Aspergillus fumigatus, an opportunistic human pathogen, frequently infects the lungs of people with cystic fibrosis and is one of the most common causes of infectious-disease death in immunocompromised patients. Here, we construct 252 strain-specific, genome-scale metabolic models of this important fungal pathogen to study and better understand the metabolic component of its pathogenic versatility. The models show that 23.1% of A. fumigatus metabolic reactions are not conserved across strains and are mainly associated with amino acid, nucleotide, and nitrogen metabolism. Profiles of non-conserved reactions and growth-supporting reaction fluxes are sufficient to differentiate strains, for example by environmental or clinical origin. In addition, shotgun metagenomics analysis of sputum from 40 cystic fibrosis patients  before and after diagnosis with an A. fumigatus colonization suggests that the fungus shapes the lung microbiome towards a more beneficial fungal growth environment associated with aromatic amino acid availability and the shikimate pathway. Our findings are starting points for the development of drugs or microbiome intervention strategies targeting fungal metabolic needs for survival and colonization in the non-native environment of the human lung. Aspergillus fumigatus and analyze fungal metabolism of infection of the lung of cystic fibrosis patients, finding that the fungus shapes the lung microbiome to promote its own growth.Here, the authors generate strain-specific genome-scale metabolic models of  The common mold Aspergillus fumigatus is essential for environmental decomposition but poses a serious threat to hospitalized patients, particularly those who are immunocompromised or have pulmonary diseases such as cystic fibrosis4. Annually, more than 1 million people have invasive aspergillosis (IA), a systemic multi-organ affecting disease starting by A. fumigatus infecting the lung, and 3 million have chronic pulmonary aspergillosis. Both conditions have high mortality rates and diagnosis remains challenging . In addition, A. fumigatus contributes substantially to fatal disease progression in chronic obstructive pulmonary disease, which appears to have a much higher prevalence than previously estimated5, while A. fumigatus is related to as many as half of the worldwide cystic fibrosis cases6.Fungal infections are an emerging and costly concern for human health and health careA. fumigatus isolates possess to cope with external stresses or accessible nutrient profiles in challenging environments such as the human lung remain largely unknown. Recently, we explored the genetic diversity of A. fumigatus and found a remarkably low fraction of core genes shared by all members of the species 7. How the genetic diversity of A. fumigatus influences phenotypic and metabolic heterogeneity, and particularly the ability to thrive in the non-native niche of the human lung, has not been addressed.The distinct characteristics that 8. We previously applied GEM analysis to reveal gut microbiome species that influence colonization levels of the opportunistic fungal pathogen Candida albicans9. The exponentially increasing number of available genome sequences makes the reconstruction of multistrain GEMs possible. The first multistrain-GEM collection of Escherichia coli enabled the definition of strain-specific adaptation to nutrition availability and the prediction of nutritional auxotrophies in some strains10. Protocols and databases were consequently updated to allow for bacterial GEM reconstruction at strain-level resolution12, although reconstructions of multistrain-GEMs remain to be explored in eukaryotes.One promising approach to studying the metabolic capabilities and growth dependencies of pathogens is genome-scale metabolic model (GEM) reconstruction and analysisA. fumigatus as a fungal model organism. By defining metabolic differences among 203 environmental and 49 clinical strain-specific GEMs we identified metabolic reactions that differ between the two populations. Subsequently, we performed shotgun metagenomics on sputum from 40 cystic fibrosis patients before and after they were diagnosed with an A. fumigatus colonization. Based on computationally defined metabolic output of the lung microbiome, we propose that the presence of A. fumigatus shapes the metabolic landscape of the lung microbiome to be favorable for fungal growth. Resolving the impact of genetic diversity on A. fumigatus metabolism is important to extending our understanding of adaptation mechanisms that likely involve aromatic amino acid metabolism and the shikimate pathway for ultimately guiding the development of new antifungal therapies.In this study, we provide multistrain-GEM reconstruction using A. fumigatus metabolism 14. This approach allowed us to acquire as many Aspergillus-associated reactions as possible in the core metabolism of A. fumigatus . It also allowed us to acquire a more comprehensive catalog of optional accessory metabolic reactions by defining strain subset diversity, which enabled subsequent strain-specific gap-filling curation 15. The largest fractions of the derived biomass function included carbohydrates and proteins . Additional essential components included lipids, DNA, and energetic co-factors  or MetaCyc . The remaining 2370 metabolic reactions (excluding exchange reactions) could not be mapped to any gene in our pan-GEM draft model and were removed from the generic pan-GEM. These reactions were retained for subsequent strain-specific refinement steps, which require accessory information e.g., for gap-filling of fragmented metabolic pathways 18 based on relaxed flux bounds of exchange reactions.Subsequently, we screened available es Figs.  and 2003ays Fig. . Concurrays Fig. . For theays Fig. , MethodsA. fumigatus wild-type (Af293 strain) and four mutant strains affecting nitrogen or carbon metabolic components, and adapted the model to publicly available gene essentiality information19  from our previous study addressing the global diversity of A. fumigatus7, we mapped strain-specific gene profiles to the reference pan-GEM and subsequently derived strain-specific GEMs (Supplementary Data 22 and crosschecking against blocked reactions with FASTCC (Methods)23. Model size varied in the different A. fumigatus strains from 1321 to 1402 reactions (mean 1361).Using a genomic dataset of 252 A. fumigatus, we found a low number of core metabolic components shared by all GEMs  and 1108 metabolic reactions (76.9%) were shared by all strain-specific GEMs, resulting in a large degree of metabolic variation across all GEMs (296 accessory genes and 338 accessory reactions). Most relevant accessory content was involved in nucleotide, energy (including oxidative phosphorylation and nitrogen metabolism), and amino acid metabolic pathways p ≤ 0.05, Fig. A. fumigatus26. Combining the ability to convert chorismate and glutamine to anthranilate, pyruvate, and glutamate, with amino acid and energy metabolism-associated conversions of methionine, succinate, or tryptamine, and the ability to take up and grow on aspartic acid, appeared sufficient for strain origin classification. Specifically, the ability to add sulfur to methionine as well as the absence of the ability to convert selenocystathione to selenocysteine or tryptamine to Indole-3-acetaldehyde appeared a characteristic of environmental strains, which in part was not present in clinical strains . Using information from only 21 reactions, classifying environmental from clinical strains achieved an area under the curve for precision over recall of 0.92 and an accuracy of 0.79 . For all patients, we had an initial culture-negative sample and a subsequent sample that was positive for A. fumigatus growth. To investigate changes to the lung microbiota after A. fumigatus colonization, we performed shotgun metagenomic sequencing for all 80 sputum samples (A. fumigatus negative and positive), generating an average of 5.59 Gbp of sequencing data per sample (standard deviation 0.80 Gbp). By combining Kraken 2 and raspir30 for taxonomic profiling, we identified a total of 67 genera and 200 species. Although spontaneous expectoration of sputum is frequently used for sample acquisition32, this practice may introduce contamination from oropharyngeal flora. By applying FEAST33, which was developed to partition microbial samples into their source components facilitating the quantification of contamination or other potential source environments, we found a dominant source of our samples to be cystic fibrosis patients’ lung microbiome and a significantly higher contribution of them than the oral or clinical environment microbiome  showing an uneven distribution in the population (Supplementary Data Streptococcus mitis (65% occurrence in the 10 most abundant species), Haemophilus parainfluenzae (61.25%), Rothia mucilaginosa (57.5%), Prevotella melaninogenica (55%), and Staphylococcus aureus (52.5%) were most frequently found among the 10 most abundant species , we generated networks from differentially correlated microbial pairs in paired A. fumigatus-negative versus A. fumigatus-positive samples from cystic fibrosis patients  between A. fumigatus-negative and subsequently positive patient samples. For the top 5 differentially abundant species , existing edges . Interestingly, modules 3 and 