Interest in bulk biomass from microalgae, for the extraction of high-value nutraceuticals, bio-products, animal feed and as a source of renewable fuels, is high. Advantages of microalgal vs. plant biomass production include higher yield, use of non-arable land, recovery of nutrients from wastewater, efficient carbon capture and faster development of new domesticated strains. Moreover, adaptation to a wide range of environmental conditions evolved a great genetic diversity within this polyphyletic group, making microalgae a rich source of interesting and useful metabolites. Microalgae have the potential to satisfy many global demands; however, realization of this potential requires a decrease of the current production costs. Average productivity of the most common industrial strains is far lower than maximal theoretical estimations, suggesting that identification of factors limiting biomass yield and removing bottlenecks are pivotal in domestication strategies aimed to make algal-derived bio-products profitable on the industrial scale. In particular, the light-to-biomass conversion efficiency represents a major constraint to finally fill the gap between theoretical and industrial productivity. In this respect, recent results suggest that significant yield enhancement is feasible. Full realization of this potential requires further advances in cultivation techniques, together with genetic manipulation of both algal physiology and metabolic networks, to maximize the efficiency with which solar energy is converted into biomass and bio-products. In this review, we draft the molecular events of photosynthesis which regulate the conversion of light into biomass, and discuss how these can be targeted to enhance productivity through mutagenesis, strain selection or genetic engineering. We outline major successes reached, and promising strategies to achieving significant contributions to future microalgae-based biotechnology. Therefore, using sunlight and CO2 to produce a variety of organic molecules and biomass, by the extensive cultivation of photosynthetic organisms, has the potential to cover a significant portion of global energy demand [2 capture from e.g. power plants or other large-scale emission sources. As unique feature, photosynthesis allows for direct energy storage into liquid fuels which can be used in the existing transport systems, while other forms of renewable energy such as hydro-, wind or photovoltaic power, cannot.Approximately 100,000 terawatts-year (TW-y) power from sunlight reach the surface of our planet. This is a renewable resource exceeding the current human global energy demand (15 TW-y) and the 24 TW-year predicted for anthropic activities by 2030 [Haematococcus, Chlorella and Dunaliella. Diatoms and cyanobacteria, which also represent a valuable biotechnological platform [Thus, mass culture of microalgae has gained interest in the past few decades. Indeed, beside small-scale traditional cultivations mainly aimed to human feeding, commercial production of algae on a larger scale has been identified in recent years as a renewable and environmentally sustainable strategy for feedstock production. Microalgae include a wide group of photosynthetic, eukaryotic, unicellular organisms: green microalgae, belonging to the class Chlorophyceae, include genera which are among the most widely used for industrial applications, such as platform , 6, willThe phyletic group of green microalgae include species which have adapted to diverse environmental conditions, even extreme, of the planet . By cons2 at efficiencies that are appreciably higher than land plants [Although photosynthetic machinery is similar to that of plants, microalgae convert solar energy into biomass and fix COd plants . The maxd plants .−1 year−1, while outdoor mass cultivation record yield beyond 100 ton ha−1 year−1 could not be reached [−1 year−1, reported for sugarcane field trials in the tropics. When considering oil yield extracted from plant vs. algal biomass, palm oil can produce a maximum of 4–5 ton ha−1 year−1 [−1 year−1 [Theoretical quantification of 8–10% in energy conversion efficiency of microalgae  translat reached . This co1 year−1 , 21 vs. 1 year−1 . Thus, ri.2 and nutrients. They lack non-productive (heterotrophic) organs to maintain, and the simpler unicellular structure make the whole biomass fully photosynthetically active, irrespective to seasonal life cycle;When growing in aerated liquid cultures, cells have easy access to light, COii.−1 of heterotrophic dry weight (DW) biomass and 6 g l−1 of photoautotrophic DW biomass [Algae are metabolically flexible and have a short doubling time. Although most microalgae are primarily photoautotrophs, many species undergo metabolic shift to heterotrophy upon changes in environmental conditions, utilizing organic compounds as C and energy source or to mixotrophy . Moreover, under optimal growth conditions, most species have doubling time of a few hours, and cultures reach as much as 10 g l biomass , 23;iii.Microalgae do not require fertile land, and can grow in wastelands, using brackish or waste water, or even sea water in the case of marine species; hence, their cultivation does not compete with resources for conventional food production, and would be more environmentally sustainable respect to extensive cultivation of crops. Therefore, microalgae offer the opportunity to shift part of unsustainable farming and fishing routines toward unproductive region;iv.Different species can be selected for specific growth conditions, suited to the local climate, which is more difficult with conventional crops.Multiple reasons contribute to such a feature:Arthrospira platensis (formerly known as Spirulina) and the green microalgae Chlorella vulgaris, Dunaliella salina and Haematococcus pluvialis, which are mainly dedicated to the production of single products in large-scale cultivation systems.In this paragraph, the bio-technological applications of best wild type species are reviewed with focus on high-value production chemicals and biomass for biofuels Table . CurrentArthrospira platensis is exploited as source of nutraceuticals [euticals , long-cheuticals , caroteneuticals  and proteuticals . Other aeuticals , 29, as euticals  and for euticals .Chlorella includes a number of species which are widely commercialized for production of nutraceuticals. Besides to the high protein, carotenoids and vitamins content [C. vulgaris contains also β- and α-glucans, d-glucose polysaccharides which act as immune stimulators, free-radical scavengers and anti-cancer compounds [Chlorella has been successfully used to produce starch, reaching 26% DW yield under mixotrophic condition [Genus  content , C. vulgompounds . Moreoveondition .Dunaliella salina through both extensive cultivation in ponds and intensive cultivation in PBRs [−1 day−1 at 1500 µmol m−2 s−1 [D. salina growing at different salinity conditions are available [Carotenoids represent the commercial product from microalgae with highest success. Carotenoids are widely used as food colorants, aquaculture feed additives and components for cosmetics and skin care; carotenoids also have biomedical applications, including anti-inflammatory activities which are related to their strong antioxidant properties . β-carot in PBRs . Recentl m−2 s−1 . Currentvailable .Haematococcus pluvialis. Under various stress conditions, this alga changes from a thin-wall mobile phase to a red thick-wall resting phase, in which astaxanthin can reach up to 5% DW [Haematococcus is a popular nutraceutical antioxidant for human diet [Chlorella zofingiensis has been proposed as an alternative astaxanthin source which is more reliable in growth [Astaxanthin is a high-value, red keto-carotenoid, successfully commercialized by many companies worldwide through cultivation of the green alga to 5% DW . Astaxanto 5% DW . Moreoveman diet . Chloreln growth .Thraustochytrium sp., Pavlova lutheri, Nannochloropsis gaditana, Isochrysis galbana, Crypthecodinium cohnii (rich in DHA and EPA), Rhodomonas salina and Tetraselmis suecica (α-linolenic acid) [Parietochloris incisa (arachidonic acid) [Nannochloropsis, Rhodomonas and Tetraselmis has higher antioxidant properties respect to fish oil, likely due to the content of valuable carotenoids and polyphenols and is expected to replace fish oils in diets soon [Biosynthesis of fatty acids and triglycerides (TAGs) is relevant for several industrial applications. Microalgae are the primary producers of lc-PUFAs such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), which accumulate in the oil of many fish species. Aquaculture farming increased demand of lc-PUFAs for nutrition, which are currently produced from fish oil, while a more sustainable lc-PUFA supply is seeked. Several marine algal species are rich in lc-PUFAs thus have a great potential for biorefinery: these include ic acid) , 43 and ic acid) ; these aic acid) ; oil froets soon .Porphyridium and Desmidiales spp. Red microalgae Porphyridium spp. are fast-growing and accumulate extracellular polysaccharides commercially used in cosmetic and medical field [Desmidiales, Netrium digitus has been successfully cultivated in porous substrate bioreactor, reaching a maximum product concentration of 25 g m−2 [Further species with high potential for large-scale exploitation of extracellular polysaccharides include the microalgae al field , 48. Amo25 g m−2 .vs. fuel problem [In the last decade, microalgae have received increasing interest as a source of biomass for replacing fossil fuels. Liquid fuels derived from raw biomass are an attractive source of renewable energy, to be used in transport system or energy cogeneration. With respect to the major biofuels currently produced worldwide, namely bio-ethanol from sugar cane and biodiesel from oil crops, the so called “third generation”  biofuels, are considered as a promising option, since these organisms are highly productive and provide a solution for the food  problem .Chlorella [Scenedesmus [Monoraphidium, the latter showing high productivity and high-quality lipid profile [Botryococcus braunii (hydrocarbons constituting up to 75% of its DW), however its potential is limited by the slow growth [Green algae accumulate high levels of polysaccharides both as cell-wall constituents and storage molecules that can be fermented to bioethanol , 52. Oilhlorella , Scenedenedesmus  and Mono profile . An unusw growth .Archaea [Despite great advantages offered by microalgae exploitation, production of third generation biodiesel is still far from being commercially viable . As an aArchaea . A numbeArchaea , 61, 62.2 shows great promise for generating future, large scale sustainable energy, a number of bottlenecks still limit its production [C. reinhardtii promising targets for genetic engineering of H2 production capacity while the use of temperature-sensitive conditional PSII mutants has been proposed in order to separate the oxygenic biomass-accumulating phase from the oxygen sensitive hydrogenase activity [Finally, microalgae and cyanobacteria can produce bio-hydrogen through photo-fermentation, in an anaerobic process involving oxidation of ferredoxin by the hydrogenase enzyme , 64. Altoduction ; howeveroduction  identifiactivity .2 released by large-scale emission plants [Scenedesmus obliquus, Chlorella vulgaris, Chlorella protothecoides and Spirulina spp., which can grow up to 15–18% CO2, although highest productivity was observed around 10% CO2 [N. gaditana is arising as promising species for CO2 removal due to a high biofixation rate—more than 1.7 g−1 l−1 day−1 [Microalgae are gaining importance in the biological offset of polluted matrix, because of their ability to thrive under extreme or polluted condition: they serve for direct carbon capture, a way for reducing COn plants . Promisi 10% CO2 , 70. In −1 day−1 .Growth in open ponds is an established technology for bioremediation of wastewater and nutrient recovery in the form of biomass . The effD. salina, and astaxanthin by H. pluvialis [Chlorella spp., Spirulina spp.) produce high-value compounds, however these productions are currently applied at small-scale cultivations, which need significant reduction in operating costs to become competitive with the same molecules extracted from other feedstocks [Despite a number of industrial applications of microalgae have been proposed and studied in lab-scale, the only successful commercial exploitation of microalgal mass culture is the production of carotenoids, namely β-carotene by luvialis . Other sedstocks . A numbeedstocks , which, edstocks . An overIn algal biomass pipeline, there are many elements which contribute to the overall cost of the process. Major factors to take into account are (i) the choice of production system, (ii) the strategies to supply nutrients, aeration, mixing, and (iii) how to harvest and process biomass, and (iv) the procedure to avoid infections and contaminants. Different approaches are available, each having benefits and limitations .