Pathogens alter the key metabolic processes in infected host, ultimately the objective of these integrative constraint-based context-specific metabolic models is to provide novel insights toward understanding the metabolic basis of the acute and chronic processes of infection, revealing cellular mechanisms of pathogenesis, identifying strain-specific biomarkers and developing new therapeutic approaches including the combination drugs. The reaction rates predicted during different time points of pathogen development enable us to predict active pathways and those that only occur during certain stages of infection, and thus point out the putative drug targets. Among others, fatty acid and lipid syntheses reactions are recent targets of new antimicrobial drugs. Genome-scale metabolic models provide an improved understanding of how intracellular pathogens utilize the existing microenvironment of the host. Here, we reviewed the current knowledge of genome-scale metabolic modeling in pathogen cells as well as pathogen host interaction systems and the promising applications in the extension of curative strategies against pathogens for global preventative healthcare.Pathogens give rise to a wide range of diseases threatening global health and hence drawing public health agencies' attention to establish preventative and curative solutions. Genome-scale metabolic modeling is ever increasingly used tool for biomedical applications including the elucidation of antibiotic resistance, virulence, single pathogen mechanisms and pathogen-host interaction systems. With this approach, the sophisticated cellular system of metabolic reactions inside the pathogens as well as between pathogen and host cells are represented in conjunction with their corresponding genes and enzymes. Along with essential metabolic reactions, alternate pathways and fluxes are predicted by performing computational flux analyses for the growth of pathogens in a very short time. The genes or enzymes responsible for the essential metabolic reactions in pathogen growth are regarded as potential drug targets, as  Mycobacterium tuberculosis takes the global priority.Pathogens give rise to a wide range of diseases threatening global health and drawing public health agencies' attention to establish preventative and curative solutions Sweileh, . As antiHaemophilus influenzae Rd in 1995, the advances in experimental and computational technologies have accelerated context-specific understanding of complex biological networks and the emergence of a new field of science, called systems biology  play crucial roles in GEMs. When hundreds or thousands of metabolic reactions occurring inside the pathogenic cells and their literature-based distribution among different compartments are taken into consideration, the reconstruction process is extensively time-consuming and needs to be standardized. This reconstruction process was described by various studies  of the metabolic reactions in the GEM. At steady state, a system of linear equations obtained from the metabolic network, is given by:Flux balance analysis (FBA) is a frequently used constraint-based modeling approach to represent the possible behavior of microbial metabolism and plays an irreplaceable role in genome-scale metabolic modeling  determines the possible range of flux quantities which is allowable with the given objective value  in the GEM holds a great importance  lowering agents or inhibitors of glycosylceramide synthesis can be considered as new opportunities to prevent fungal infections  lowering antifungal agents have an important potential to control and prevent infections. Acyhydrazones have been identified as the inhibitor of glucosylceramide synthase enzyme. When the glycosylceramide synthesis gene is deleted in in silico killing strategies of pathogenic bacteria is the main goal of the pathogen-specific GEMs; however, the maximum amount of production of valuable metabolites takes great importance in industrial microorganisms' GEMs with the minimum cost. Depending on the application of the GEMs, various approaches are also available in the literature. Among them, dynamic FBA (dFBA) was suggested for the study of the metabolic network dynamics since the classical FBA is used for steady state systems  and metabolomics data  by the same research group for comparative analysis of these two strains for understanding the pathogenicity  to provide insight into the relationship between virulence factor production and growth. These two in silico models included 112 and 108 virulence-associated genes, respectively. The genes only critical for virulence factor production, growth and both were analyzed and thus non-obvious links between virulence factor production and growth were observed. iMO1056 and Opt208964, an automatically generated GEM by Model SEED, were used as initial drafts in the development of iPAO1, which is then extensively curated using