4 were enriched in amino acid metabolism, for example, phenylalanine and tryptophan, but also valine and (iso-)leucine. Further enrichments included glycosaminoglycan and steroid biosynthesis (module 1), glycan biosynthesis (module 2), propanoate and fatty acid metabolism (module 3), and flavonoid metabolism  and positive correlations between FEV and a number of amino acid-involving pathways including biosynthesis of phenylalanine, tyrosine and tryptophan, and also with glycolysis  and L-Phenylalanine:2-oxoglutarate aminotransferase (EC2.6.1.1), which showed notably constrained flux-bound variability across most simulated strain GEMs  decreased metabolic activity significantly  and growth  on minimal media . Relative metabolic activity and radial growth increases based on minimal media were highest for glutamine  reconstructed a comprehensive pan-GEM of A. fumigatus metabolism in a data-driven manner, which we validated against phenotypic microarray and gene essentiality data; (ii) derived 252 strain-specific GEMs from individual genome assemblies and manually curated them towards growth feasibility and minimal fractioned network topologies; and (iii) determined metabolic differences differentiating clinical from environmental strains, such as metabolic reactions involving several amino acids, particularly aromatic amino acids as well as chorismate or thioredoxin. Chorismate is an important precursor for aromatic amino acids and is formed in the shikimate pathway. This seven-step pathway is not present in animals and enables the synthesis of the aromatic amino acids tyrosine, phenylalanine, and tryptophan. Thioredoxin is an important factor for DNA synthesis and is associated with A. fumigatus virulence42.In this study, we built a suite of Pseudomonas putida43, loss of fitness-relevant pathways for survival in the gastrointestinal environment in extraintestinal Salmonella spp.44, and strain-specific metabolic capabilities of Staphylococcus aureus linked to pathogenic traits and virulence acquisitions45. Here, we applied this strategy to explore metabolic diversity in a eukaryotic fungal pathogen. This strain-specific A. fumigatus GEMs platform is publicly available (BioModels ID MODEL2211100001) for investigating the metabolically relevant A. fumigatus gene set and its impact on the metabolic diversity influencing growth rate capabilities, metabolic adaptation, and pathogenicity in this important human fungal pathogen.Multistrain-GEMs have been used to elucidate the metabolic diversity of human-pathogenic bacteria. For example, they defined the pan-metabolic capabilities of A. fumigatus colonization. Clinical isolate-specific simulations and analysis showed significantly increased growth rates in the predicted lung microenvironment of cystic fibrosis patients after a confirmed A. fumigatus colonization, suggesting that the lung microbiome is remodeled to a state more favorable to fungal growth. Our analysis predicted 77 metabolic reactions associated with aromatic amino acid metabolism and the shikimate pathway would have significantly different flux ranges after A. fumigatus colonization of the lung. These reactions appear not only in aromatic amino acid metabolism but also in sulfur, nitrogen, and lipid metabolic pathways, highlighting the advantage of including topological pathway information when analyzing metabolic activity. Several of these reactions are shown to be essential for the growth of the fungus in knockout experiments, including knockouts for aroC, TrpC, and MET16, making these genes potential targets against the fungus25. However, we identified a challenge in drug design using potentially targetable biomarkers originating from lethality data of in vitro gene deletions in A. fumigatus. Our analyses using metabolic modeling coupled with growth experiments suggest that A. fumigatus might be able to grow without the function of targeted genes by obtaining the necessary metabolites externally. This might occur in patients, for example, with A. fumigatus acquiring metabolites generated by lung bacteria  used to build the strain-specific GEMs and the clinical strains in the cystic fibrosis patients in our study. In our previous study, however, we showed that genomic similarities of clinical strains are relatively high even when the strains originate from different countriesA. fumigatus and its metabolic interdependencies with bacterial communities in the human lung.Altogether, the presented analyses demonstrate that fungal, genome-scale metabolic modeling is feasible at the strain level. The results contribute towards a mechanistic understanding of the impact of genome diversity on the metabolic phenotype of A. fumigatus biomass composition based on several sources in the literature. First, we assigned proportions of main biomass components, as described,49 as 38.8% carbohydrates, 9.9% lipids, 30% proteins, 0.6% DNA, and 3.7% RNA. Since this resource neglected polyols, we added 4% polyols as reported for Aspergillus oryzae50 for a total of 42.8% carbohydrates. After adding 6.6% cofactors, these main components made up the total biomass composition together with the reported 6.4% ash fraction49. Next, we screened the literature to specify fractions of carbohydrate subcategories, e.g., glucans or trehalose53; lipids including sterols; phospholipids; neutral lipid and free fatty acid compositions55; amino acid composition of the protein content57; and co-factor content including energy carriers such as NADH and vitamins such as riboflavin59. After calculating the mmol/g content of each fraction, we added ATP demand according to models developed for Saccharomyces cerevisae and Aspergillus niger61. Furthermore, we added a nongrowth-associated ATP maintenance reaction and calibrated flux through it for reported growth rates of the fungus in batch-fermentation process in hypoxic and normoxic glucose-limited conditions .All reconstruction and analysis efforts used COBRApy (v0.17.1)13. We downloaded the Aspergillus CoReCo model for A. fumigatus (Biomodels ID MODEL1604280029) and further Aspergillus models from the CoReCo repository including A. oryzae (Biomodels ID MODEL1604280012), Aspergillus nidulans (Biomodels ID MODEL1604280008), A. niger (Biomodels ID MODEL1604280021), Aspergillus clavatus (Biomodels ID MODEL1604280016), Aspergillus terreus (Biomodels ID MODEL1604280019), and Aspergillus gossypii (Biomodels ID MODEL1604280044) from the BioModels repository (https://www.ebi.ac.uk/biomodels/). In addition, we incorporated metabolite and reaction information from a recently published A. fumigatus central metabolism model14. This yielded a base model of 7,606 reactions  and 3,578 metabolites. Of note, the high number of initial exchange reactions originated from models created with the CoReCo pipeline, which includes an exchange reaction for each defined metabolite by default. All subsequent curation efforts aimed to keep only reactions for which annotation information was available or that were necessary for model feasibility. Filtering duplicate reactions and metabolites reduced the model by 73 reactions and 210 metabolites. Filtering duplicate reactions was necessary because our initial merge of metabolic information included reactions and converted metabolites from different sources. Only when reactions or metabolites had different naming conventions we reduced to a single naming scheme. When different isozymes encoded the same metabolic reaction, we included all alternative genes as OR relationships (instead of AND) in the genes-to-protein rule of the affected metabolic reaction and removed the duplicate reaction. The biomass formation was modified based on the literature on A. fumigatus metabolism and enriched with information from closely related species if we did not find A. fumigatus-specific information (Biomass formulation section).We gathered and combined information from automatically generated draft reconstructions based on the CoRoCo pipelinehttps://www.kegg.jp/) and MetaCyc (https://metacyc.org/) databases for gene annotation for A. fumigatus metabolism and added 1444 genes to the model. When available, we adopted AND and OR relationships for genes for metabolic reactions and crosschecked with genes encoding reactions in the yeast consensus model61. During this step, 2370 reactions could not be mapped to any annotated gene and were removed from the template model but kept for subsequent gap-filling procedures. Further curation efforts were run in parallel, since modifications influenced other curation procedures. Curation included compartmentalization, resolving erroneous energy generating cycles (EGCs)17 and gene essentiality information19, and adaptation to phenotypic growth assays . We considered 0.001 mmol/grDW/h as an absolute minimum for growth rate to account for reported low-oxygen growth conditions, e.g., in glucose-limited media21. We identified essential carbon, nitrogen, sulfur, and phosphorus components  for subsequent GEM analysis . Simulation scripts for all analyses are at Github: https://github.com/mohammadmirhakkak/A_fumigatus_GEM/, which also holds the MEMOTE report (Github folder: https://mohammadmirhakkak.github.io/A_fumigatus_GEM/memote_report.html). Simulation-specific constraints are in Supplementary Data Last, we checked the compatibility of our model with metabolic