−2 day−1) due to poor gas exchange and dark zone, therefore low cell density forces to cover extensive areas and requires high costs for harvesting the biomass.Microalgae are mainly cultivated in open ponds, which are cheaper to build, and easier to operate and to scale-up than closed systems. Generally, ponds are raceways at depth of 20–30 cm, in which biomass is mixed by paddles or left unstirred. Drawbacks of these systems include the complication of controlling contaminations, and the difficult of keeping constant growth parameters ; moreover, they suffer of low productivities , bio-flocculation (caused by secreted polymers) or microbial-induced flocculation [Following growth, biomass must be (i) harvested and (ii) processed to dryness. Both steps remain a major obstacle to industrial scale processing and contribute to ~ 1/3 of the final biomass cost. Current harvesting methods include chemical, mechanical and bio-based procedures: electrolytes or polymers are added to flocculate cells; centrifugation or flow filtration are rapid methods, which however implies high investments and operating costs; biological-based methods include auto-flocculation  is used to drive photosynthesis. Indeed, an area of promising research aims at improving the light-to-biomass conversion efficiency under mass culture conditions.−1 year−1, respectively. Instead, outdoor mass cultivation showed that, with the present technology and wild type strains, annual productivities beyond 80–100 ton DW ha−1 year−1 cannot be maintained at large scale and over long periods [Calculations in  provided periods . OvercomWild type algal strains suffer of light use inefficiency. Enhancing light-to-biomass conversion efficiency will help counterbalancing the cost of energy and nutrients used in the cultivation system, as well as reducing the costs of downstream biomass processing, making it a target for genetic improvement.2 fixation rate, thus photosynthetic rate increases non-linearly as a function of light; (3) when light intensity overcomes the rate of downstream biochemical reactions, photosystems get rid of energy absorbed in excess, and in this phase photosynthetic rate reaches a plateau.Regulation of light harvesting capacity is crucial for cells in order to balance light reactions and downstream biochemical events of photosynthesis. Indeed, autotrophs have evolved regulatory mechanisms, to fine-tune continuous transitions between “conservative” and “dissipative” state of absorbed energy. In particular, photosynthesis typically displays a light saturation curve Fig. , in whicC. vulgaris, where light attenuation is minimized, light saturation is reached at around 1200 µmol photons m−2 s−1. At this irradiance, algae protect themselves from excess illumination by triggering the non-photochemical quenching (NPQ) mechanism, a feedback-regulated de-excitation of Chls that operates in the PSII, to prevent over-excitation of reaction centers  energy quenching is significantly activated only upon acclimation to excess light conditions [2 fixation rate. However, algae can experience very high light intensity, saturating photoprotective mechanisms. Light in excess of photosynthesis saturation level is dissipated rather than contributing to biomass accumulation, or even causes synthesis of reactive oxygen species (ROS), which damage the photosynthetic machinery and affect biomass yield. C. reinhardtii mutant npq4, devoid of NPQ response, was indeed more susceptible to photo-oxidation [In a dilute culture of activity , while inditions . NPQ actxidation .Due to the absorption of pigments bound to the large antenna systems in both photosystems, light distribution within the culture is inhomogeneous, and this strongly contributes to the gap between theoretical vs. real productivity. Such large arrays of antenna complexes have been selected by evolution as favorable trait, since they maximize the light-harvesting capacity and therefore the adaptation to a natural water environment where light is often scarce and limits growth, and cell density remains low. Contrary to the natural environment, growth conditions in mass cultures requires high cell biomass per volume of installed facility. However, this results in high optical density and light shortage in the deeper layers of the culture while cells at the surface layers intercept most photons, resulting in saturation of photosynthesis, dissipation of excess energy and/or photoinhibition. The most inner layers easily drop below the compensation point of photosynthesis while active respiration consumes energy. Thus, dense algal cultures suffer both photo-deprivation and photo-inhibition, decreasing the overall light-to-biomass conversion efficiency far below the theoretical score. Rapid mixing of biomass is often suggested as a solution to light gradients, but it is not: rapid light/dark cycles between dark and over-saturating irradiances have a deleterious effect on biomass yield . Modelin2 and can also use O2 to give oxygenated substrates [One additional factor which contributes to the inefficiency of photosynthesis is carbon fixation: the enzyme RuBisCO, which catalyzes the carboxylation of Ribulose 1,5-bisphosphate (RuBP), has low affinity for CObstrates , which u2 availability at the RuBisCO active site within the pyrenoid, a micro-compartment of the chloroplast. Engineering such a mechanism into algal species devoid of pyrenoids might augment the overall C fixation rate and thus photosynthetic efficiency.Interestingly, many microalgae use biophysical carbon concentrating mechanisms for active retention of inorganic C  to increIn conclusion, results reported in this section suggest that modulation of photosynthetic reactions is a key factor controlling biomass yield at both saturating and sub-saturating irradiances, that is worth to be considered for a domestication strategy aimed at improving performances in PBRs.According to the previous section, a gap between theoretical and real biomass productivities of microalgae originates from the high OD of cells. With respect to the problem of inhomogeneous light distribution, high density planting of crops is a condition limiting PAR penetration thus productivity, and it is not different than the condition of elevated cell density reachable in a PBR. Moreover, biomass production with wild type algal strains is poorly viable likely as farming with ancestral crop varieties.The so called ‘Green Revolution’ of agriculture, a domestication based on breeding and phenotypic selection, succeeded in pursuing crop productivities over the past 50 years , 93. IndN′-nitro-N nitrosoguanidine (NTG) and ethyl methanesulfonate (EMS).Random mutagenesis is recognized as a powerful technology in mutation breeding, widely employed for strain improvement and for studying the molecular basis of metabolic processes. Forward genetic approach is of particular relevance for algal biotechnology, since it avoids restrictions to GMO for outdoor production system . The mosAttempts for algae genetic improvement, aimed to enhance light-to-biomass conversion efficiency, relied on random mutagenesis and screening of favorable traits. These approaches, while overcame scarcity of genetic engineering tools in microalgae, needed for efficient screening strategies for strains with higher productivity. Some of these approaches, which succeeded in increasing photosynthetic yield, are presented as follows.C. reinhardtii was employed to isolate mutants having a truncated light-harvesting system [C. sorokiniana, and selected pale-green mutants by imaging Chl fluorescence. Mutants were able to perform photosynthesis more efficiently than wild type, minimizing photoinhibition in high light; the positive effect on photosynthetic productivity was confirmed in both lab-scale and outdoor PBRs. Similar results were obtained with N. gaditana strains having reduced cellular pigment content [C. reinhardtii, by an RNAi triple knock-down strategy, resulted in improved light-to-H2 (+ 180% than wild type) and light-to-biomass (+ 165%) conversion efficiencies [Due to detrimental effect of high OD for mass cultivation, strains carrying truncated antenna size were proposed to perform better in light transmittance than wild type . Mutageng system –100: allg system  applied  content . Finallyciencies .f, an oxidized form of Chl a, has been isolated from the cyanobacterium C. fritschii [f synthase succeeded in accumulating this chromophore in Synechococcus sp. Since Chl f expands the spectral range for photosynthesis by absorbing far red light, its expression in microalgae may confer advantages for mass culture in PBRs, which suffers for detrimental sieve-effects at high cell densities. However, the feasibility of this approach, which assumes a correct binding of the new chromophores into the existing pigment-binding complexes, still await experimental confirmation.Implementation of biosynthetic pathway of accessory pigments, e.g. phycobilins or Chls, into genus of industrial interest, has been proposed for improving harvesting efficiency over the full PAR spectrum . Recentlritschii ; heterol2 fixation rate is a major limiting step in biomass yield, which arise from RuBisCO inefficiency. Genetic engineering of RuBisCO to increase its catalytic activity or to enhance its specificity towards CO2 have been proposed as ways to overcome these limitations [E. coli-based screen of new RuBisCO variants obtained by direct evolution allowed the identification of an unexplored subunit interface with potential of increasing CO2 fixation rate [2 fixation. An additional approach may reside in the generation of hybrid RuBisCO by using novel activase isoforms from chemolithoautotroph microorganisms such as Acidithiobacillus ferrooxidans [COitations . It is witations , indeed itations , 106 theion rate . Site-diooxidans .C. reinhardtii and H. pluvialis, trigger fast phototactic response, for fine-tuning exposure to light. Indeed, phototaxis confers fitness advantage and it is regulated by cytoplasmic redox balance, which in turn is affected by photosynthetic electron transport rate [C. reinhardtii mutant population for rapid phototaxis response and identified mutants with