the literature and databases toward the study of polymyxin treatment in P. aeruginosa metabolism at the systems level  in fatty acid and phospholipid metabolism were predicted to be essential for growth and production of at least eight virulence factors. Growth-essential genes in aromatic amino acid synthesis  were predicted to be essential for the synthesis of six virulence factors which are chorismate, pyocyanin, 1-carboxyphenazine, salicylate, dihydroaeruginoic acid and pyochelin. Moreover, the interconnectivity for each knockout was computationally researched by plotting virulence factor vs. growth inhibition compared to wild-type simulations. Uncertain connections between growth and virulence factor production were determined from the distribution of data points on plots. Several growth-essential genes partially hindered virulence factor synthesis. The virulence factor-related essential genes were significantly altered and not correlated with pathway complexity.The essential genes in P. aeruginosa to polymyxin B treatment unlocked its effect on bacterial metabolism with the help of genome-scale metabolic model iPAO1  for E. coli was reconstructed for K-12 MG1655 commensal and common laboratory strain , two uropathogenic (UPEC) and two commensal strains) by means of pangenome and core GEM. In addition to several pathogen specific reaction deletions, eight new reactions unique to EHEC strains were added, which are fructose synthetase, gentisate 1,2,-dioxygenase, perosamine synthetase, salicylate hydroxylase, sucrose transport, urease, tellurite reduction and UDP-N-acetylglucosamine 4-epimerase reactions. UPEC strains included only one addition of propionate CoA-transferase reaction in common. Furthermore, one unique reaction was added to each UPEC strain; hydroxy-pyruvate reaction for UTI89 strain and galactose isomerase reaction for CFT073 strain. The comperative simulations of strain-specific E. coli models resulted in variations of biomass yields on glucose due to the strain-specific metabolic reactions.Due to its genetic simplicity as well as its history in a variety of infections, r et al.  developeE. coli strains were broadened ; however, increased number of reactions in the core metabolism to 1,065 from 885. This increase in the number of the reactions in the core metabolism among E. coli strains indicates a conserved metabolism in E. coli. The reactions absent from Shigella core metabolism were distributed in various pathways including D-allose degradation, phenylethylamine and phenylacetate degradation, and biosynthesis pathways related amino acids, nucleotides and fatty acids. Another strain-specific GEM was built for 55 strains of E. coli and Shigella to shine light on adaptations to diverse environments  were predicted significantly higher than those of experimental results. After adaptive evolution of K. pneumoniae to myo-inositol by serial passage, the experimental growth rate increased and the rate of growth error decreased from 80% to 24%. Hence, GEMs can also give remarkable clues toward uncovering adaptive evolution mechanism. In another study, the in silico iYL1228 was used as a platform to reconstruct GEMs for 22 K. pneumoniae strains  was identified as the highest-ranked putative drug target satisfying virulence, druggability and broad-spectrum criteria and ZINC95543764 was suggested as a potential Klebsiella inhibitor in gene-centric approach. The enzymes related to essential metabolites were suggested as putative drug targets in metabolite-centric approach. These are enoyl-(acyl carrier protein) reductase (FabI) in fatty acid metabolism, riboflavin synthase subunit α (RibC) and riboflavin synthase subunit β (RibH) in riboflavin metabolism, and penicillin-binding protein 1A-C  in the peptidoglycan biosynthesis. PBP 1A-C were found to be synthetic lethal and the remaining three were essential.The screening of putative drug targets in Salmonella differ in their antimicrobial host specificity, resistance profiles, and virulence phenotypes. For example, S. typhimurium is the leading cause of human gastroenteritis and have more than 2000 serovars. An in silico strain-specific metabolic reconstruction was performed for 410 Salmonella strains and the metabolic capabilities on minimal media with more than 500 different growth-supporting nutrition sources including carbon, nitrogen, phosphorous, and sulfur were compared across the Salmonella genus in aerobic and anaerobic conditions . A total of 21 catabolic pathways like the utilization of D-tagatose, L-xylulose, D-xylose, deoxy-D-ribose, L-idonate, D-glyceraldehyde, and allantoin, that form part of the host's diet and/or exist in the intestinal environment, contributed to Salmonella fitness during intestinal infection. The compositional differences in the intestinal vs. extraintestinal milieu of the host may reflect differences in pathogenicity of different Salmonella types.Salmonellae