores are compared to the target scores (one for positive reactions and zero for negative reactions) with a loss function for updating the model parameters  as a baseline method. The learning architecture of NVM is relatively simple. It uses Node2Vec  and mean pooling (averaging node features) to generate metabolite and reactions features, respectively, without feature refinement . We compared CHESHIRE with the state-of-the-art machine learning methods NHP and C3MM as they have been demonstrated to display superior performances over previous gap-filling (or hyperlink prediction) methods. We also included Node2Vec-meanBelow we performed two types of internal validation based on artificially introduced gaps Fig. a. For boP < 10−4, two-sided paired-sample t-test) and Recall (P < 10−16), suggesting that CHESHIRE recovers the majority of true reactions without sacrificing its ability to distinguish true from fake reactions. For all the performance metrics, the wide distributions indicate that the performance of CHESHIRE (as well as the other three methods) is GEM-dependent, even though they are all high-quality models. The most easily gap-filled GEM is a reconstruction from Phaeodactylum tricornutum CCAP 1055/1 (model iLB1027_lipid), where CHESHIRE is able to achieve AUROC = 0.92, Recall = 0.89, Precision = 0.82, and F1 score = 0.86 on the testing set. Notably, CHESHIRE is not sensitive to the threshold score, the negative sampling strategy, and the negative sampling ratio in this validation, where its performance still prevails over the other methods . We found that CHESHIRE achieves the highest recovery rate at the four cutoffs for both types of reaction pools . While the performance of CHESHIRE declines when more reactions were added, it is still significantly better than the second-best method C3MM at the top N cutoff . Furthermore, as expected, using the entire BiGG universal reaction pool would undermine the performances of recovery rate for all the methods. CHESHIRE nevertheless accomplishes the best performance compared to NHP and NVM .To perform the second type of internal validation, we tested CHESHIRE on the same BiGG GEMs with 90% training and 10% testing using genus-specific and the universal BiGG reaction pools  are ranked based on both confidence and similarity scores. The confidence score, returned by CHESHIRE, quantifies the probability of a candidate reaction being present in the GEM. The similarity score measures the maximum correlation between a candidate reaction and all existing reactions in the GEM. Given our rationale that dissimilar reactions are more likely to be functionally complementary to the existing ones, all candidate reactions whose confidence scores ≥ 0.9995 are ranked by their similarity scores (least similar to most similar). The top 200 reactions are added to the draft GEM to produce a gap-filled GEM  for unbalanced datasets. We compared four different groups of models: the draft GEMs reconstructed from CarveMe (CarveMe), gap-filled GEMs by adding the top 200 reactions predicted by CHESHIRE and NHP (CHESHIRE-200 and NHP-200), and gap-filled GEMs by randomly adding 200 reactions from the universal BiGG reaction pool (Random-200).We applied the workflow to a compiled dataset including fermentation profiles of 9 metabolites from 24 bacterial organisms  and, in particular, the F1 score for 11 of the 24 draft GEMs. Compared across the 9 fermentation metabolites, the biggest improvement of CHESHIRE over CarveMe draft models was observed on acetic acid followed by lactic acid production. Among the 24 draft GEMs, CHESHIRE increases the correct predictions of acetic acid and lactic acid phenotypes from 8 to 17 and 14 to 20, respectively. Finally, we demonstrated that the improved performance is not simply due to more reactions by showing a significantly better performance of CHESHIRE-200 than that of Random-200 .We observed a high variability across the draft CarveMe models to predict fermentation profiles. One model correctly predicts all phenotypes (F1 = 1.0) but 14 others fail to predict any (F1 = 0.0). Adding the 200 NHP-predicted reactions barely improves the phenotypic predictions and the improvement is even worse than that after randomly adding 200 reactions Fig. b–e. To tTo test whether the improvement of CHESHIRE over CarveMe-reconstructed draft models is generalizable to GEMs from other sources, we performed the same fermentation test on ModelSEED-reconstructed draft models. Similar results were observed, where CHESHIRE-200 improves the predictions over draft models, and draft models plus randomly selected 200 reactions , and NHP-200 shows no improvement at all H-mediated redox reactions  from the top 200 candidates that causally fill the gaps Fig. ”. The gaTo assess the false positives added by CHESHIRE-200, we counted the number of fermentation products and amino acids that were added by CHESHIRE but not observed experimentally. For the fermentation test, CHESHIRE adds a phenotype in 27 simulations , where 15 are true positives and 12 are false positives. For the amino acid test, all 67 simulations that predicted a gain of phenotype are true positives. These results suggest that the risk of introducing false positive phenotypes may depend on the model and phenotype. We further assessed whether these false positives may be linked to the bias of CHESHIRE to score specific types of reactions higher than the others. For nearly all enzymatic functional classes . Correct predictions of phenotypes do not necessarily mean correct inference of missing reactions. It is likely that different enzymes carry the same metabolic functions  from different substrates. While adding reactions is expedited by increasingly advanced machine learning methods, it is still a heavily manual task to trim reactions that are wrongly added. This challenge, termed as content removal, has been recognized as one of the two fundamental bottlenecks for GEM quality improvementP < 0.01, two-sided paired-sample t-test). Thus, our preliminary cofactor analysis highlights a promising future direction for metabolic network gap-filling.In this