enhanced photoautotrophic growth and lipid production, respectively 1.9- and 8.1-fold increases than wild type.A high photosynthetic efficiency is attainable only in low irradiance and controlled light environments, which allow most absorbed photons can be utilized by the culture; instead in the outdoor, efficiency drops due to fluctuating irradiances, exceeding the photosynthetic capacity. Autotrophs evolved mechanisms for regulating the efficiency of light capture, which can become target of domestication strategies. Several microalgal species e.g. ort rate . In the ort rate  analyzedSynechocystis than wild type cells [C. reinhardtii, following by Chl fluorescence imaging screening, has produced mutants specifically devoid of lhcsr genes [C. reinhardtii strains lacking the two lhcsr3 genes were more productive than wild type, thus confirming down-regulation of NPQ is a strategy for improving light use efficiency in microalgae. Instead, more recently, [C. reinhardtii wild type and the npq4 lhcsr1 mutant, devoid of all Lhcsr isoforms.Photosynthetic organisms dynamically regulate the amplitude of NPQ see “” sectionpe cells . Random sr genes , 114. Insr genes  proposedecently,  observedC. reinhardtii. Förster et al. [very high light resistant (VHL-R) mutations, which allowed near maximal growth rate at irradiances lethal to the control genotype; characterization of these strains reveals they affected the regulatory pathways which modulate photoprotective response, including PSII repair and ROS detoxification. In [−2 s−1); the two mutations selected, which both mapped in the putative Light Responsive Signal 1 (LSR1) gene, conferred an improved resistance of cells against exogenous ROS. Recent results concern the isolation of pale-green, singlet oxygen resistant mutant by EMS-mutagenized C. vulgaris, which showed biomass yield enhancement by 68% than wild type strain .Microalgae growing in mass culture experience rapid changes in the irradiance due to cell mixing into the PBR. The amount of time spent in sub-saturating vs. excess light influences the biomass productivity, which is lower in fluctuating light conditions  possiblyr et al.  isolatedtion. In , wild tytion. In  performeGenetic manipulation approaches have the potential to revolutionize industry based on microalgae cultivation. These include transfer of genes isolated from other species to generate strains with desirable commercial traits such as tolerance to excess light and heat stress, resistance to herbivore/pathogen, capacity to outcompete opportunistic organisms, or to express biosynthetic pathways into more productive strains Fig. . Recent C. reinhardtii: being able to growth both in autotrophic and heterotrophic conditions, as haploid or diploid cells, Chlamydomonas was adopted as powerful genetic system for studying different physiological mechanisms. Nuclear transformation of C. reinhardtii may be achieved by several methods such as electroporation [Agrobacterium-mediated transformation [Agrobacterium-mediated transformation also succeeded in transforming algae of economic interest such as C. vulgaris, Neochloris oleoabundans and H. pluvialis [In the last decades, several efforts have been attempted to optimize the transformation efficiency of different microalgae species. Stable transformation was first developed in poration , Agrobacormation , siliconormation , 123 andormation . Both elluvialis –127. Theluvialis . Nuclearluvialis ; indeed luvialis , 131.N. salina [In last years, many efforts have been attempted to increase the heterologous expression potential of microalgae. Increased expression of transgenes was obtained by fusing the sequences encoding the gene of interest and the selection marker in a unique bicistronic RNA , 133. Re. salina .The biolistic method (particle-gun bombardment) is the elective procedure for chloroplast transformation in microalgae . The intC. reinhardtii are summarized in Table Further details concerning nuclear and chloroplast expression in P. tricornutum [C. reinhardtii, allowing for deletion of specific gene functions [FTSY and ZEP double-gene knockout, that resulted in improved photosynthetic productivity. Analogous approach was used to abolish the functions encoded by MAA7, CpSRP43 and ChlM genes, which led to pale-green mutants [C. reinhardtii, the replacement of Cas9 with the Cpf1 ribonucleoprotein achieved a more efficient homology-directed DNA replacement [N. oceanica [N. gaditana was obtained by deleting a transcription factor that acts as negative regulator in lipid biosynthesis [Genome editing technology enables for both gene deletion and gene integration, therefore implementation of these novel genetic tools in microalgae would allow for manipulation of metabolic networks. Recently, a novel approach based on CRISPR–CAS9 genome editing technology have been successfully developed in both the marine diatom cornutum  and in Cunctions ; the lat mutants . In C. rlacement . Howeveroceanica  in whichoceanica . Recentlynthesis .TLA1), a nuclear gene putatively involved in the regulation of the antenna size of C. reinhardtii, was up- and down-regulated by overexpression and RNAi, respectively. The strain over-expressing TLA1 showed a larger antenna size for both photosystems and lower Chl a/b ration than the wild type, while its down-regulation resulted in the opposite phenotype changes [C. reinhardtii, LHCII translation efficiency is regulated by the cytosolic RNA-binding protein NAB1, which is subjected to specific nitrosylation in limiting light, thus making such repressor less-active and promoting accumulation of LHC [As previously described, a truncated antenna size yielded into increased productivity in green microalgae see “” section changes . LHCII, n of LHC .rbcL (RuBisCO large subunit) gene [C. reinhardtii did not increase the overall photosynthetic yield.Manipulation of RuBisCO activity, namely the major constraint for C assimilation e.g. under excess light conditions , may impit) gene , 151 as it) gene  have beeNannochloropsis oceanica by overexpressing endogenous RuBisCO activase [2 production in C. reinhardtii [Recently, biomass productivity as well as lipid yield increased up to 40% in the oleaginous activase . Conversnhardtii , likely Chlamydomonas rbcL with sequences representing natural variants of this gene, which yielded isoforms with higher Vmax of carboxylation catalysis [C. reinhardtii, which succeeded in enhancing photosynthetic efficiency in D. bardawil [2 Inducible (LCI) proteins in C. reinhardtii, under conditions which typically repress their synthesis (i.e. high CO2 concentration), increased biomass production under elevated CO2 conditions as much as 80% than control strain [Further strategies included (i) the PCR-based gene shuffling of atalysis ; (ii) reatalysis ; (iii) obardawil ; finallyl strain .Dunaliella) or high pH (e.g. Spirulina), thus outcompeting naturally occurring contaminants. Hence, a trait which confers competitive advantage over undesirable microorganisms, is crucial both to increase the biomass productivity and to reduce the operating costs for maintenance of axenic cultures , thus trophic conversion by metabolic engineering would be desirable. Chlamydomonas cells expressing the hexose transporter HUP1 (monosaccharide-H+ symporter from C. kessleri) metabolized externally supplied glucose for heterotrophic growth and showed higher H2 production capacity; however, results suggest that glucose cannot fully replace acetate as a C source, for long-term growth in the dark [Some algal species are strict autotrophs or are highly selective for their C source  [Other algal species can metabolize a large array of sugars, and strongly increase their productivity under heterotrophic or mixotrophic growth conditions ; howeverectively . Contrarhardtii) , thus lahardtii) , 166. Alhardtii) , the nathardtii) .An overview of the major genetic manipulations which may lead to an improvement of biomass productivity is represented in Fig. smart strains” with improved yield, in order to make the production successfully marketed. The opportunities offered by investments in both basic and applied research, are considerable: (i) the rapid evolution of genome sequencing techniques will help defining the gene networks controlling growth, while -omics approaches allow to identify regulatory points of cellular pathways, thus enabling manipulation of key metabolic steps; (ii) prospective redesigns of algal system include light-to-biomass conversion efficiency, oil content/composition, nutrient recovery capacity; (iii) extend the genetic transformation techniques, now carried out successfully in few species only, to the most industrially-relevant species, will offer the opportunity to address the biological constraints limiting growth yield; finally, (iv) the development of reproducible genome editing techniques will permit a fine matching of the primary metabolism to the mass culture conditions, or the development of molecular strategies for strain containment. Encouraging results have recently been obtained by boosting light-use-efficiency or by strengthening specific metabolic pathways. Additional research efforts and funding for implementing innovative biorefineries, will realistically support progress toward next-generation algal biotechnology.Commercially cultivated for several decades, microalgae are now recognized to offer a great potential for exploitation in different fields including pharmaceuticals, aquaculture and renewable energies. Former efforts in their industrial applications mainly focused in optimizing culture parameters and selecting the best performing wild type strain. However, to promote cultivation of microalgae as a new biotechnological sector, a number of challenges still have to be overcome. Domestication strategies achieved by genetic and metabolic engineering will be crucially important to isolate “