are gram-negative bacterial pathogens with a wide host range, causing millions of human infections and hundreds of thousands of deaths per year worldwide. The serovars of in silico model of S. typhimurium (iRR1083) provided a suitable platform to analyze the role of reactions important in infection and pathogenesis, such as those required for drug efflux, proton pumps, inhibitory effects of antibiotics, mechanisms for reactive nitrogen species (RNS) and reactive oxygen species (ROS) resistance  and virulent (can grow) in the host cells, respectively. Of the 24 in vivo essential genes from the literature, the in silico analysis correctly predicted 22 virulence characterizations. For example, AceA, which was predicted as non-essential and mutants defective in AceA are virulent. AceA is required only for chronic and not for acute Salmonella infections  responsible in acetyl phosphate formation lead to virulent strain. The fatty acid degradation genes fadA, fadD/fadE genes have been implicated for virulence in chronic Salmonella infection, however the model predicted the presence of glucose or pyruvate but not acetate or short chain fatty acids during the early stages of infection  was reported suggestive of an aerobic condition in vivo, during the early stages of infection  predictions were consistent with the literature  mutants and super oxide dismutase mutants (sodA and sodCII) are virulent, confirming the pathogens have multiple defenses to oxidative stresses. On the other hand, the model predicted that mutants defective in aromatic amino acid biosynthesis are avirulent.The S. aureus strains were reconstructed by using genome annotation, functional pathway analysis and comparative genomics approaches  were present in all strains. All these genes, exluding fabI, were determined as essential. The analysis of single unconditionally essential enzymes  resulted in 70 essential enzymes in one or more of 13 strains and 44 in all S. aureus strains. Of the 44 shared essential enzymes, minimum six of them were experimentally supported in S. aureus including transketolase, hydroxyl-methylbilane synthase, methionine adenosyltransferase, UDP-N-acetyl-glucosamine 1-carboxyvinyltransferase, protein N (pi)-phospho-histidine-sugar phosphortransferase and acetyl-CoA carboxylase  and B3 (niacin) were additionally required to grow in glucose minimal media. The thiamin and niacin auxotrophy are due to absence of the pathways converting tyrosine to thiamin and nicotinate-nucleotide diphosphorylase, respectively. The prediction of growth capabilities on alternative sources  indicated 238 nutrients including glucose and glycerol as carbon sources and arginine as nitrogen source are used by all 64 in silico S. aureus strains. Among other strain-specific nutrients, both uracil and thymidine were predicted as nitrogen source for 42 strains, and dulcose and inosine as carbon source for 42 and 13 strains, respectively. The simulations of growth resulted in the presence of two virulence factors (staphylokinase and IgG binding protein A precursor) and the ability to catabolize maltotriose distinguished human-associated strains from livestock-associated strains. Thus, the simulations based on genome-scale metabolic modeling allow the classification of S. aureus strains according to the presence of virulence factors.Strain-specific GEMs enabled phenotype predictions via metabolic flux distributions of 64 Burkholderia mallei and Burkholderia pseudomallei) and three category A pathogens which are Yersinia pestis Bacillus anthracis and Francisella tularensis causing plague, anthrax, and tularemia, respectively  evaluated the potential threats from various microorganisms and classify them into three categories , the family of Enterobacteriaceae. The shared essential enzymes should be further exploited for the development of broad-spectrum antibiotics that can be used against this group of human pathogens.A GEM study (iPC815) on in silico analysis of Francisella strain demonstrated that Francisella tularensis undergoes significant changes of its metabolism upon its entry into the host cell (during intracellular growth)  gene in Francisella and the adenylyosuccinate lyase gene that catalyzes its formation, is a condition independent lethal gene, essential for survival and are proposed as a potential drug target. Thus, the predicted accumulation of AICAR in the host (macrophage) revealed a role as a potential master regulator during infection.The F. tularensis to select a group of enzymes as drug targets and ~20,000 small-molecule compounds were screened to list potential inhibitors against these targets , pantetheine-phosphate adenylyltransferase (CoaD), chorismate synthase (AroC) and sedoheptulose 7-phosphate isomerase (LpcA) enzymes, that are important in various metabolic pathways, these enzymes were suggested as potential drug targets. NadE takes part in NAD+ biosynthesis, CoaD