modeling standards by running MEMOTE testsA. fumigatus was derived for 300 environmental and clinical strains with a global distribution7. By mapping the genomes for 252 strains to the Af293 A. fumigatus reference genome annotation, we identified metabolically relevant genes by requiring at least 95% sequence identity under the rationale that high sequence identity preserves metabolic function. Small deviations from the chosen sequence identity threshold did not change the results. The similarity analysis was done by BLAST analysis of protein sequences using diamond (v0.9.24.125)68. The presence or absence of metabolic reactions was deduced for each strain using the associated gene-protein-reaction rules from the pan-GEM and the relevant identified genes. The metabolic core comprised reactions and genes that occurred in all strains with other reactions and genes defining, respectively, the accessory reactome and genome. To ensure that all strain-specific GEMs showed nonzero growth capability on minimal media with glucose as the carbon source, we identified and resolved minimal sets of essential reactions required to operate when adapted to the minimal cut set concept22. Finally, we guaranteed a consistent network property by identifying and discarding blocked reactions for each isolate using FASTCC23. All models were deposited in BioModels24 and assigned the identifier MODEL2211100001.Recently, the pan-genome of pyrG-, ΔlysF, Δmet2, and ΔniaD were grown at 25 °C for 7 days before experimental assays on malt agar supplemented with 5 mM uracil. Uracil supplementation was required for growth of the CEA17 pyrG- strain (a uridine auxotroph) and was added to all media to ensure comparable growth. Mature conidia were harvested by rubbing plates with sterile distilled water and filtering the resulting solution through a 30-µm cell strainer to remove mycelial fragments. Spore purity was assessed and confirmed by microscopy. Spore solutions were adjusted to a transmittance of 75%. Phenotypic microarrays were performed using Biolog Phenotypic Microarray plates PM1, PM2, PM3, and PM4  prepared following the manufacturer’s protocol for filamentous fungi, including resuspending conidia in filamentous fungi (FF) media and the addition of 0.16 ml of Biolog Redox Dye D to the master mix of each plate to quantify fungal metabolic activity. By using PM1-PM4 we could investigate 379 different growth conditions rather than only 95 as available with Biolog’s FF plate (Fungi Identification Test Panel). The plates were incubated at 37 °C for 3 days and metabolic activity was measured colorimetrically using an OmniLog microplate reader with readings taken every 15 min. The incubation temperature was changed from the initial 25 °C to 37 °C for the phenotypic microarray experiments to mimic the change in temperature that occurs in the transition from the environment to the human host as used before71. Experiments were performed as biological duplicates or triplicates , CEA17 72. The diagnosis of cystic fibrosis was verified by established diagnostic criteria74. Spontaneously expectorated sputum was collected during visits to the Cystic Fibrosis Center at the University Hospital Heidelberg and frozen in liquid nitrogen on the day of visit. Sputum samples were collected from 40 cystic fibrosis patients before and after they had positive A. fumigatus colonization. Samples were frozen within 24 h after reception at the microbiology department. The cohort for 80 total samples was 15 females and 25 males aged 23.6 ± 4.96 years (mean ± standard deviation) before A. fumigatus colonization. Samples were taken during visits from patients without exacerbation or intravenous antibiotic treatment in the previous 3 months. Supplementary Data Burkholderia cepacia special agar  , and Sabouraud agar  . Two other media were used for anaerobic isolation (36 °C): Schaedler agar  and kanamycin-vancomycin agar . Colonization was defined as positive culture from a specimen from the sputum samples on at least one of the agar plates.This study was approved by the Ethics Committee of the University of Heidelberg and written informed consent was obtained from all patients or their parents or legal guardians (S-370/2011). Patients were treated according to the standard of careg (8000 rpm) for 1 min. To QIAamp Mini spin columns 500 μl Buffer AW2 was added before centrifuging at full speed  for 3 min. To the QIAamp Minispin columns, 200 μl Buffer AE or distilled water was added before incubating at room temperature for 1 min and centrifuging at 6000g (8000 rpm) for 1 min. Genomic DNA was randomly fragmented by sonication.DNA extraction procedures followed the QIAamp DNA Mini and Blood Mini Handbook. The QIAampl DNA minikit was used for DNA extraction. Sputum samples were placed in a 1.5 ml microcentrifuge tube and 180 μl Buffer ATL was added. Proteinase K (20 μl) was added, and samples were mixed by vortexing and incubated at 56 °C. After centrifuging, 4 μl RNase A (100 mg/ml) was added, followed by mixing by pulse-vortexing for 15 s, and incubating 2 min at room temperature (15–25 °C). After centrifuging to remove drops from inside the lid, 200 μl Buffer ATL was added. Samples were mixed by pulse-vortexing for 15 s and incubated at 70 °C for 10 min. After briefly centrifuging, 200 μl Buffer AL was added to samples, followed by mixing by pulse-vortexing for 15 s, and incubating at 70 °C for 10 min. After brief centrifugation, 200 μl ethanol (96–100%) was added and samples were mixed by pulse-vortexing for 15 s. After brief centrifugation, QIAamp Mini spin columns and 500 μl Buffer AW1 were added before centrifuging at 6000DNA fragments were end-polished, A-tailed, and ligated with full-length adapters for Illumina sequencing, followed by PCR amplification with P5 and indexed P7 oligos. PCR products for final construction of libraries were purified with the AMPure XP system . Libraries were checked for size distribution with an Agilent 2100 Bioanalyzer , and quantified by real-time PCR to ensure an amount of at least 3 nM). Qualified libraries were inserted into Illumina sequencers (MiSeq system). For quality checking, 1 ethidium bromide negative control was added for every 11 samples and treated with the same handling procedure as the experimental samples.30 was used to support our Kraken 2 results. Only the species that can be detected in at least one sample by raspir were kept 35 were also used to assess the reliability and for comparison of Kraken 2 results. FEAST 33 was used to check for possible contamination and to estimate the contribution of potential source environments using the cystic fibrosis lung microbiome dataset from three published studies 77, an oral microbiome dataset (ENA project ID PRJEB28422)78 and a clinical environmental dataset (ENA project ID PRJNA376580)79. Taxonomic assignment of the datasets followed the same pipeline used for our dataset. FEAST results  pipeline was used to assign nonhuman reads to KEGG pathways. R packages vegan (v2.5) and picante (v1.8.2) were used to calculate alpha diversity with a Shannon and phylogenetic diversity index for each sample using the read counts of species. Statistical differences between samples before (A. fumigatus -) and after (A. fumigatus +) colonization with A. fumigatus were obtained by Wilcoxon signed-rank test. For beta diversity, R package coda.base (v0.3.1) was used to calculate the pairwise Aitchison distance for samples using the relative abundance of species. Statistical differences between samples before and after colonization with A. fumigatus were calculated by PERMANOVA.Trimmomatic was used to clip adapter and low-quality bases . Remaining reads shorter than 30 base pairs were discarded. BWA (v07.17) was used to align quality-filtered reads to the human reference genome (hg38) for the removal of human-derived reads. From 1.9e + 07 ± 2.7e + 06 (mean ± standard deviation) metagenomic reads, 7.8e + 05 ± 8.9e + 05 remained after preprocessing samples. To estimate the taxonomic composition of the nonhuman reads, Kraken 2  was used with its standard database as the reference. To further control false positive rate, raspir (v1.0.2)A. fumigatus colonization . Subsequently, MEGENA (v1.3.7) was used to identify co-expressed modules  in the constructed network using differentially correlated microbial pairs. To identify molecular functions, we investigated the enrichment of KEGG pathway information (https://www.genome.jp/kegg/pathway.html) by permutation testing to determine if correlations between modules and KEGG orthologies were possible by chance81. First, for a given module, all correlation coefficients and p-values for a KEGG orthology and all species in the module were obtained using the Spearman correlation method. The sum of absolute correlation coefficients in the module was then calculated. Following that, a random set of species of the same size as any given module was chosen 1000 times from all species, calculating the sum of absolute correlation coefficients each time. Finally, the sum of correlation values for each module was evaluated. If higher than 95% of the sums of correlation values in the 1000 repeats of randomly selected species, we inferred a significant correlation between modules and KEGG orthologies.The co-abundance network was constructed based on the relative abundance