Plasmodium gene functions in mosquito and liver stages remain poorly characterized due to limitations in the throughput of phenotyping at these stages. To fill this gap, we followed more than 1,300 barcoded P. berghei mutants through the life cycle. We discover 461 genes required for efficient parasite transmission to mosquitoes through the liver stage and back into the bloodstream of mice. We analyze the screen in the context of genomic, transcriptomic, and metabolomic data by building a thermodynamic model of P. berghei liver-stage metabolism, which shows a major reprogramming of parasite metabolism to achieve rapid growth in the liver. We identify seven metabolic subsystems that become essential at the liver stages compared with asexual blood stages: type II fatty acid synthesis and elongation (FAE), tricarboxylic acid, amino sugar, heme, lipoate, and shikimate metabolism. Selected predictions from the model are individually validated in single mutants to provide future targets for drug development.  •P. berghei knockout (KO) mutants analyzed for stage-specific phenotypes1,342 barcoded •Life-stage-specific metabolic models reveal reprogramming of cellular function•High agreement between blood/liver stage metabolic models and genetic screening data•Essential metabolic pathways for parasite development and mechanistic origin revealed Plasmodium to establish infection in mammalian hosts.A genome-scale knockout screen identifies genes important for the entire malaria parasite life cycle and reveals growth bottlenecks in  Plasmodium, remains a disease of major significance to global public health. Despite increased attention and funding, malaria still kills about half a million people each year, and the combination of drug and insecticide resistance slows down progress against this deadly disease . These vectors integrate efficiently into the genome due to their long homology arms . We used this model to examine the reasons underlying the observed loss-of-function phenotypes and provide new insights into liver-stage physiology, systematically predicting thermodynamic bottlenecks, genetic interactions, and growth-limiting nutrients. To validate hypotheses generated from this model, we produced and analyzed individual knockout mutants for 20 genes and compared their phenotypes with their model-predicted essentiality.In this study, we generated pools of these blood-stage-viable knockout mutants and analyzed their phenotypes throughout the entire parasite life cycle for the first time. Using barcode sequencing, we measured changes in the relative abundance of knockout mutants in midgut oocysts, salivary gland sporozoites, and in mice following injection of sporozoites, revealing stage-specific functions for 461 genes, including transcription factors, structural proteins, and enzymes. We combined the data of the genetic screen with a liver-stage transcriptome  to generPlasmodium parasites can be propagated continuously to drug-select for knockout mutants, meaning that only genes that are not required for blood-stage development can be investigated at later stages of the cycle using barseq. Extending barseq phenotyping beyond blood stages faces three potential obstacles: (1) population bottlenecks, (2) changes in ploidy following gamete fusion in the midgut, and (3) segregation of mutant alleles in the oocyst. In a pilot screen, we first tested whether barcoded alleles could be transmitted robustly through the population bottleneck posed by the only approximately 400 oocysts that in our hands form on average on each infected Anopheles stephensi midgut. Using knockout vectors targeting 15 genes with known functions at the liver stage and 19 control and test genes  screen. To minimize random losses of barcodes through non-representative sampling, the pool size was limited to 60 mutants, and each pool was studied in three independent transmission experiments , which is required for sporozoite gliding, entry into salivary glands and hepatocyte invasion  at the SG-B2 transition showed a remarkable enrichment (p < 0.01) for metabolic genes  (in silico P. berghei (iPbe) model on a set of metabolic tasks  , we baseic tasks  and on aic tasks . We builic tasks . In a unic tasks A. We iniic tasks . The iPbic tasks B and S2Cic tasks A, workinTo evaluate computationally the essentiality of the 428 genes in iPbe by PhenoMapping, we initially assumed unlimited transport capabilities, but we worked with the known range of metabolite concentrations and gene expression levels , and we in silico knockouts of all genes individually (in silico for growth in iPbe-liver (PlasmoGEM resource). iPbe-liver predicts essentiality with 85% accuracy and a Matthew Correlation Coefficient (MCC) of 0.51, the latter providing a more appropriate measure considering the different number of dispensable and essential SG-B2 phenotypes (116 and 16 respectively). Similarly, iPbe-blood is 84% accurate at an MCC of 0.7  pathway FASII,  and are ectively . Our scrion hpi; C. Aside ormation D. The di mutants E and S4BPhenoMapping with iPbe suggests that the knockout of FASII can lead to loss of fitness in three different physiological scenarios . In the de novo or taken up from the host cell, can undergo cyclical elongation and desaturation before being integrated into phosphoglycerolipids and cellular structures. Little work has been performed on FAE in malaria parasites, which harbor a pathway in the endoplasmic reticulum (ER) that comprises three elongases , a ketoacyl-CoA reductase (KCR), a hydroxyacyl-CoA dehydratase (DEH), and an enoyl-CoA reductase (ECR), required for the final elongation step  also showed a complete growth arrest in the mosquito. Ookinetes and oocysts were produced in normal numbers  and unsaturated fatty acids (such as linoleate) cannot be scavenged at a sufficient rate. Long-chain fatty acids have diverse roles in protein trafficking, synthesis of cellular structures, and signaling. Some of these functions are also likely important in blood stages, which we hypothesize obtain sufficient long-chain fatty acids from the host red blood cell or serum .in vitro , lacking the PPM enzyme involved in the production of GDP-mannose, showed strongly reduced oocyst numbers, despite normal levels of ookinete formation, and these parasites failed to produce salivary gland sporozoites . We produced single knockout mutants for each of these genes . ΔUSP parozoites A and S6Bnto mice E and S6Celopment D, in agrelopment B, with aelopment A, prevenPhenoMapping with iPbe liver and blood predicts that the parasite can take up nucleotide sugars from the host cells (both the erythrocytes and hepatocytes), and a difference between the uptake rate or availability of the different nucleotide sugars from the host would explain the differences observed between blood- and liver-stage phenotypes . This anAll genes that had been shown to have a more than 100-fold reduction in abundance in the screen showed a clear delay in pre-patent period when sporozoites of single knockout mutants were injected into mice E, and 7EPlasmodium mosquito- and liver-stage metabolic pathways, we performed the first systems-level analysis of Plasmodium pre-erythrocytic physiology by combining large-scale genetic screening with metabolic modeling. Barseq sequencing enabled us to analyze the phenotypes of >1,300 gene knockout parasites at three developmental transitions, namely (1) between blood stages and mosquitoes, (2) between the mosquito midgut and the salivary glands, and (3) as the parasites transitioned from the mosquito salivary gland to establish and undergo development within the liver. Although in the polyploid stages cross-fertilization leading to heterozygosity imposes limits on the power of pooled screens, we find that barcoded knockout alleles can be effectively transmitted through population bottlenecks to the haploid sporozoite stage in the salivary gland, allowing mutants to be screened for the first time at a significant scale for gene functions during subsequent development.To fill the critical knowledge gap of P. berghei, which does not allow blood-stage essential genes to be screened at other life cycle stages, (2) the coverage of the PlasmoGEM resource, which extends to only around 60% of the core genome, and (3) the inherent error rate of any genetic screen. To compensate for these challenges, we developed an iPbe-liver metabolic model that integrates data from the current screen with available genomic, transcriptomic, and metabolomic data to predict with good accuracy the phenotypes associated with a majority of the metabolic subsystems, such as fatty acid and amino sugar biosynthesis. The overall Matthew correlation coefficient of our blood- and liver-stage models  is similar to MCC scores of 0.6 achieved for model organisms E. coli  the current genetic system for  E. coli  and S. crevisiae . These cPlasmodium liver stage illustrate how the combined use of metabolic modeling and high-throughput gene knockout screening can generate deep new insights into a poorly tractable organism. iPbe-liver will guide the design of future gene knockout experiments and analysis of metabolic capabilities in the liver stages for the 141 genes predicted to be essential at this stage but for which single knockout parasites have not been generated or phenotyped. Moreover, iPbe-liver predicts liver-stage-specific essentiality for 27 genes that are fully dispensable in iPbe-blood (Plasmodium metabolism. Possible avenues include (1) further screens as the PlasmoGEM resource grows to cover more of the remaining 32% of 428 genes in the model that currently lack vectors, (2) screens for synthetic lethal interactions predicted by the model, which are now possible in P. berghei  Plasmodium growth in the liver stages.Validating the screen and model with individual mutants confirmed the crucial role of the FASII pathway for liver stages and further revealed the essentiality of the FAE pathway for liver-stage parasites and its coupled enzymes, e.g., CBR in the ER, demonstrating a clear dependence of the liver stage on its own fatty acid metabolism. Through modeling genomic data, we show here for the first time that not only short-chain fatty acids but also long-chain and unsaturated fatty acids are insufficient to support the rapid P. berghei. We hypothesize that the parasite has a differential ability to take up from the host sufficient quantities of specific amino sugars, with the uptake of N-acetyl-glucosamine appearing to be significantly limited in the liver stage. This would to our knowledge be the first indication of an intracellular pathogen taking up amino sugars from its host cell. Despite the biosynthesis pathway of amino sugars being identical in P. falciparum and P. berghei, the amino sugar metabolism of blood stages appears to differ between these malaria parasites. Data from phenotyping of blood stages of knockout mutants .Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Volker Heussler  aged eight to fourteen weeks were infected with Mice were bred at the Wellcome Sanger Institute or purchased from Envigo. Transfected parasites were injected intravenously into the tail of female adult BALB/c inbred mice aged 8-22 weeks (median age 10 weeks). This animal model was chosen to minimize host genetic variability and to obtain robust infections with a low incidence of cerebral pathology. Experimental groups consisted of three mice housed together. Three internally controlled biological replicates per parasite pool proved adequate to identify phenotypes with confidence.ad libitum. The health of animals was monitored by routine daily visual health checks. The parasitemia of infected animals was determined by FACS analysis.The work in Bern (Switzerland) was performed experiments in accordance with the guidelines of the Swiss Tierschutzgesetz  and approved by the ethical committee of the University of Bern (Permit Number: BE132/16). Mice were kept in specific-pathogen-free conditions and subjected to regular pathogen monitoring by sentinel screening. They were housed in individually ventilated cages furnished with autoclaved aspen woodchip, a mouse house and paper tissue at 21 ± 2°C under a 12:12 h light-dark cycle at a relative humidity of 55 ± 10%. They were fed a commercially prepared autoclaved dry rodent diet and water, both available 2 chamber and parasites isolated following exsanguination. For feeding of mosquitoes, upon reaching a parasitemia of 7%–15%, mice were anaesthetized with a terminal dose of ketamine:xylazine and when no longer reacting to touch stimulus were placed on a cage of approximately 150 mosquitoes. For in vivo liver experiments, female C57BL/6JRj mice  were injected with sporozoites and were euthanized when parasitemia reached 2% to prevent any incidence of cerebal malaria.Female BALB/c mice  were used to maintain transfected parasites and for feeding of mosquitoes with parasites. Mice were injected via an intraperitoneal or intravenous route. When parasitemia reached 2%–5%, mice were euthanized in a COad libitum. The health of animals was monitored by routine daily visual health checks. The parasitemia of infected animals was determined by methanol fixed and Giemsa-stained thin blood smears. Transfection and phenotypic analysis were performed with female OF1 mice .All