study, we have taken initial steps towards further reduction of the number of CHESHIRE-predicted reactions. First, CHESHIRE-200 conducted a comprehensive search of 236 metabolites to identify metabolic phenotypes that can be potentially gap-filled by adding 200 reactions th entry of H, i.e., Hip, has value one. If not, it is equal to zero.As a natural extension of graphs, hypergraphs are composed of hyperlinks  which can join any number of nodesn nodes, we therefore propose an encoder-based approach to produce node features by simply passing the incidence matrix H through a one-layer neural network, i.e.,hi denotes the ith row of the incidence matrix, Wenc and benc are the learnable parameters in the encoder, and hard-tanh is a nonlinear activation function defined as50. Incidence matrix of a hypergraph is able to capture multidimensional relationships unambiguously while keeping low memory costs51. Hence, we believe that our approach can provide more accurate initial node features of a hypergraph with less computational costs.In a transductive learning setting, the node attributes are not provided. It is thus necessary to generate the node features based on the hypergraph structure solely. Given an incomplete hypergraph denc. Let xij denote the jth entry of the feature vector xi for node vi. Then the element-wise normalized features are given byαj is a learnable parameter that controls how much information need to keep in the mean, γj and βj are the affine parameters, and μj and σj are the mean and standard derivation of the features in each clique, respectively. Graph normalization has proved to be advantageous in training graph convolutional networks compared to other normalization methods such as batch and layer normalization52. In order to prevent overfitting, we further add an alpha dropout layer after the graph normalization. The alpha dropout utilizes a scaled exponential linear unit (SELU), which includes self-normalizing properties such as maintaining the mean and standard derivation of the inputs and avoiding exploding and vanishing gradients53. For convenience, we drop the tilde notation and use xi as the updated features.Feature refinement is the most critical component in CHESHIRE, which is composed of normalization, dropout, and graph convolutional networks. First, we decompose the hypergraph into a disjoint graph with separate cliques formed by the hyperlinks. Two nodes in the disjoint graph share the same feature space if originating from the same node in the hypergraph. After obtaining the node features of the disjoint graph, we feed the features to a graph normalization layer for each clique. Suppose that the dimension of the feature vectors is 32. Given a hyperlink vi byK is the Chebyshev filter size, L is the symmetric normalized Laplacian matrix of the clique with the largest eigenvalue D and A are the degree and adjacency matrices of the clique, respectively. For convenience, we drop the head notation and use xi as the updated features.Second, we continue to refine the features with a CSGCN on each clique . CSGCN exploits the Chebyshev polynomial expansion and spectral graph theory to learn the localized spectral filters which can extract local and composite features on graphs that encode complex geometric structuresdconv and denote xij as the jth entry of xi. We propose to employ a Frobenius norm-based  pooling function to generate hyperlink features, which is defined as33.We aggregate the refined node features within each clique/hyperlink to produce a score. There are many pooling functions such as mean pooling and maximum pooling. Here we use two different pooling functions. Suppose that the dimension of the convolutional feature vector is 22 defined asep is then given byIn order to achieve a better performance, we also incorporate the maximum minimum-based pooling function54. We chose the above loss function since it offers a better performance compared to traditional classification loss functions such as cross entropy loss. We exploit the highly efficient Adam optimization algorithm to train CHESHIRE. During the training stage, CHESHIRE tries to learn the weights of the deep neural network by minimizing the loss function, which maximizes the scores for positive hyperlinks to be higher than the average score for negative hyperlinks. During the testing stage, CHESHIRE uses the learned weights to calculate a probability score for an unseen hyperlink .We train CHESHIRE with the following loss functionThe key hyperparameters of CHESHIRE are the encoder feature dimension, the graph convolutional feature dimension, the Chebyshev filter size, the dropout probability, and the learning rate. We used a universal hyperparameter set for CHESHIRE during internal and external validations. We found that the performance of CHESHIRE with a pre-selected universal hyperparameter set is close to that obtained by grid search. This implies that CHESHIRE is not sensitive to these hyperparameters. Therefore, we decided to use a universal hyperparameter set for all the GEMs, which can also save a great amount of computational resources. The encoder feature dimension, the graph convolutional feature dimension, the Chebyshev filter size, the dropout probability, and the learning rate are set to 256, 128, 3, 0.1, and 0.01, respectively. For hyperparameters of NVM, C3MM, and NHP, see “Hyperparameter selection in Supplementary Note 22. Suppose that we have a hypergraph f, where half of the nodes in f are from e (rounding is required for odd number of metabolites) and the remaining half are from e). The motivation behind the strategy is that it is extremely unlikely that half of the metabolites from a valid reaction and randomly sampled metabolites are participated in another valid reaction. We denote the set of negative hyperlinks as In order to accurately predict missing reactions from a metabolic network, it is necessary to sample negative reactions, i.e., reactions that do not exist. We used the negative sampling strategy proposed inFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


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