values of species . DGCA (v2.0.0) was used to construct the network from differentially correlated microbial pairs in paired cystic fibrosis samples, comparing before to after 40. In brief, MAMBO optimizes a highly correlated metabolic profile with a given relative abundance profile using growth rate simulations of bacterial GEMs associated with a particular metagenomic sample. For 200 raspir-confirmed bacterial species, we downloaded 143 matching bacterial GEMs from the AGORA2 (https://vmh.life/files/reconstructions/AGORA2/version2.01/)82 and CarveMe collection (https://github.com/cdanielmachado/embl_gems/tree/master/models)83. Optimizations were run in a python environment (v3.7) using a high-performance cluster . Finally, 929 metabolites were obtained using MAMBO , we randomly sampled 50% of the majority group and oversampled the minority group using ADASYN implemented in R package imbalance (v1.0.2.1). Subsequent feature selection used Boruta (v7.0.0), VSURF (v1.1.0), MUVR (v0.0.973), and sPLS-DA . These steps were repeated 50 times and selected features and their selection frequency were recorded. Finally, the Extra Trees algorithm from PyCaret (v2.3.2) was run for the feature sets, scanning frequency cutoffs to optimize the cutoff value for ML performance. The best hyperparameters of the Extra Trees model were automatically selected by scikit-optimize .A. fumigatus was cultivated on Aspergillus minimal medium (AMM)84 agar plates for 5 days at 37 °C. Conidia were harvested with sterile phosphate buffered saline containing 0.01 % (v/v) Tween 80 and filtered through a 40-µm pore-size cell strainer . Conidia numbers were determined using a CASY cell counter . Spore purity was assessed and confirmed by microscopy.For conidia production, 85. In brief, in a 96-well cell culture microplate  190 µl AMM/resazurin containing, when indicated, 5 mM glutamine, glycine, phenylalanine or tryptophan, was inoculated with 10 µl of conidial suspension at 12 different concentrations, for 1.6 × 106 to 0.78 × 103 conidia per well. After 18 h, resazurin reduction was measured as fluorescence using a microplate reader (Tecan Infinite M200 Pro) at wavelengths 570 nm (excitation) and 615 nm (emission). Relative metabolic activity was determined as the number of conidia needed as inoculum to reach a fluorescence of 5000 at an optimal gain.Metabolic activity was determined based on a resazurin assay as described6 conidia ml−1) point-inoculated on AMM or cystic fibrosis media (SCFM2)86 agar plates supplemented with 5 mM glutamine, glycine, phenylalanine or tryptophan, when indicated. Radial colony growth of A. fumigatus cultures at 37 °C was determined after 24 h using a caliper.Radial growth was determined with 5 µl conidial suspension  coupled with a quadropole detector  controlled by ChemStation (Agilent). Raw data were converted to netCDF format using Chemstation (Agilent), before data were imported and processed in Matlab R2018b  using PARADISe software88.Sample analysis was performed by MS-Omics  as follows. Samples were derivatized with methylchloroformate using a slightly modified version of the Smart et al. protocolt test using a p-value cutoff of 0.05. Multiple test correction as described by Benjamini–Hochberg was applied when appropriate. Gaussian mixture model was used to identify significantly abundant lung species. Functional enrichment of KEGG terms was done using overrepresentation analysis. Spearman correlation was applied to report correlation of KEGG modules with forced expiratory volume of cystic fibrosis patients (uncorrected p ≤ 0.05). All codes and data to reproduce the presented results are described in the code and data availability section of this manuscript and are stored in zenodo, github, BioModels and ENA repositories.All statistical test procedures are described in the respective methods sections. No statistical method was used to predetermine the sample size. No data were excluded from the analyses. Testing group differences included two-tailed Wilcoxon or Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary Data FilesSupplementary Data S1Supplementary Data S2Supplementary Data S3Supplementary Data S4Supplementary Data S5Supplementary Data S6Supplementary Data S7Supplementary Data S8Supplementary Data S9Supplementary Data S10Reporting Summary


This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.To close the gap between the rates of computational screening and experimental realization of novel materials An autonomous laboratory, the A-Lab, is presented that combines computations, literature data, machine learning and active learning, which discovered and synthesized 41 novel compounds from a set of 58 targets after 17 days of operation. In contrast to conventional ML algorithms used for optimization, human researchers benefit from a wealth of background knowledge that informs their decision-making, and it is increasingly recognized9 that autonomy will require a fusion of encoded domain knowledge, access to diverse data sources and active learning.Although promising new materials can be identified at scale using high-throughput computations, their experimental realization is often challenging and time-consuming. Accelerating the experimental segment of materials discovery requires not only automation but autonomy—the ability of an experimental agent to interpret data and make decisions based on it. Pioneering efforts have demonstrated autonomy in several aspects of materials research, including robotic and Bayesian-driven optimization of carbon nanotube yield13, the A-Lab addresses the unique challenges of handling and characterizing solid inorganic powders. These often require milling to ensure good reactivity between precursors, which can have a wide range of physical properties related to differences in their density, flow behaviour, particle size, hardness and compressibility. The use of solid powders is well suited for manufacturing and technological scaleup, and the approach of the A-Lab to synthesis produces multigram sample quantities that facilitate device-level testing.Here we present the A-Lab, an autonomous laboratory that integrates robotics with the use of ab initio databases, ML-driven data interpretation, synthesis heuristics learned from text-mined literature data and active learning to optimize the synthesis of novel inorganic materials in powder form. Although autonomous workflows based on liquid handling have been demonstrated in organic chemistry14, the A-Lab generates synthesis recipes using ML models trained on historical data from the literature and then performs these recipes with robotics. The synthesis products are characterized by X-ray diffraction (XRD), with two ML models working together to analyse their patterns. When synthesis recipes fail to produce a high target yield, active learning closes the loop by proposing improved follow-up recipes. Over 17 days of operation, the A-Lab successfully synthesized 41 of 58 target materials that span 33 elements and 41 structural prototypes  screened using the Materials Project14 and cross-referenced with an analogous database from Google DeepMind. Because the A-Lab handles samples in open air, we only considered targets that are predicted not to react with O2, CO2 and H2O , an active-learning algorithm that integrates ab initio computed reaction energies with observed synthesis outcomes to predict solid-state reaction pathways17. Experiments are performed under the guidance of this algorithm until the target is obtained as the majority phase or all synthesis recipes available to the A-Lab are exhausted.For each compound proposed to the A-Lab, up to five initial synthesis recipes are generated by a ML model that has learned to assess target ‘similarity’ through natural-language processing of a large database of syntheses extracted from the literaturerature16 . If thesThe A-Lab carries out experiments using three integrated stations for sample preparation, heating and characterization, with robotic arms transferring samples and labware between them Fig.  and 2. T19. Because the target materials considered in this work have no experimental reports, their diffraction patterns are simulated from computed structures available in the Materials Project and corrected to reduce density functional theory (DFT) errors  following the methodology outlined in previous work20  intermediate phases that leave only a small driving force to form the target material should be avoided, as they often require long reaction time and high temperature29.The active-learning cycle of the A-Lab2P2O9 was optimized by avoiding the formation of FePO4 and Ca3(PO4)2, which have a small driving force (8 meV per atom) to form the target. This led to the identification of an alternative synthesis route that forms CaFe3P3O13 as an intermediate, from which there remains a much larger driving force (77 meV per atom) to react with CaO and form CaFe2P2O9, causing an approximately 70% increase in the yield of the target 2HPO4, which proceed through a series of decomposition reactions and ultimately evaporate above 450 °C (ref. 30). Still, recipes based on these precursors can succeed if the ammonium phosphate reacts with another precursor before its evaporation temperature, effectively locking the phosphate ions in the solid state. For example, volatility does