experiments were approved by the Animal Experiments Committee of the Leiden University Medical Center (DEC 12042 and 14207). The Dutch Experiments on Animal Act is established under European guidelines . Mice were kept in specific-pathogen-free conditions and subjected to regular pathogen monitoring by sentinel screening. Mice were housed in individually ventilated cages furnished with autoclaved aspen woodchip, fun tunnel, a wood chew block and Nestlets at 21 ± 2°C under a 12:12 h light-dark cycle at a relative humidity of 55 ± 10%. They were fed a commercially prepared autoclaved dry rodent diet and water, both available Anopheles stephensi. They were bred in-house in conditions of 27°C, 80% humidity. Following infection with P. berghei parasites, mosquitoes were maintained at 20.5°C at 80% humidity. All mosquitoes were supplied with 8% fructose solution  and for dissection were anaesthetized in chloroform vapor before submersion in 70% alcohol.Mosquitoes used at the University of Bern were of the strain P. berghei  isolation from blood was performed using phenol-chloroform extraction as described in . A totalTo sequence the vector-specific barcodes, 0.1-10 μL of each DNA sample served as a template for a PCR reaction using Advantage 2 Taq polymerase (Clontech) with primers arg444 and arg445 , which bind to constant annealing sites flanking each barcode. For sample-specific indexing, 5 μL of the first amplicon served as template for a further ten amplification cycles  using one generic oligonucleotide (PE1.0) and one of a set of 64 sample-specific indexing oligonucleotides . A totalP. berghei parasites were generated for a total of 20 genes using deletion plasmids obtained from PlasmoGEM . The parental P. berghei ANKA line used for transfection of GOMO-GFP-Cherry-FACS vectors was cl15cy1 (Single gene knockout r.ac.uk/ ). The paghei.eu; ) which c cl15cy1 . Parasithdhfr::yfcu selectable marker (SM), under the control of the P. berghei eef1α promoter region and 3′ terminal sequence of pbdhfr. In the cases of KCR and PMM, these sKO lines are sometimes referred to as version 1 .For genes PDH-E2 (PBANKA_0505000), HCS1 (PBANKA_0511000), FabD (PBANKA_1410500), FabG (PBANKA_0823800), FabH (PBANKA_0308200), LipA (PBANKA_1357500), ELO-A (PBANKA_0820900), KCR (PBANKA_0522400), CBR (PBANKA_1143400), USP (PBANKA_1232300), PMM (PBANKA_0501700), PGM3 (PBANKA_0918200), GFPT (PBANKA_0509300), LPD1 (PBANKA_1446900), IS-SDHA (PBANKA_1428800) and GCH1 (PBANKA_1438900), single knockout parasites were generated using a vector with a “3xHA-hdhfr-yFCU” selection cassette, which contains a hdhfr::yfcu selectable marker (SM), under the control of the P. berghei eef1α promoter region and 3′ terminal sequence of pbdhfr but additionally cassettes for GFP and mCherry expression, controlled by the hsp70 and eef1α promoters respectively and terminated by the 3′ terminal sequence of pbdhfr. In the cases of KCR and PMM, these sKO lines are sometimes referred to as version 2 . The GOMO cassette was adapted for use with PlasmoGEM vectors in collaboration with Olivier Silvie and adapted from (For genes KCR (PBANKA_0522400), PMM (PBANKA_0501700) and UAP (PBANKA_1356600), single knockout parasite lines were generated using a vector with a “GOMO-GFP-Cherry-FACS” selection cassette, which contains not only the ted from .NotI. Parasites of line 1868cl1 or cl15cy1 were transfected with the 3xHA-hdhdfr-yFCU or GOMO-GFP-Cherry-FACS constructs respectively using standard transfection technologies and transformed parasites selected by positive selection with pyrimethamine , collected in 500 μL WME with 10% fetal bovine serum. 1,000 parasites were immediately injected intravenously into a naive mouse.Parasites harboring a 3xHA-hdhfr-yFCU cassette were cloned by limiting dilution. Parasites harboring a GOMO-GFP-Cherry-FACS cassette were enriched by FACS sorting for red/green parasites, using a procedure based on that of ; once thPlasmoGEM constructs containing hdhfr::yfcu (SM), the PFG-separated chromosomes were hybridized with a mixture of two probes: a probe of the hdhfr gene and a ∼800cbp fragment of the 5′UTR of PBANKA_0508000 located on chromosome 5 -separated chromosomes and diagnostic PCR analysis . To showmosome 5  for line  and allowing female mosquitoes to feed directly on these mice  and GOMO-GFP-mCherry-FACS parasite lines (ΔKCRese mice .V1 or 1868cl1) were intravenously injected into each of three naive mice. The parasite growth was routinely recorded from day 1 until 6 post-injection by FACS (ACEA Novocyte machine).To assess relative development of blood stage parasitemia, 100,000 asexual blood stage parasites  were counted in Giemsa-stained slides according to the classification in Janse et al., 1985.Gametocyte production of those lines showing reduced oocyst numbers was determined as described . The gamine/NaCl . BrieflyA. stephensi mosquitoes were allowed to feed for 1 h on anaesthetized infected mice whose asexual parasitemia had reached at least 7% determined by FACS and were carrying comparable numbers of gametocytes as determined on Giemsa stained blood films. Day 6-8 post feeding 10 mosquitoes were dissected in PBS1X and oocysts on their midguts counted. Images were recorded using a 5 × objective on a Leica DM 6000 B microscope fitted with a Leica DFC 350 FX camera. Pictures of the 10 mosquito midguts were taken and the oocyst number per midgut was recorded by using the software ImageJ (FIJI) for the 1868cl1 and the mutant parasite lines. Red or green fluorescent oocysts were recorded for the 3xHA-hdhfr-yFCU or GOMO-GFP-Cherry-FACS parasite lines respectively. The results were statistically evaluated using Prism (GraphPad) with a one-way analysis of variance (ANOVA) test with Dunnet’s multiple comparisons. At day 18-22 post feeding, salivary glands were dissected from up to 50 blood-fed infected mosquitoes, homogenized and assessed for the presence or absence of sporozoites.For mosquito transmission experiments, 100-150 in vivo liver stage parasite development, at Day 18-22 post feeding, 3-15 mosquitoes were dissected and their salivary glands crushed with a homogenizer to release sporozoites, which were then quantified using a hemocytometer. Five thousand salivary gland sporozoites were intravenously injected into three female C57BL/6JRj  for the 1868cl1 or the mutant parasite line for each experiment. For the crossed parasites, sporozoites were counted using a 20x objective with green fluorescence to discriminate between GFP+mCherry+ mutant and cl15cy1 sporozoites and six rather than three mice were injected. Three of the mice injected with the crossed parasites were supplied with pyrimethamine pressure the day after injection to eliminate cl15cy1 parasites within the blood.To analyze +GFP+ parasites for crossed knockout parasites. For ΔHCS1, ΔFabD, ΔFabG, ΔFabH, ΔLipA and ΔELO-A, relative blood stage parasitemia was determined by luciferase assay, taking advantage of the constitutive luciferase expression of the background 1868cl1 parasite strain. For this, 2 μl tail blood was added to 20 μl 1x Cell Culture Lysis reagent (Promega). Following incubation on ice for 1 min and vortexing for 10-15 s, the sample was centrifuged at 12,000 rpm for 15 s and the supernatant was stored at −20°C. 20 μl of this lysate was mixed with 100 μl Luciferase Assay Reagent (Promega) and relative light units were measured immediately using a Spectramax L Microplate Reader (Molecular Devices). A sample of uninfected blood was used as a control and relative light units for this sample were subtracted from those of experimental samples. In all cases, mice were allowed to reach a parasitemia of 2%.For ΔPDH-E2, ΔKCR (xWT), ΔCBR, ΔUSP, ΔPMM (xWT) and ΔUAP (xWT) parasites, parasitemia was monitored daily by FACS analysis of mouse tail blood (based on mCherry fluorescence for standard KO experiments and based on mCherryP. berghei parasites for 2 h. Infections were performed in triplicate. The infected cells were then detached using accutase (Innovative Cell Technology) and seeded a lower density. At 48 hpi, cells of one plate were fixed with 4%PFA/PBS for 10 min. Parasite size and number were determined by automated microscopy  monitoring the mCherry signal in the case of KO parasites generated with 3xHA-hdhfr-yFCU-based vectors and GFP in the case of KO parasites generated with GOMO-GFP-Cherry-FACS-based vectors. At 65 hpi the detached cells in the medium of infected cells were quantified by fluorescence microscopy (Olympus CKX41). The detached cell formation rate was calculated as the percentage of parasites at 48 hpi that form detached cells. The results were statistically evaluated using Prism (GraphPad) with a one-way analysis of variance (ANOVA) test with Dunnet’s multiple comparisons.HeLa cells were seeded in a 96-well plate at high density the day before infection and were infected with ∼20,000 Plasmodium genes and metabolic reactions based on the PlasmoDB  and folA draft metabolic network of an organism involves a set of enzymes (and its associated reactions and metabolites) based on the functional metabolic annotation of the organism’s genome. One could annotate the metabolic functions to the genome of an organism of interest using various sources of information: available experimental evidence, annotated genomes of closely related organisms, or blasting against previously characterized proteins, which are proteins with an annotated metabolic function.−15, minimal score ratio of 0.8, and minimal score ratio for a knocked-out gene of 0.3 as input for the function getKEGGModelForOrganism in RAVEN). We then merged the two draft metabolic networks to incorporate all annotated metabolic functions, using as reference the network obtained from iPfa. We included 63 genes that had been annotated based on KEGG and were not part of the draft metabolic network generated from iPfa.We generated two draft metabolic networks of iPbe; a draft network that we annotated based on iPfa , and a draft network that we newly annotated based on KEGG , C20120_c (S-Farnesyl protein), C00550_r (Sphingomyelin), G00009_r (N-Glycan), C00344_r (Phosphatidylglycerol), quinone_m (Quinone), 2Fe2S_a ([2Fe-2S] iron-sulfur cluster), 2Fe2S_m ([2Fe-2S] iron-sulfur cluster), 4Fe4S_a ([4Fe-4S] iron-sulfur cluster), 4Fe4S_m ([4Fe-4S] iron-sulfur cluster), C00268_c (Dihydrobiopterin). We recalculated the stoichiometric requirements for the biomass building blocks following the procedure described for iPfa . Removed building blocks are: C00043_c (UDP-N-acetyl-D-glucosamine), C00096_c (GDP-mannose), C00143_c , C00325_c (GDP-L-fucose), C16237_a (Protein N6- (lipoyl)lysine), C17569_m (Ubiquinone-8), C00550_c (Sphingomyelin), C00120_c (Biotin), C00029_c (UDP-glucose), C00052_c . Added building blocks are: C00004_c (NADH), C00005_c (NADPH), C00415_c (Dihydrofolate), C04549_c (1-Phosphatidyl-1D-myo-inositol 3-phosphate), C19085_c (tRNA with a 3 CCA end), C04419_a (Carboxybiotin-carboxyl-carrier protein), G13044_r (Glycosylphosphatidylinositol (GPI)-anchor protein), G13052_r (Fucosylated protein), C00143_m , C15672_m (Heme O), C00126_m  based on previous mice genome-scale models  and metaIt is normal that draft metabolic networks cannot produce all biomass building blocks from the available substrates. This happens because we have an incomplete annotation of genomes. One should integrate into the draft network a set of hypothetical reactions also called gap-filling reactions to properly connect the metabolites within the metabolic network and allow the production of biomass. One can look for gap-filling reactions in available