in coenzyme A biosynthesis, AroC in phenylalanine, tyrosine and tryptophan biosynthesis and LpcA in lipopolysaccharide biosynthesis. These four enzymes are putative drug targets and the experimental validation of their enzymatic inhibition is an important step in the development of candidate antimicrobial compounds.A multidisciplinary approach was applied to Y. pestis, B. anthracis and F. tularensis were examined toward having a common drug target. Nine essential enzymes were shared between these three high threat pathogens  was reconstructed by combining iAB-AMØ-1410 and iNJ661 which represent Salmonella metabolism during infection was studied by using host-cell nutrient environment and gene expression data obtained from S. typhimurium growing inside macrophage cell lines  was performed in three different host niches including human bloodstream, urinary tract and macrophage  containing all metabolic reactions and associated genes from 16 E. coli genome, the experimentally-based nutrient compositions were used as simulation constraints to mimic these three host environments . Therefore, this control strain lost highest number of these genes predicted for each of the three host environments. As expected, most of the lost essential or important genes were computed in intestinal pathogen E. coli O157:H7 due to evolutionary outcomes. The simulations of E. coli UT189 successfully predicted least amount of essential and important genes lost in human bloodstream and urinary tract where it causes infection. However, the inconsistencies in urinary tract simulations demonstrated a necessity of additional constrains for more accurate predictions in this location.Prediction of essential genes for enterobacterial human pathogens  , the metabolism of the parasite Salmonella enterica subsp. enterica serovar Typhimurium (S. typhimurium) were higher if the infection occurred during the rest phase compared to the infection initiated in the middle of the active phase  can regulate cellular immunity against bacteria, viruses, and parasites  differences. The diagnosis and treatment related challenges can be solved by examining the metabolic fluxes in tissues and develop strategies for treatment options on the basis of few experimental data. In brief, HP models help elucidate the role of host environment on pathogen metabolism during the course of an infection.Host-pathogen (HP) models have unraveled the pathogen adaptation and carbon source utilization de novo synthesis from palmitoyl-CoA and serine, sphingomyelin hydrolysis by sphingomyelinases and reacylation of sphingosine catalyzed by ceramide synthase) and consumption reactions covering conversion to sphingosine, sphingomyelin, glucosylceramide and ceramide 1-phosphate catalyzed by the enzymes of ceramidase, sphingomyelin synthase, glucosylceramide synthase and ceramide kinase, respectively.During the microbial infection process, the sphingolipid molecules play a critical role in host-pathogen interaction mechanism  converts sphingomyelin to ceramide and has a great importance in membrane reorganization. Several bacterial pathogens activate the ASM and ceramide-enriched membrane platforms are formed in response to increase in ceramides  , H. influenzae, Chlamydia trachomatis, Legionella pneumophila, Candida albicans, Cryptococcus neoformans, Aspergillus fumigatus, etc.  would be an alternative solution. For the rare bacterial species involved in infections, an economically feasible route for pharmaceutical companies may be the search for broad spectrum antibiotics. Another option would be the development of combinatorial treatment protocols using current drugs that are already in use against infections. Here, it is strictly necessary not to forget the human orthologs, which need to be excluded from being drug targets in order to avoid side effects. The computational genome scale models presented in this review article have not only the ability to reduce the search space for novel drug targets in the pathogen metabolism, but can also give insights on putative side effects on host metabolism. These metabolic network models can be used as an additional screening tool to predict potential toxicity of the predicted target in a healthy cell or tissue model. Many side effects are due to patient-to-patient genetic variability. Using context specific constraint-based models integrated with omics data, the potential causes of the side effects such as drug off-target binding, downstream transcriptional effects (changes in gene expression that are induced with drug treatment), and the pharmacokinetics of drug clearance can be predicted in patients and serve as In case of extremely deadly pathogens, this genome scale modeling approach is indispensable since experimental analyses are associated with the need for high-security laboratory conditions, and in some cases prohibited. These models identify novel virulence genes as well