not seem to be an issue for the Mn-containing phosphates targeted in this work, as each Mn oxides precursor reacts with the ammonium phosphates at low temperature (<500 °C) to form Mn2(PO4)3 as an intermediate. This precursor behaviour can, in principle, be learned when sufficient pairwise reaction data have been collected, after which the A-Lab may favour the selection of precursors that trap in phosphate ions at low temperature and therefore preclude unwanted volatility.Precursor volatility disrupted all synthesis experiments targeting CaCr3)5, whose synthesis attempts produced amorphous samples 5 with energies as low as 61 meV per atom above the crystalline ground state, a finding that is consistent with the widely reported glass-forming ability of phosphate-rich compounds34.Melting of samples at high temperature inhibited the crystallization of one target, Mo, even though it is widely reported in the literature to be experimentally accessible35. If the energy of LaMnO3 were lowered, consistent with its experimental stability, La5Mn5O16 would be destabilized (above the hull). Errors in the computed energy of LaMnO3 may arise from its strong Jahn–Teller activity36, compositional off-stoichiometry37 or the presence of f-states in La—all of which present challenges to conventional DFT. Problems with YbMoO4 were found to be because of a poor pseudopotential choice in the Materials Project that destabilizes the well-known oxide, Yb2O3, and it is likely that, in more accurate calculations, YbMoO4 is not stable. A similar lanthanide-related electronic-structure problem may also be responsible for the failure to synthesize BaGdCrFeO6. These examples demonstrate the ability of the A-Lab to provide important feedback to high-throughput computed datasets. With improved calculations that exclude the computationally problematic compounds in this work, our total success rate would increase to 78% (43/55 targets).Some failure modes result from inaccuracies in the computed stability of the target and therefore cannot be addressed by modifications to the experimental procedures. Fundamental-electronic-structure challenges are probably affecting LaIn 17 days of closed-loop operation, the A-Lab performed 355 experiments and successfully realized 41 of 58 novel inorganic crystalline solids with diverse structures and chemistries. This unexpectedly high success rate (71%) for the synthesis of computationally predicted compounds was achieved by integrating robotics with: (1) DFT-computed data to survey the energetic landscape of precursors, reaction intermediates and final products; (2) heuristic suggestions for synthesis procedures obtained from ML models trained on text-mined synthesis data; (3) ML interpretation of experimental data; and (4) an active-learning algorithm that improves on failed synthesis procedures. The study also revealed several opportunities to enhance the lab’s active-learning algorithm by addressing failures caused by slow reaction kinetics, which would enable an improved success rate of 74% with in-line solutions.Our paper demonstrates that autonomous research agents can markedly accelerate the pace of materials research. Researchers initialized the A-Lab by proposing 58 target materials, which were successfully realized at a rate of >2 new materials per day with minimal human intervention. Such rapid discovery points to a vast landscape of opportunities in materials synthesis and development. Although this work focused on a limited subset of all possible synthesis targets, many new candidates await evaluation. As the breadth of ab initio computations continues to grow, so will this list of novel materials.Advances in simulations, ML and robotics have intersected to enable ‘expert systems’ that show autonomy as an emergent quality by the sum of its automated components. The A-Lab demonstrates this by combining modern theory-driven and data-driven ML techniques with a modular workflow that can discover novel materials with minimal human input. Lessons learned from continuing experiments can inform both the system itself and the greater community through systematic data generation and collection. The systematic nature of the A-Lab provides a unique opportunity to answer fundamental questions about the factors that govern the synthesizability of novel materials, serving as an experimental oracle to validate predictions made on the basis of data-rich resources such as the Materials Project. In future iterations of the platform, such an oracle may be expanded to investigate factors beyond synthesizability, including microstructure and device performance. Although our current success rate for the synthesis of novel compounds is high, the remaining discrepancies between current predictions and their experimental outcomes is a crucial signal required to improve our understanding of materials synthesis.4+), as determined by pymatgen38.The 58 targets evaluated by the A-Lab were identified from the Materials Project database (version 2022.10.28). We first obtained all entries from the Materials Project that were marked as ‘theoretical’  and predicted to be thermodynamically stable (at 0 K) or very close to the convex hull (<10 meV per atom). We did not consider materials with ≤2 elements nor those containing elements that are radioactive , exceedingly rare  or toxic . Owing to concerns with the experimental handling of certain materials systems , we constrained our selection to only include the following types of material: oxides, carbonates, bicarbonates, hydroxides, sulfates, sulfites, bisulfates, silicates, fluorides, chlorides, bromides, orthoborates, metaborates, tetraborates, phosphates, phosphites, chlorates, chlorites and hypochlorites. Finally, we removed all compounds predicted to have uncommon and potentially challenging oxidation states  and temperatures ranging from 600 to 1,100 °C. We further checked for reactivity with CO2 and H2O under those same conditions by using the Interface Reactions module in pymatgen41. From the resulting list of 146 new compounds that were stable in air, we selected 58 targets for which precursors were readily available. Later in the process, we found literature evidence for a small number of these compounds, but most (52/58 compounds) are believed to have no previous reports . It operates autonomously once given the following information: which elements to consider in the target materials, how large an upper limit to impose on each material’s energy above the convex hull, the atmospheric conditions under which the materials will be synthesized and a threshold on the reaction energies that exist between each material and the gaseous species present in the specified atmosphere. The algorithm then scrapes the Materials Project and produces a list of candidate materials that satisfy these criteria. Further filtering may be considered on the basis of the availability and cost of precursors for each target. Although this is done manually in the current version of the algorithm, potential improvements could automate the process by using online data from chemical inventory lists and vendor websites.The algorithm we used for identifying potential synthesis targets is available on GitHub  target and each known material in the knowledge base is evaluated using the cosine similarity between their encoded vectors. After identifying the reference material that is most similar to the target, its precursors are included in the new recommendation. When these precursors do not cover all the elements in the target, we use a masked precursor completion model12 to account for such missing precursors. Subsequent recommendations are implemented by moving down the list of known materials ranked to be most similar to the target.We have established a pipeline for recommending synthesis recipes by using a knowledge base of 33,343 solid-state synthesis procedures extracted from 24,304 publications11. The target and its associated precursors are transformed into three sets of features: (1) precursor properties including melting points, standard enthalpies of formation and standard Gibbs free energies of formation; (2) target compositional features indicating which elements are present; and (3) the calculated thermodynamic driving force associated with pairwise reaction paths from precursors to target. Although the proposed synthesis temperature is dependent on the precursors, not just the target, it may vary for each recipe. However, to maximize the efficiency with which the A-Lab operates, we chose to use one fixed temperature for each target. This temperature was calculated by averaging the proposed synthesis temperatures for the top five precursor sets recommended for a given target. This allowed all such precursor sets to be batched in a single furnace.For each set of recommended precursors, the most effective synthesis temperature is predicted using an XGBoost regressor trained in previous workThe A-Lab performs fully automated solid-state synthesis and characterization. It is a bespoke robotic platform that consists of a precursor preparation station with a central robot arm (Mitsubishi) for powder dispensing and mixing (custom-made with Labman Automation Ltd.), a high-temperature heating station with four box furnaces , a product-handling station developed in-house for powder retrieval and sample