databases. The hypothetical metabolic capabilities in genome-scale models might be later characterized experimentally.Escherichia coli iJO1366 to identify reactions that produce iron-sulfur clusters. We used the KEGG database and the ATLAS of Biochemistry to integrate reactions that produce proteins, glycan, and linolate. We also defined manually the synthesis of a generic protein from the Aminoacyls-tRNA; we assumed the generic protein is formed of 200 amino acids based, which is the average protein length defined in the first sequence of P. falciparum genome  gin vivo but the model predicts no growth, and False Positives (FP) when there is no growth in vivo but the model predicts growth.The workflow involves three sets of analyses . We perfin silico a gene as essential because the metabolic model lacks information regarding another protein carrying out the same function (an isoenzyme). Therefore, we looked for missing genes, intracellular reactions, or transports/uptakes/secretions in the metabolic model following a gap-filling approach. This information is missing in the metabolic model due to uncertainty in the functional annotation of the genes, the uncertainty in the transportability of metabolites across membranes, and the limited knowledge concerning the (lack of) specificity of enzymes. We can reconcile such cases through the integration of genes with higher uncertainty in the annotation. We integrated into iPbe 67 genes with putative annotation based on PlasmoDB that were not part of the initial iPbe.We first suggest a reconciliation of FNs to correct or add missing information in the model. We sequentially evaluate the redefinition of metabolic tasks and GPRs, and the integration of missing alternative biochemistry and uptakes or secretions into the metabolic model. We study the metabolic tasks impacted upon knockout of the FN gene. Such studies identify the biomass precursors whose production requires the function of the FN gene. One might identify a molecule whose production is only required in one context of study and hence should not be defined as a biomass precursor in a generic model. For example, in a previous version of iPbe, the requirement of lipoate production in the apicoplast organelle was responsible for the incorrect essentiality prediction of the fatty acid synthesis (FAS) II in the blood stages . The FASNext, we suggest a reconciliation of FPs (green) to first add missing biomass precursors and then constraints that describe a context-specific behavior. If the FPs are not blocked based on a flux variability analysis, we perform a PhenoMapping analysis to identify the conditions in which the FPs become essential. If the FPs are blocked or inactive, we perform gap-filling to connect the FP genes and reactions to the metabolic network and send the model to a second iteration through the PhenoMapping workflow. For example, in iPbe, we defined the GPI-anchored proteins (downstream metabolite) instead of the nucleotide sugars (upstream metabolite) as biomass precursors to account for the essentiality of GPI-anchor biosynthesis. In addition, we performed gap-filling  to allow the production of the GPI-anchored proteins in the endoplasmic reticulum.in silico analysis of essential genes and reactions on iPbe, iPbe-liver and iPbe-blood using the Fast-SL approach (in silico phenotypes based on the ranges of relative growth (KO/WT) predicted: between (1 and 0.9); (0.9 and 0.1); and (0.1 and 0) determined a dispensable, slow and essential in silico phenotype, respectively. The predictions of essential genes with iPbe, iPbe-blood and iPbe-liver are available in We sought for the essential genes and reactions for liver and blood development in iPbe-liver and iPbe-blood, respectively. We performed approach  within Fapproach  in the Capproach  and matTapproach  Toolbox.approach  are discin vitro and in vivo observations provided a measure of the genome-scale model’s accuracy to describe the metabolic function at the life stage in which the phenotypes were obtained, i.e., liver stages of the Plasmodium infection. The accuracy score was calculated as (TP+TN)/(TP+TN+FP+FN), where TP (true positive) and TN (true negative) define predictions that correctly simulate growth and non-growth, respectively, based on the available experimental data. While FP  and FN  describe predictions that incorrectly simulate growth and non-growth, respectively. We also calculated the Matthew Correlation Coefficient (MCC) as (TP x TN – FP x FN)/((TP+FP) x (TP+FN) x (TN+FP) x (TN+FN))0.5. In iPbe-liver, an accuracy score of 85% and a MCC of 0.51 are obtained based on the liver phenotypes of this study (Mild liver phenotypes (2-100 fold reduction) and genes blocked in iPbe (the metabolic model before integration of context-specific data) were not considered in the calculation of the accuracy score and MCC. Moreover, we evaluated the predictions per metabolic subsystem with the liver phenotypes from this study. We calculated a measure for agreement per metabolic subsystem as (TP+TN+0.5∗MP)subsystem i / (FP+FN+TP+TN+MP)subsystem i, where MP defines the number of mild phenotypes that are part of the subsystem. We called this measure degree of agreement (DoA) and the results are presented in The essentiality predictions in iPbe-liver with TFA were compared with the liver phenotypes obtained in this study. The comparison of is study . Mild liGenome-scale models are powerful platforms for the study of metabolism. Predictions on the metabolic function of an organism obtained from a genome-scale model are the product of some biological information or assumptions integrated into the model like the association of genes and enzymes, biochemistry defined in the metabolic network, or other types of data specified in the model in the form of constraints. A classification of the information integrated into a genome-scale model and mapping of this information to cellular processes will help us understand what cellular processes underlie predicted phenotypes.organism-specific and context-specific information  is not yet fully understood. The aim of the analysis of in silico minimal media (IMM) is to identify the minimum number of substrates that the cell needs for growth and if applicable the number of alternative sets of IMMs.Some organisms such as M is the total set of transport reactions between the extracellular environment and cytosol. Here, the net flux through a transport reaction i is given by the exclusive use of its forward flux vf,i (secretion) or its reverse flux vr,i (uptake), which are always positive. In the OP 1, an integer variable yi indicates whether an uptake reaction vr,i within the set of transport reactions M is inactive (yi = 1) or active (yi = 0). An uptake reaction i is inactive (yi = 1) when the reverse flux vr is blocked . Otherwise, an uptake reaction i is active (yi = 0) when it carries a minimal flux Fmin, which is defined here as 10−7 mmol/g-DW/h . We also define a capacity constraint with the maximal flux that a reaction can carry Fmax, which is 50 mmol/g-DW/h. The resolution of the OP 1 identifies the maximum number of uptake reactions that can be blocked and allow a minimum growth requirement. We defined the growth requirement by setting the lower bound of the biomass function at a 10%, pOGR, of the optimal growth yield, vopt,biomass. The uptake reactions that are active after the optimization of the OP 1 constitute an IMM. We identify alternative IMMs by integrating integer cut constraints after generating each solution iteratively (M is the set of transport reactions between the extracellular environment and cytosol (same as OP 1). The set Mk is the subset of the transport reactions in M that have inactive integers  in a solution k of the OP 1. The cut constraint enforces that the next solution k+1 has at least one integer of the set Mk different.As applied before in iPfa , the IMMratively   is to identify the minimum number of substrates that the cell needs to secrete to sustain a phenotype like growth and if applicable the number of alternative sets of IMSs.All cells secrete metabolites that are byproducts of growth and maintenance of the cell function. In parasites like i controls its forward flux vf,i instead of its backward flux vr,i.An analogous OP to OP 1 allows the study of IMS. In an IMS formulation the integer variable of a reaction The composition of the extracellular medium might have an effect on gene essentiality. The analysis of essential genes per IMM and IMS aims to identify what genes can become essential due to substrate inaccessibility and due to impossibility of metabolite secretion, respectively.in silico rich medium of 248 substrates. Analogously, we also define the IMSs in iPbe and perform gene essentiality at each IMS. We identify the genes that are essential in the IMSs and not in the in silico rich medium.As done before for iPfa , we defiM of uptake reactions. The exclusion of the IMM substrates from the set M of uptake reactions assures that the IMM substrates are available to the cell, and enables the identification of a set of substrates that one should add to the IMM to rescue growth. We identify alternative sets of substrates by integrating integer cut constraints (Equation 1) after generating each solution iteratively. We follow the same approach to identify secretions responsible for gene essentiality in the IMS formulation.The analysis of bottleneck substrates finds the substrates that one should add to an IMM to render non-essential a gene that is essential in such IMM. This method requires the prior identification of all alternative IMM compositions and the essential genes in the IMMs, as explained above. For an IMM and upon knockout of a gene that is essential in this IMM and not in the rich medium, the analysis of bottleneck substrates identifies alternative sets of substrates to add to the IMM and rescue the phenotype, in this case growth. This method uses a mixed-integer linear programming (MILP) formulation (OP 1). We apply the OP 1 when the gene of study is knocked out and when the composition of the IMM at which the gene becomes essential is excluded from the set ln) of the metabolite concentration (mol/Lcell) ranges were integrated within the TFA framework. The lowest and highest values correspond to the lower and upper bounds of the concentration variable in TFA, respectively. The constrained range of concentration limits the allowable ΔrG’ range of the reactions in which the metabolites participate and with it the flux ranges of the neighboring reactions. The physiological or generic range of concentration of 1 μM and 50 mM was considered for a metabolite if no data was available , and thereby a gene that is essential becomes non-essential.zi is associated to each metabolite concentration range in the set M of metabolites for which experimental concentration data is available. The variable zi is inactive (zi = 0) when a metabolite i can keep its experimental concentration range, Ci ∈ . We define the experimental concentration range of a metabolite i with an upper concentration value, CEXPmax,i, and a lower concentration value, CEXPmin,i, based on the standard deviation from the average measurement reported in the metabolomics study. We work with concentration values in mol per unit of volume in liters of cell.As presented before for iPfa , the anazi is active (zi = 1) when the concentration range of a metabolite i should be relaxed (increased), Ci ∈ , compared with its experimental concentration range. In this way, the new minimal concentration Cmin,i is lower than the minimal experimental