as enable the evaluation of pathogen metabolism, predict disease phenotypes and hence get the infection scenario. They offer insight into why the same microbial pathogen may cause disease in some host environments but not others.Pathogen species, infection stages and host niches have great importance and need to be taken into consideration in clinical and computational studies to extend curative strategies against pathogens for global preventative healthcare. Throughout the infection course, different pathogens develop various stage-specific interaction mechanisms to host cells depending on their invasion and response features. The experimental elucidation of such complex infection process requires long and difficult work alone. An effective application of stage-specific genome-scale metabolic modeling approach guides experimental studies and facilitate the development of novel candidate drugs targeting key essential biomolecules at all stages infection.MS and KOU contributed to writing the manuscript and preparing the table and figures. Both authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Pathogenic microorganisms exploit host metabolism for sustained survival by rewiring its metabolic interactions. Therefore, several metabolic changes are induced in both pathogen and host cells in the course of infection. A systems-based approach to elucidate those changes includes the integrative use of genome-scale metabolic networks and molecular omics data, with the overall goal of better characterizing infection mechanisms for novel treatment strategies. This review focuses on novel aspects of metabolism-oriented systems-based investigation of pathogen-human interactions. The reviewed approaches are the generation of dual-omics data for the characterization of metabolic signatures of pathogen-host interactions, the reconstruction of pathogen-host integrated genome-scale metabolic networks, which has a high potential to be applied to pathogen-gut microbiota interactions, and the structure-based analysis of enzymes playing role in those interactions. The integrative use of those approaches will pave the way for the identification of novel biomarkers and drug targets for the prediction and prevention of infectious diseases. One major focus of systems biology in medical research is the integrated use of high-throughput omics data and molecular interaction networks, with the overall goal of elucidating molecular mechanisms of diseases to identify potential biomarkers and personalized treatment strategies. A wealth of research has been conducted to apply this approach on infectious diseases by focusing on cell metabolism. This included collecting transcriptomic, proteomic, or metabolomic data from pathogens or infected hosts on one side, and developing interaction models focusing on the enzymes and reactions of crucial metabolic pathways on the other side Bumann, .Rather than investigating host and pathogen metabolisms separately, novel systems biology based approaches consider the pathogen-host interaction (PHI) system as a whole to elucidate the mechanisms of infection. Here, omics data is collected from both organisms in a single experiment, termed dual-omics data, focusing on all the molecules within the PHI system . The bacteria CjR is now resistant to many antibiotics. Therefore, there is a need to predict new drug targets based on unique and new pathways of the resistant CjR. In the first phase, the authors used the complete protein set of the resistant CjR to prioritize the essential proteins of CjR taking role in unique metabolic pathways. These unique pathways were pathogen specific only, therefore, absent in the host organism. In the next step, the authors filtered out the human homolog proteins. Finally, using this subtractive genomics strategy, they identified 38 eligible therapeutic targets, about 10 of which were metabolic enzymes. They studied corresponding 3D structural interactions with FDA-approved drugs. Additionally, they identified peptidoglycan biosynthesis as a pathway based drug target, to be explored by experimental methods. To help future research, a comprehensive database was also made, and it has 3D structure and other related data of the listed drug targets.Hossain et al.  presenteEntamoeba histolytica, Leishmania donovani, Trichomonas vaginalis, and Salmonella typhimurium. Since the Sulfur metabolism pathway is absent in human and all other mammals, the enzymes taking role in this pathway were best considered as drug targets and subjected to the structure based methods to predict potential strong binders , which is the main source of nosocomial infection worldwide, Uddin et al. (The pathogens and host relationship is governed by the Protein-Protein Interactions (PPIs) and several of those interactions can involve enzymes. With methicillin resistant n et al.  developen et al.  the pathWith the birth of dual-omics technologies, we