loading, a characterization station with a powder X-ray diffractometer  and two collaborative robot arms  that transfer samples and labware between stations. Further details on the robotic platform are provided in Supplementary Note 2 balls and crucibles) and precursor dosing bottles containing between 50 and 100 g of powders are manually loaded before starting a new experimental campaign. Prescribed amounts of the precursor powders are dispensed into a plastic vial by an automatic dispenser balance . The precursor powders are then mixed thoroughly with ethanol and ten 5-mm ZrO2 balls in a dual asymmetric centrifuge  for 9 min. To ensure proper slurry viscosity, the ethanol amount is automatically calculated on the basis of the amount and density of each powder comprising the mixture. The resulting slurry is transferred with an automated pipettor  into an alumina crucible, which is then dried at 80 °C in a closed evaporation system. A UR5e robot arm on a linear rail (Olympus Controls) removes the dried samples from the precursor preparation station and loads them into one of four box furnaces. Heating is performed in batches, with each furnace containing up to eight samples on an alumina tray. Each batch is heated to 300 °C with a slow ramping rate of 2 °C min−1 to raise the likelihood that any phosphate precursor has time to react before it becomes volatile at higher temperature. The samples are then heated to the specified synthesis temperature with a nominal ramp rate of 15 °C min−1, followed by a 4-h dwell. After the dwell is complete, the samples are naturally cooled to 100 °C, at which point a UR5e arm removes the samples from the furnace and waits another 10 min to allow the samples to cool to room temperature.The synthesis process starts from the precursor preparation station, where the necessary consumables  consisting of six convolutional layers, with max pooling applied between each, followed by three fully connected layers with ReLU activation. Batch normalization and a dropout rate of 50% is applied between the fully connected layers for regularization. At inference, we apply Monte Carlo dropout to create an ensemble of 100 networks with 50% of their connections randomly excluded. The final prediction is taken as the phase that seems most frequently in the ensemble and its associated confidence is defined as the fraction of models that predict it.Given an XRD pattern obtained from an unknown sample, we apply XRD-AutoAnalyzer to identify the constituent phases and estimate their weight fractionsA unique model instance is trained on the chemical space defined by each target. Experimental-structure entries with elements shared by the given target are extracted from the ICSD, also including carbonates and hydroxides. For the DFT-calculated target, we apply a machine-learned volume correction to its lattice parameters  were trained using reinforcement learning based on a proximal policy optimization algorithm18. Such cases were still considered successful as long as the disordered version of the target retained the same crystal structure and overall composition as the ordered version.When the automated refinement gives a poor fit, manual analysis is performed. For targets for which we suspect the poor fit resulted from configurational disorder, we refined the XRD patterns using cation-disordered versions of the target’s structure taken from the Materials Project. The cations allowed to be exchanged (disordered) with one another were selected on the basis of the Hume-Rothery rules, as detailed in previous work3, our recently developed algorithm that learns from previous experimental outcomes to identify improved reaction pathways. Given the products obtained from a set of precursors proposed by our natural-language models at temperature TNLP, ARROWS3 first suggests that a lower temperature (TNLP − 300 °C) be tested for the same precursor set. The intent of this approach is to reveal which intermediate phases lead to the formation of each impurity observed at higher temperature. From the low-temperature-synthesis outcome, information is extracted about the pairwise reactions that occurred, including those between the precursors (to form the observed intermediates), as well as those between the intermediates (to form the high-temperature impurities). New synthesis experiments are then proposed on the basis of sets of precursors expected to avoid such reactions, giving priority to those with a maximal thermodynamic driving force to form the target. The driving force is calculated as the free-energy difference between a target and its associated precursors, in which all solid energies (at 0 K) are extracted from the Materials Project and corrected using a machine-learned descriptor that accounts for vibrational-entropy contributions at the specified temperature46.Active learning is performed using ARROWSTNLP − 300 °C), iteratively higher temperatures (ΔT = 100 °C) are examined until the target is obtained with a yield exceeding 50% or until the temperature reaches TNLP. At each step, the algorithm determines which pairwise reactions occurred and records them in a database that is referred to throughout all other experiments performed by the A-Lab. In subsequent iterations, ARROWS3 gives priority to sets of precursors containing pairs of phases that are expected to form the desired target, while avoiding pairs that form unwanted impurities. Moreover, to avoid testing redundant synthesis routes for which different precursors form identical products, the algorithm checks whether the low-temperature (TNLP − 300 °C) intermediates obtained from a given precursor set differ from those obtained with previous (unsuccessful) recipes. If not, then no further experiments are proposed for that set of precursors. This process is repeated until the target is successfully obtained or until all the available precursor sets have been exhausted. Further details on the active-learning process are provided in Supplementary Notes After testing a precursor set at low temperature  handling powders and slurries in the sample preparation station used to dispense and mix precursors before heating. The video is played at 20× speed.Supplementary Video 2Robot arm R2 (UR5e) moving crucibles from the sample preparation station to the box furnaces. The video is played at 20× speed.Supplementary Video 3Robot arm R3 (UR5e) retrieving powder samples  and cooperating with an Aeris X-ray diffractometer for their characterization. The video is played at 12× speed.


E. coli and S. cerevisiae to test the method benchmarking the capability of these industrial microbes for overproduction of acetate and glycerol under aerobic conditions and succinate and ethanol under anaerobic conditions. The results illustrate that OptEnvelope is capable to find multiple strong coupled envelopes located in the desired solution space because of its novel target point oriented strategy of envelope search. The results indicate that E. coli is more appropriate to produce acetate and succinate while S. cerevisiae is a better host for glycerol production. Gene deletions for some of the proposed reaction knockouts have been previously reported to increase the production of these metabolites in experiments. Both organisms are suitable for ethanol production, however, more knockouts for the adaptation of E. coli are required. OptEnvelope is available at https://github.com/lv-csbg/optEnvelope.Finding the best knockout strategy for coupling biomass growth and production of a target metabolite using a mathematic model of metabolism is a challenge in biotechnology. In this research, a three-step method named OptEnvelope is presented based on finding minimal set of active reactions for a target point in the feasible solution space (envelope) using a mixed-integer linear programming formula. The method initially finds the reduced desirable solution space envelope in the product versus biomass plot by removing all inactive reactions. Then, with reinsertion of the deleted reactions, OptEnvelope attempts to reduce the number of knockouts so that the desirable production envelope is preserved. Additionally, OptEnvelope searches for envelopes with higher minimum production rates or fewer knockouts by evaluating different target points within the desired solution space. It is possible to limit the maximal number of knockouts. The method was implemented on metabolic models of  The organism strains used in biotechnology define the opportunities and limitations of biotechnological production, therefore, their selection and modification are important ways to improve the production features  and sustSeveral methods have been published for the prediction of necessary reaction deletions and/or insertions for growth-coupled strain design. The developers choose different platforms to implement methods mostly in Matlab  required to keep the metabolism active in the target point are determined and the other reactions (non-MAR reactions) are removed. The non-MAR reaction deletions lead to a new growth-coupled production envelope. While the strategy of the other methods is to find the best knockouts, OptEnvelope finds MAR for a target point and then, by removing non-MAR reactions, a new production, MAR variability determined envelope is obtained. Then, maximizing reinsertion of the removed