concentration CEXPmin,i , and the new maximal concentration Cmax,i is greater than the maximal experimental concentration CEXPmax,i .The variable zi = 0) or the relaxed (if zi = 1) concentration ranges determine the bounds of the logarithm (ln) of the metabolite concentration Ci, which is a variable in the TFA framework. The logarithm of the metabolite concentration (ln(Ci)) will serve to calculate the Gibbs free energy of the reaction (ΔrG’).The experimental . We consider a gene is essential when its knockout leads to a reduction in optimal wild-type growth of 90% or more. Hence, we defined the growth requirement of the OP 2 by setting the lower bound of the biomass function at a 10%, pOGR, of the optimal growth yield, vopt,biomass. This analysis is done when the gene of study is knocked out, which implies that all its associated reactions are knocked out based on the gene-protein-reaction associations (vKO = 0).Here, a gene becomes essential when we account for thermodynamic constraints and some metabolite concentrations belong to the experimental concentration range M is the set of metabolites for which experimental concentration data is available (same as OP 2). The set Mk is the subset of the metabolites in M that have active integers  in a solution k of the OP 2. The cut constraint enforces that the next solution k+1 has at least one integer of the set Mk different.We identify alternative sets of metabolites by integrating integer cut constraints after generating each solution iteratively   quantify the amount of mRNA present in a cell at a certain time point of its life cycle, thereby suggesting which genes and reactions might be active at that time point. Gene expression might change between the blood and liver stages of pl, and ph to associate to lowly and highly expressed reactions. TEX-FBA translates gene levels to reaction levels using the gene-protein-reaction rules in the model. We assumed that the combination of lowly and highly expressed genes in a gene-protein-reaction rule with OR and AND associations allow a high and low flux, respectively, through the associated reactions. TEX-FBA then performs flux variability analysis (FVA) to identify the allowable flux ranges (FR) of the reactions at the conditions of study, and associates within the FVA ranges a maximum allowable flux through the lowly expressed reactions and a minimum required flux through the highly expressed reactions using a MILP formulation. For high-labeled reactions we enforced a minimal flux such that FR = , while we enforced a maximum flux value for low-labeled reactions such that FR = . The maximum number of highly and lowly expressed reactions that satisfy the associated FRs define a maximum consistency score. TEX-FBA allows the identification of all alternative gene expression profiles for the maximum consistency score. See next sections for a description of the input parameters in the integration of gene expression into iPbe.The TEX-FBA methodology  maximizeGene essentiality might change based on gene expression. For instance, cells might have redundant metabolic pathways to produce an essential metabolite. If both pathways are active simultaneously (their associated genes are expressed), they will be part of synthetic lethal pairs. If only one of these pathways is active, it will be essential for growth. The goal of this approach is to identify genes that become essential upon integration of gene expression data into a genome-scale model.We integrated gene expression data following the TEX-FBA approach , and we in silico essentiality with transcriptomics data integrated. In this context, a lack of regulation indicates that a lowly expressed gene cannot get upregulated to compensate for the knockout of a highly expressed gene.Transcriptional regulation links metabolism and gene expression in the cell . Upon knWe knocked out all lowly expressed genes by removing them from the GPR associations. We then identified essential genes at alternative gene expression profiles as described above.Integration of gene expression data into a genome-scale model rewires the distribution of fluxes in the metabolic network. New genes might become essential due to gene expression. One could identify reactions whose flux levels at a gene expression profile render a gene essential.After identification of the gene expression profiles within TEX-FBA  and the in silico essential genes for growth) to underlying cellular processes . One can compare the predicted phenotypes with available experimental phenotypic data to suggest cellular processes that might happen in the conditions/context/life-stage studied. Such hypotheses on context-specific cellular physiology can guide the generation of context-specific metabolic models.PhenoMapping maps predicted phenotypes . For example, there is a gene predicted as essential at an IMM and thanks to PhenoMapping we know the substrates that should be available at the IMM to make this gene non-essential. When the experimental phenotype describes this gene as essential, we can suggest the substrates mapped might not be available to the cell. Similarly, when the experimental phenotype defines this gene as dispensable, we hypothesize the substrates mapped might be available to the cell in the conditions studied. We note that there are genes with different experimental phenotypes  that are mapped to the same cellular process. In this case, we suggest treating as dispensable all genes associated to this cellular process, which will avoid false negatives . We suggest performing the PhenoMapping analysis with a context/condition/life-stage agnostic metabolic model that integrates thermodynamic constraints with generic concentration ranges. We generate a context-specific metabolic model by integrating information into this context-agnostic model in the following order: we first define the available substrates, we second constraint the possibility of secretion, we third integrate metabolomics data, we next integrate transcriptomics data. Any of these steps might be skipped if there is no omics data available, e.g., we did not integrate metabolomics data into iPbe-blood or iPbe-liver.in silico essential genes that we did not identify in the independent analysis of cellular processes with PhenoMapping. PhenoMapping could also be applied to perform combinatorial studies of cellular processes that are simultaneously responsible for a phenotype. A combinatorial analysis was not performed here.We note that the final context-specific model that integrates simultaneously more than one cellular process might present new We provide here details of the PhenoMapping analysis on iPbe used to generate iPbe-blood and iPbe-liver, to be read in parallel with in silico unconditionally essential genes in iPbe, we performed essentiality studies with TFA using a rich medium with 248 substrates, and without integrating metabolomics or transcriptomics data into iPbe. We identified 118 such unconditionally essential genes in iPbe  (The rich medium in iPbe comprises 248 substrates (see definition of media in reconstruction of iPbe). We searched for the um (IMM)  (method um (IMM) .in silico minimal secretion (IMS) sets that allow growth in iPbe under all the 248 possible secretion conditions. The IMS results are the same in iPbe-for-blood and iPbe-for-liver. There is at least one metabolite that should be secreted when growth is required and there are 11 alternative such metabolites . The analysis of bottleneck metabolites suggests that the concentration ratio between UDP-glucose and UMP in the cytosol is responsible for the essential function of PBANKA_1232300. When we disregard the experimental concentration range of UDP-glucose and allow its concentration to vary between 1 μM and 50 mM, the gene PBANKA_1232300 becomes dispensable in iPbe. Overall, the integration of metabolomics data from P. falciparum into iPbe increases the consistency with the blood-stage phenotypes but not with the liver-stage phenotypes. These results might suggest that the intraerythrocytic P. berghei shows similar metabolite levels to those measured in blood stage trophozoites of P. falciparum. Despite this observation, we did not integrate metabolomics data into iPbe.As defined before , bottlenpl = 2x10−5, ph = 2 x10−3, low P value = 25, and high P value = 75. There is a unique blood-stage and two liver-stage specific metabolic profiles that renders a maximum consistency score.We integrated transcriptomics data (RNaseq for the blood and liver stages ) into iPin silico blood-stage essential, there are blood phenotypes for 49 genes and these define 24 essential genes, 7 slow genes, and 18 dispensable genes. Similarly, out of the 70 genes that are in silico liver-stage essential, there are liver phenotypes for 34 genes and these define 8 essential genes, 7 slow genes, and 19 dispensable genes . These are substrates that should be added to the joint IMM to avoid false predictions of essentiality. We included such substrates in the joint IMM. We followed this approach to increase the consistency between in silico and in vivo observations and avoid increasing the disagreements. We additionally allowed the uptake of all amino acids and inorganics that were not part of the IMMs, since they are available in any growing condition. Based on this procedure, we suggest a medium of 90 and 94 allowed uptakes for the intraerythrocytic and intrahepatocytic trophozoite stages of P. berghei, respectively. We defined this media composition in iPbe-blood and iPbe-liver (see lb).We first defined a medium that includes all substrates present in the alternative IMMs  when the uptake of oxyhemoglobin is allowed  or not allowed . We performed essentiality analysis lb). Glucose is known to be the primary carbon source in the malaria parasite  is essential in the blood stages and one (PBANKA_0820900) in the liver stages based on the available phenotypes.P. berghei.We did not integrate metabolomics data since there was no dataset available for To generate the final blood- and liver-stage specific iPbe models we relaxed the bottleneck reaction levels underlying dispensable phenotypes.P. berghei iPbe  describe the metabolic tasks associated with the present pathways in the liver stage development of the malaria parasites. All references to intracellular and extracellular metabolites refer to metabolites inside or outside the parasite cell. Blood ((P. berghei growth. A white box indicates that no phenotype is available for the corresponding gene and life stage. Enzyme abbreviated names as used in the Kyoto Encyclopedia of Genes and Genomes (KEGG (http://www.genome.jp/kegg/) or PlasmoDB , and the standard deviation between technical duplicates was calculated for each log-proportion. A moving average was taken by abundance with a window width of 11 across genes in the same sample. We enforced monotonicity on the relationship between abundance and standard deviation. For each stage transition, B1-M, M-SG, and SG-B2, we calculated a log2 fold change by subtracting the log-proportion in the former stage from the latter. We also propagated the errors associated with each of these measurements into an estimate of the error of this fold-change. In the case of the salivary gland to blood transition we additionally normalized for reductions that might be due to impaired blood stage growth, by correcting with a factor calculated from the Bushell et al., 2017 blood stage growth measurements of each gene, allowing for the three days of growth that mutants were given following sporozoite injection. Error values for blood stage growth measurements were again propagated.Comparison of data from the technical duplicate PCR reactions made from each gDNA sample allowed identification of reactions in which technical failures had occurred, meaning that PCRs were repeated. Sampling-induced error was modeled as follows: the log proportion of all barcodes in each sample was calculated as logTypically we now had three measurements for each transition for each gene. We amalgamated these into a single fold-change value by taking the inverse-variance weighted mean of the available values, and propagated errors through to this final value.We calculated proportions (relative abundance) for each gene in its respective pool for each sample, meaning the barcode count divided by the total barcode counts for that sample. We plotted relative abundance on a log2 scale in the MG sample against that in the B1 sample, coloring individual data points based on phenotype: no power, reduced, not reduced. We used violin plots to illustrate variance of relative abundances at the B1 stage and observed a higher variability and lower absolute relative abundance for the mutants with no power phenotype. This can be expected because if the absolute relative abundance is low at the B1 stage, this may lead to noisy measurements.∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001).For oocyst number quantification, results were statistically evaluated using Prism (GraphPad) with a one-way analysis of variance (ANOVA) test with Dunnet’s multiple comparisons .Relative sizes of liver stage parasites were statistically compared using Kruskal-Wallis tests; .Detached cell formation data were statistically evaluated using Prism (GraphPad) with a one-way analysis of variance (ANOVA) test with Dunnet’s multiple comparisons (https://github.com/theosanderson/M-L_Screen.Raw screening data as well as the downstream data processing workflow can be found in the following github repository: P. berghei iPbe and the blood- and liver-stage specific models, i.e., iPbe-blood and iPbe-liver, in mat file format are available at the LCSB database http://lcsb-databases.epfl.ch/GEMs.The generic genome-scale metabolic model of www.github.com/EPFL-LCSB/phenomapping. The software package includes a tutorial that indicates step-by-step how to analyze context-specific information integrated into a genome-scale model using PhenoMapping. The genome-scale model iPbe and data used in this study are also available in the repository. The tutorials indicate how to reproduce the results of this paper. PhenoMapping requires the matTFA toolbox (Documented implementation of the PhenoMapping workflow in MATLAB is available on  toolbox  for TFA  toolbox  for the


E. coli communities can be established where the mutant strains are metabolically coupled. A novel algorithm, OptAux, was constructed to design 61 unique multi-knockout E. coli auxotrophic strains that require significant metabolite uptake to grow. These predicted knockouts included a diverse set of novel non-specific auxotrophs that result from inhibition of major biosynthetic subsystems. Three OptAux predicted non-specific auxotrophic strains—with diverse metabolic deficiencies—were co-cultured with an L-histidine auxotroph and optimized via adaptive laboratory evolution (ALE). Time-course sequencing revealed the genetic changes employed by each strain to achieve higher community growth rates and provided insight into mechanisms for adapting to the syntrophic niche. A community model of metabolism and gene expression was utilized to predict the relative community composition and fundamental characteristics of the evolved communities. This work presents new insight into the genetic strategies underlying viable nascent community formation and a cutting-edge computational method to elucidate metabolic changes that empower the creation of cooperative communities.Understanding the fundamental characteristics of microbial communities could have far reaching implications for human health and applied biotechnology. Despite this, much is still unknown regarding the genetic basis and evolutionary strategies underlying the formation of viable synthetic communities. By pairing auxotrophic mutants in co-culture, it has been demonstrated that viable nascent  E. coli auxotrophic mutants. Many of the knockouts in the catalog had the predicted effect of disabling a major biosynthetic process. As a result, these strains were predicted to be capable of growing when supplemented with many different individual metabolites , but the strains would require a high amount of metabolic cooperation to grow in community. Three such non-specific auxotroph mutants from this catalog were co-cultured with a proven auxotrophic partner in vivo and evolved via adaptive laboratory evolution. In order to successfully grow, each strain in co-culture had to evolve under a pressure to grow cooperatively in its new niche. The non-specific auxotrophs further had to adapt to significant homeostatic changes in cell’s metabolic state caused by knockouts in metabolic genes. The genomes of the successfully growing communities were sequenced, thus providing unique insights into the genetic changes accompanying the formation and optimization of the viable communities. A computational model was further developed to predict how finite protein availability, a fundamental constraint on cell metabolism, could impact the composition of the community .Many basic characteristics underlying the establishment of cooperative growth in bacterial communities have not been studied in detail. The presented work sought to understand the adaptation of syntrophic communities by first employing a new computational method to generate a comprehensive catalog of  Microbial communities are capable of accomplishing many intricate biological feats due to their ability to partition metabolic functions among community members. Therefore, these microbial consortia have the attractive potential to accomplish complex tasks more efficiently than a single wild-type or engineered microbial strain. Past applications include applying communities to aid in waste decomposition, fuel cell development, and the creation of biosensors . In the E. coli strains by co-culturing an L-isoleucine auxotroph with a L-leucine auxotroph iJL1678b effs) were used for the community ME-model simulations . The first set of keffs  was imposed by setting all keffs in iJL1678b-ME equal to 65 s-1. The next set of keff values (“default model”) used the default set of keff parameters included with iJL1678b-ME. Most of the metabolic keffs in this default set are determined by scaling a median keff value (65 s-1) by an estimation of the solvent accessible surface area of the enzyme complex that catalyzes the reaction  included 234 keffs from Davidi et al. [effs not estimated in Davidi et al. were imputed using the median estimated keff value from Davidi et al. (6.2 s-1). For all three keff sets, all non-metabolic processes were assigned a keff of 65 s-1.Three different sets of enzyme turnover rates  of secreting the cross-fed metabolites, first the exchange reactions into the shared compartment for all potential cross-feeding metabolites were constrained to zero, except the metabolite inferred from the experimental data (Table 2). Then the enzymatic efficiency of the outer membrane transport process of the inferred cross-feeding metabolite was altered in each strain. The outer membrane transport reactions for each inferred metabolite  have multiple outer membrane porins capable of facilitating the transport process. To account for this, the keff kinetic parameter of each porin and reaction was changed by multiplying the default keff value by the appropriate multiplier. The COBRAme software was used for all ME-model computations [To vary the proteome efficiency (kutations .https://github.com/coltonlloyd/OptAux.All code and data necessary to reproduce the presented results can be found on GitHub at E. coli K-12 BW25113 [gltA or ΔgltB knockout strain was a donor strain and the ΔprpC or ΔgdhA knockout strain was a receiving strain (Table B in S1 Appendix), respectively. These four knockout strains were used for the construction of the double knockout strains, ΔgltAΔprpC and ΔgdhAΔgltB. Each mutant was confirmed not to grow in glucose M9 minimal media without supplementation of an auxotrophic metabolite predicted by the iJO1366 model.All single gene knockouts used in this work were obtained from the Keio collection, a collection of all single gene knockouts in  BW25113 . To geneKnockout mutants were each initially grown in lysogeny broth from a single colony, then washed 3 times and resuspended in M9-4g/L glucose medium. The washed cells from each knockout mutant preculture were then transferred to fresh M9-4g/L glucose medium and co-cultured with mutants from the partner strain. Cultures were initially inoculated with equal numbers of cells from the two relevant auxotrophs, then serially propagated (100 μL passage volume) in 15 mL (working volume) flasks of M9 minimal medium with 4 g/L glucose, kept at 37°C and well-mixed for full aeration. An automated system passed the cultures to fresh flasks once they had reached an OD600 of 0.3 , a point at which nutrients were still in excess and exponential growth had not started to taper off. Four OD600 measurements were taken from each flask, and the slope of ln(OD600) vs. time determined the culture growth rates. The timescale of the evolution was reported using the cumulative number of cell divisions, as opposed to generations or days, as mutations occur primarily during cell division events .hisD endpoint clone was unable to be isolated via colony selection for ALE #11. Genomic DNA of the co-culture populations and mutant clones was isolated using the Macherey-Nagel NucleoSpin tissue kit, following the manufacturer’s protocol for use with bacterial cells. The quality of isolated genomic DNA was assessed using Nanodrop UV absorbance ratios. DNA was quantified using the Qubit double-stranded DNA (dsDNA) high-sensitivity assay. Paired-end whole genome shotgun sequencing libraries were generated using KAPA HyperPlus kits and run on an Illumina MiSeq platform with a PE600v3 kit or an Illumina HiSeq 4000 with a PE-410-1001 kit for 150bp reads. DNA sequencing data from this study is available on the Sequence Read Archive database (accession no. SRP161177).Co-culture population samples were collected at multiple midpoints throughout the ALE and sequenced. Additionally, the starting mutant strains and clones of both mutants isolated from the ALE endpoints were sequenced. The ΔS1 DataOutput of the OptAux algorithm ran for one, two, and three reaction knockouts on glucose minimal media for all carbon containing exchange metabolites. Four different competing metabolite uptake thresholds were used .(XLSX)Click here for additional data file.S2 DataAll MSE designs along with further information regarding the subsystems of the reaction knockouts and the metabolites that can restore growth in each design.(XLSX)Click here for additional data file.S3 DataThe breseq identified mutations for all samples collected in this work. Both the full output and a table with only mutations observed in the endpoint clones are provided.(XLSX)Click here for additional data file.S4 DataGenes with read coverage meeting the duplication criteria. Separate spreadsheets are provided for all samples using the mutant pair, ale number, flask number, isolate number, and replicate number to identify each sample.(ZIP)Click here for additional data file.S1 Appendix(PDF)Click here for additional data file.