have entered a new era where interactions between pathogens and their host can be directly predicted to unravel molecular mechanisms of infectious diseases. Although integrated pathogen-host GMNs are currently scarce, we believe that the increase in the number of dual omics datasets, especially dual RNA-seq studies, will pave the way for the reconstruction of more and more integrated GMNs . MappingTÇ conceived the study. TÇ, GP, RU, and SD wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Pseudomonas putida KT2440, an emerging industrial microbe. From the 63 solution-sets, our omics guided process identifies one experimentally feasible solution requiring 14 simultaneous reaction interventions. We implement a total of 14 genes knockdowns using multiplex-CRISPRi. MCS-based solution shifts production from stationary to exponential phase. We achieve 25.6 g/L, 0.22 g/l/h, and ~50% maximum theoretical yield (0.33 g indigoidine/g glucose). These phenotypes are maintained from batch to fed-batch mode, and across scales .High titer, rate, yield (TRY), and scalability are challenging metrics to achieve due to trade-offs between carbon use for growth and production. To achieve these metrics, we take the minimal cut set (MCS) approach that predicts metabolic reactions for elimination to couple metabolite production strongly with growth. We compute MCS solution-sets for a non-native product indigoidine, a sustainable pigment, in  Pseudomonas putida.The trade-off between growth and production affects the application of engineered microbes. Here, the authors take the minimal cut set approach to predict metabolic reactions for elimination to couple metabolite production strongly with growth and achieve high production of indigoidine in  Yet, even the most well-designed heterologous pathway requires considerable additional work to reach the titers, rate and yield (TRY) necessary for the adoption of these systems by industry4. In addition, the production parameters of a strain at lab-scale are often not predictive of its performance and robustness when cultivated in different modes or at larger scales. Consequently, only a small fraction of such bioproduction strains have been successfully scaled and deployed2. In contrast, many native microbial processes show high productivity and reliability at scale and represent the most prominent examples of successful high-volume bioproduction. Examples include the generation of ethanol5 and organic acids7 during fermentation where production of these metabolites are required for carbon utilization during fermentative growth. Recently, a minimal cut set (MCS)-based approach8 showed that theoretically, production of a majority of metabolites can be strongly coupled to growth via elimination of a minimal set of metabolic reactions. Strong growth coupling9 is defined as metabolic rewiring, which demands production of the target metabolite  even when cells do not grow. Metabolic flux towards unwanted side-products is minimized, and metabolite production is feasible even when cell growth is sub-optimal or negligible. In this study we examine if coupling production of a heterologous product to microbial growth is possible, and if such dependence could lead to high TRY and the ability to maintain production parameters across different growth modes and scales.Synthetic biology approaches for heterologous, non-native bioproducts, can provide alternative sustainable routes to a vast number of chemicals ranging from fuels and commodities to fine chemicals. Heterologous production has been demonstrated for many desirable compounds and in a wide variety of microbial hosts10, a non-ribosomal peptide, as the heterologous product to prototype our approach. Indigoidine is a viable alternative for colorants in the dye, ink, and pigment industry11. Development of a robust production system for this compound stands to have an immediate benefit as a sustainable dye in the garment industry where the use of petrochemical derived dyes contribute to its negative impact on the environment14. We implemented this system in Pseudomonas putida KT2440 , leveraging the availability of the iJN1462 genome scale metabolic model (GSMM) for P. putida KT244016.We use indigoidine8 to compute intervention strategies that enforce strong growth coupled product formation. We combine these analyses with publicly available -omics data18 to exclude essential genes from editing. The corresponding set of gene loci are repressed using multiplex CRISPR interference (CRISPRi), which we optimize for use in P. putida KT2440. Our implementation results in a highly edited strain that, in a single iteration of strain engineering, achieves close to 50% max theoretical yield of indigoidine in P. putida KT2440 and TRY characteristics that maintain fidelity from laboratory to industrially relevant scales.Here, we use the minimal cut set (MCS)-based approachP. putida iJN146216 