nonMAR reactions, it is attempted to reduce the required number of knockouts attempting to keep the previously achieved MAR- determined production envelope. The word “envelope” was used in the name of this method because of its different approach compared to the previous coupling methods: OptEnvelope finds the engineered strain production envelope before finding the required knockouts.OptEnvelope searches to find various envelopes by implementing the method for target points around the original target point selected by the user. OptEnvelope can find multiple envelopes in one run by changing the target point in the desired space, thus possibly discovering various valuable envelopes. The user can compare and select the best strain designs characterized by envelopes based on several factors like fewer knockouts, higher minimum and maximum production rates, etc. to choose the optimum envelope for coupling. The strategy of defining a target point combined with the low CPU time of OptEnvelope makes it easy to switch the target point to inside feasible space and find other favourable envelopes. OptEnvelope is also a good tool for fast benchmarking of different organisms for production of particular metabolites due to its fast operation. The appropriateness of an organism to produce a particular metabolite is determined by the shape of the envelope and the number of necessary deletions.E. coli and S. cerevisiae, respectively. The models were used in the irreversible form (each reversible reaction was separated into two separate forward and backward reactions) to simplify the solution. The growth on glucose minimal media was simulated by considering the upper bounds of 10 mmol/gDCW/h for glucose uptake rate and 1000 mmol/gDCW/h for NH4, H2O, SO4, O2, H+, phosphate, and inorganic ion consumption reactions under aerobic conditions. The same glucose uptake rate used for the two models makes it possible to compare the capabilities of structures of the metabolic network. Oxygen uptake was bound to zero in iJR904 for the anaerobic growth simulation of E. coli. For S. cerevisiae, cytochrome c oxidase was removed from iMM904 to implement anaerobic conditions as very small amounts of oxygen are essential for biomass [In this study, the genome-scale metabolic models iJR904  and iMM9 biomass . The upt biomass  and Gurohttps://github.com/lv-csbg/optEnvelope) includes three steps: 1) selecting a target point (most desirable operation point) in the feasible region of a wild-type product-biomass envelope by the user, 2) determining the minimal active reactions (MAR) at the selected point and removing the other reactions to find a desired envelope for coupling, and 3) reinsertion of the non-MAR reactions removed at step 2 to find minimal knockouts required to support the desired envelope.OptEnvelope  was applied according to the LP problem presented in The maximum and minimum growth rates on the glucose minimal medium were initially calculated considering biomass reaction as the objective function. Then, the space between the minimum and maximum growth values was divided into 100 points including the endpoints. The objective function was changed to the exchange reaction of the desired product and the minimum and maximum production rates at each point of the growth rate were calculated. Production rate versus growth rate was plotted and the feasible region was specified. A typical envelope of product excretion rate versus growth rate for a wild-type strain and growth-coupled strains is presented in The plots in j); the variable zj is a binary variable that can only take the values of zero and one; and vj,max is the upper bound of reaction j. j according to For finding MAR of each target point, it has to be mentioned that the reactions can be categorized by flux variability range into four groups at each feasible point in j = 0. So, to avoid the simultaneous use of forward and backward directions in reversible reactions, j,f and zj,b are binary variables for forward and backward fluxes of reversible reaction j, respectively. Thus, if one direction of the reaction has a non-zero flux, As the metabolic model is in the irreversible form, the metabolic model might activate the forward and backward fluxes of reversible reactions at the same time and make cycles to unrealistically increase the number of genes with zAfter solving the MILP problem, all fluxes were bound to zero except reactions belonging to the set of MAR, and then, the production envelope was drawn. This envelope is determined by the flux variability of MAR at the target point. The coupling is strong if the minimum secretion rate of the desired product in lack of growth as shown in After finding the optimal envelope and MAR, sequential reinsertion of the deleted reactions was done to find minimal reaction knockouts needed to support the minimum secretion rate of the desired product if the coupling is strong. In fact, the minimum secretion rate was preserved in the reinsertion step to be sure that the coupling is strong and the lack of secretion of the desired product is infeasible.In case of unsatisfactory features of found envelopes setting the target point from envelope boundary to the internal area of the solution space can be useful, because it may result in an envelope with similar maximum production rate as for the primary target point but better other parameters (higher minimum production rate and/or fewer knockouts). So, evaluation of the envelopes for internal target points located in the desired space and comparison with the primary envelope obtained for the original target point located on the upper edge is necessary. Therefore, in this research, after finding the primary envelope for the primary target point, the line space between the production rate of the primary target point and the MP point as shown in For all target points, the envelope and its important parameters  are determined and considered selecting the best envelope. If the minimum production rate for an envelope is lower than that for the primary envelope or the number of knockouts for an envelope is more than that for the primary envelope, the envelope is not favourable and the envelope is not considered as an alternative. For the selection of the best envelope, various criteria can be considered. Higher values of maximum and minimum production rates at zero growth rate as well as higher production rates at the MP point of the envelope are desirable. For the similar production rates at MP point, higher growth rate is better to achieve higher productivity. Furthermore, the required number of knockouts to obtain the envelope is an important criterion for the decision. After running the code, all the data are presented in a table on the MATLAB’s command and the user can select the most appropriate envelope depending on the specificity of the biotechnological task.After finding the envelope and reinserting the reactions, the number of knockouts is determined by OptEnvelope. If necessary, it is possible to reduce the number of knockouts, however, that may result in an envelope with lower production rate. Hence, the effect of the reduction of the number of knockouts is required to be studied. For this purpose, a procedure for using the dual problem for the inner LP problem similar to the method used by Tepper et al.  is impleFor each envelope found in the previous section, it is easily possible to study the effect of the reduction of the number of knockouts. MAR of that envelope is added to the list of reactions that are not allowed to be knocked out and the number of allowed knockouts is determined. If it is equal to or more than the value found by the reinsertion step of OptEnvelope, the same envelope is found. Otherwise, if the number of allowed knockouts is set to be less than the determined value by the third step of OptEnvelope, an expansion of the envelope is observed after reducing the number of knockouts. An example in the results (subsection 3.2.) is presented to illustrate the effect of knockout reduction on an envelope. The pipeline of OptEnvelope is presented in S. cerevisiae and E. coli was investigated using OptEnvelope. The primary envelopes of two hosts obtained by the primary target points were compared in this research for simplicity.OptEnvelope can be implemented simply and quickly for various hosts, and thus can easily evaluate the impact of host change. Similar to the process of selecting the best envelope for a model, various criteria including higher values of maximum and minimum production rates, more production rate at the MP point of the envelope, and fewer necessary knockouts can be considered for the selection of the better host. The coupling of the production of acetate, glycerol, ethanol, and succinate in E. coli metabolic model at 3% of the maximal growth rate was selected. MAR and the primary envelope for the primary target point were calculated. Then, OptEnvelope selected target points inside the space using the MP point of the primary envelope, and after finding MARs for these target points, the corresponding envelopes were calculated. The characteristics of envelopes including the number of knockouts, name of reactions, and the rates for all envelopes found by OptEnvelope on E. coli for acetate production under aerobic condition are presented in According to the explanations in the previous section, a primary target point on the upper edge of the wild-type envelope of the The envelopes indicate that diverse results are presented for the middle points and it must be mentioned that some of the envelopes are found multiple times. The minimum number of knockouts is three , that correspond to the 8th envelope with a maximum growth rate of 0.146 1/h at an acetate production rate of 17.4 mmol/gDCW/h and minimum and maximum production rates of 7.6 and 25.6 mmol/gDCW/h, respectively. While this envelope represents the minimum number of knockouts, its production rate at MP point is less than the in other envelopes and even less than in the primary envelope. The 2nd envelope with six knockouts is also recommended having maximum growth rate of 0.143 1/h at a production rate of 22.7 mmol/gDCW/h and minimum and maximum production rates of 9.5 and 27.1 mmol/gDCW/h, respectively. The minimum production rate is increased 2.1 times compared to the primary envelope while a slight reduction in the maximum production is observed. The production rate at the MP point is a little more than that for the primary envelope which is valuable. So, all criteria for this envelope are better compared to the primary envelope and this demonstrates the usefulness of switching the target point into the solution space. It is interesting that knockout of ATP synthase is only suggested for envelopes with 3 and 6 knockouts. For the other envelopes with more knockouts, the removal of this important reaction is not needed. Thus, the various strategies proposed by OptEnvelope give the user freedom to decide not only the number of reactions but also the pathway of reactions to be knocked out.The highest minimum production rate and the production rate at MP point are 19.0 and 24.7 mmol/gDCW/h, respectively, which belongs to the 10th envelope with nine knockouts. Its maximum production rate is reduced to 25.2 mmol/gDCW/h which is about an 8 percent reduction compared to the primary envelope. This envelope has the minimum value of maximum growth rate (0.022 1/h) and this low growth rate might have a negative effect on the productivity of the knockout strain and needs to be evaluated by the user. So, if the higher number of knockouts and low growth rate are not acceptable, this envelope can be selected as the candidate envelope for stain design.To implement the limitation of the number of knockouts, after selecting an envelope  for E. coli and S. cerevisiae, respectively. Furthermore, the envelope of S. cerevisiae after reinserting the reactions (step three of OptEnvelope) is worse than its optimal envelope  including 7 reaction knockouts are almost the same. So OptEnvelope predicts that S. cerevisiae is a better host for glycerol production.S. cerevisiae is a well-known producer of glycerol and its better results illustrate that its metabolism has evolved to be a good glycerol producer. The experimental results show that S. cerevisiae uses glycerol as a sink for excess NADH and glycerol production increases with increased cytosolic NADH concentrations. Hence, the deletion of NADH2_u6cm (NADH dehydrogenase) suggested by OptEnvelope is logical. The experimental reports confirm that achieving high glycerol production yield by S. cerevisiae is possible. By deleting tpi1 and adh1 genes and overexpressing gpd1 and ald3, Cordier et al. [tpi1Δ nde1Δ nde2Δ gut2Δ S. cerevisiae strain.r et al.  achievedr et al.  achievedE. coli and previous reports confirm that E. coli is not a natural producer of glycerol and its fermentation has been achieved in E. coli using added pathways from other organisms [S. cerevisiae with deletions of the tpiA, mgsA, and edd genes [The experimental data also agree with the prediction of OptEnvelope for rganisms . A glycedd genes .E. coli is a better succinate producer than S. cerevisiae. The maximum theoretical yield of succinate for E.coli under anaerobic growth is 1.12 g/g which is higher than this value for S. cerevisiae (0.84 g/g) as presented in E. coli is excellent with a minimum succinate production yield of 0.93 g/g and a maximal growth yield of 0.01 g/g at a succinate yield of 1.1 g/g. This envelope can be obtained with nine knockouts while for achieving the envelope of S. cerevisiae, at least 46 knockouts are required. The succinate yields presented in two review papers [E. coli is an ideal host, and the yields for S. cerevisiae are much lower. It is also mentioned that achieving the succinate yield suggested by OptEnvelope of 1.1 g/g glucose is possible using E. coli [ldhA, pta, citF, and ptsGH supporting reactions LDH_D, PTAr, CITL, and GLCpts proposed by OptEnvelope for succinate overproduction by E. coli.Unlike with glycerol, w papers ,26 confi E. coli . The exp E. coli –27 also S. cerevisiae and E. coli can be promising ethanol producers  at a maximal anaerobic growth yield of 0.115 g/g according to S. cerevisiae strains can achieve ethanol yields above 90% of the maximum theoretical. For example, with some production processes in the Brazilian fuel production industry, the yield of 0.469 g ethanol per g glucose is reported [S. cerevisiae seems to be the better ethanol producer.While for three earlier cases, one of the two hosts was clearly superior, both microbes present acceptable results for ethanol production that indicate roducers . Both mireported . In addiE. coli and the ethanol yield at maximal anaerobic growth is improved from 0.12 to 0.466 g/g  with eight reaction knockouts. These results demonstrate that more knockouts are required to have an ethanol producer of E. coli compared to S. cerevisiae. The experimental data confirm that achieving high values of ethanol yield is possible even for E. coli with multiple gene knockouts. For example, by deleting eight genes from E. coli MG1655, Trinh et al. [Δzwf Δndh ΔsfcA ΔmaeB ΔldhA ΔfrdA ΔpoxB Δpta), which could produce ethanol with a yield of 0.36 g/g glucose in anaerobic conditions. Furthermore, by adding pdc and adhB genes from Zymomonas mobilis to the TCS083 strain, the yield improved to 0.49 g/g. Woodruff et al. [E. coli strain LW06 which can reach up to 80% of the theoretical yield on glucose.OptEnvelope is highly effective in the overproduction of h et al.  producedf et al.  also devWhile the previous methods of coupling attempt to directly find the best knockouts, OptEnvelope first uses a MILP formula to find minimal active reaction (MAR) at a biotechnologically attractive target point in the solution space of wild type strain. The strategy of initially finding the envelope and then finding knockouts makes OptEnvelope able to suggest a strong coupling if it exists.Considering that sometimes the envelopes located inside the area are more desirable because of some of their features, OptEnvelope tries to find all desired envelopes based on moving the target point towards the middle of the feasible space. As OptEnvelope is simple and fast to run, different target points can be easily evaluated and exploited. Then, the user can compare the envelopes based on various criteria  and select the best envelope for strain design. The user can decide about the number of knockouts and whether moving to the upper edges or staying inside the feasible solution is better depending on the number of knockouts. Furthermore, the diversity of envelopes located in the desired space with various sets of knockouts proposed by OptEnvelope enables the user to consider different priorities during the selection of the final envelope.It is also possible to determine the number of allowed knockouts as an input of OptEnvelope. After selecting the best envelope, if the number of knockouts is not acceptable, it can easily be reduced to a favourable value by the user and the effect of the reduction of the number of knockouts on the shape of the envelope is presented by OptEnvelope (section 3.2.). The existence of acceptable envelope with smaller knockouts depends on the features of the model. Increasing the number of middle points can increase the chance of finding good alternative envelopes at the cost of increasing the computation time.The size of the envelope and the number of deletions depend on the peculiarities of the metabolism of the producer organism. OptEnvelope is a fast tool for finding the most appropriate organism for a particular product having better productivity at maximal growth and a smaller number of deletions as a criterion.OptEnvelope ranked correctly the appropriateness of the analysed hosts indicating the natural adaptation of microbes for a specific product. A host is not appropriate for a product if the optimal envelope is not strong coupled or if the number of knockouts is too high for implementation. In such cases changing the host can be considered instead of shifting to a worse envelope of existing host with a lower number of knockouts. A good production envelope that is achievable with a small number of knockouts is an important indication that the metabolism of a microbe has naturally evolved to synthesize a product of interest.OptEnvelope can serve also for the assessment of sustainability features of an organism for production of a metabolite of interest due to easy switch between the models of organisms and the possibility to implement sustainability objective function  as a criS1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.