model that can be made essential for growth. For this analysis we used the MCS algorithm8 that identified minimal sets of reactions (cut sets), the elimination of which would enforce strong growth coupled production of a given metabolite (see “Methods”). Our initial analysis revealed that for P. putida around 99% of the producible metabolites accounted for in the genome scale model had the potential for strong growth coupling. This potential growth coupling for all metabolites is consistent with reported calculations demonstrated in other hosts8  as cofactors. The maximum theoretical yield (MTY) for glutamine and indigoidine was calculated to be 1.141 mol/mol and 0.537 mol/mol, respectively, from glucose as the carbon source , we then needed to extract the gene–protein-reaction relationships (GPRs) for each of the reactions embedded in the GSMM to translate the selected metabolic reactions into specific genes and assess their role in other functions. We eliminated potential cut sets that targeted genes coding for multi-functional proteins, as we sought to limit additional metabolic perturbations that could confound our analysis. A total of 882 reactions were excluded from consideration. These included spontaneous reactions, exchanges, transporters, some core essential reactions such as ATP maintenance, ATP synthesis reactions, reactions associated with the TOL plasmid genes and reactions that were not assigned a GPR. A total of 826 reactions out of 2928 were not associated with any genes. Of these 826 reactions, 740 reactions are either exchanges, transporters, biomass reaction, ATP maintenance, demand or sink reactions. Only 86 (10%) are true cytosolic reactions that might be associated with unidentified GPR. Their inclusion in the solution under such constraints would assist in identifying unknown GPRs and hold promise for further investigation. Of the 2030 reactions in iJN1462 that are associated with genes, only 60% have a single gene associated with them. If a metabolic reaction was catalyzed by more than one gene product (genes coding for isozymes or multi-subunit enzymes), we included both genes for inactivation. From this workflow, we analyzed 63 cMCS in total but only one feasible cMCS emerged with the predicted potential for high indigoidine titer .We then sought to confirm if the set of specific genes (rather than enzymatic reactions) for intervention was metabolically sound. For this we used flux balance analysis (FBA) and flux variability analysis (FVA) to confirm that the 16-gene cMCS strategy resulted in obligatory glutamine production. Using our constructed MCS algorithm-based platform Fig. , we set P. putida, such as para-coumarate and lysine22. These substrates are important carbon streams that could be utilized from lignocellulosic biomass hydrolysates24. FBA with these alternate carbon sources  indicated that a strain engineered using the 16-gene cMCS strategy for the glucose would fail to produce glutamine 27 from Streptomyces lavendulae that catalyzes indigoidine formation from two molecules of glutamine in an ATP-dependent manner10. Activation of BpsA requires a post-translational pantetheinylation conferred by a promiscuous Sfp from Bacillus subtilis28. The genomically integrated production strain harboring a plasmid-borne dCpf1 and non-targeting genomic RNA (gRNA) serves as the control production strain. The basal production of indigoidine in P. putida is 2.3 g/L indigoidine from 10 g/L glucose after 24 h 18 35. An endonuclease-deficient class II CRISPR-Cas enzyme, FnCpf136, was chosen over Streptococcus pasteurianus dCas9 as the Cpf1 crRNA is only 19 bp in size, compared to the corresponding crRNA (gRNA scaffold sequence) from Cas9, which is 76 bp37. Each gRNA was driven by a different P. putida tRNA ligase promoter/terminator pair, and dCpf1 was placed under the control of the lacUV5 promoter. Minimal 100-bp promoter sequences from native tRNA ligases were sufficient to express mCherry fluorescent protein, confirming that heterologous messenger RNA (mRNA) transcripts for gRNAs would be generated . Indirect changes in gene expression were detected  of the genes targeted with CRISPR interference. We used RNAseq analysis to examine the engineered strain, and compared normalized RNA expression levels to the control strain Fig. . RNA expted Fig. , consistpara-coumarate as the sole carbon source, in agreement with the modeling  of 12.5 g/L indigoidine from 60 g/L glucose. The control production strain produced 5 g/L, and production of the final molecule was realized after glucose was exhausted from the medium. When galactose was fed, the engineered strain also exhibited improved titers of 25.6 g/L of indigoidine from 60 g/L galactose as opposed to the control strain that generated only around 900 mg/L of indigoidine; a 28-fold improvement in production was observed in the engineered strain. Moving to an industrially relevant cultivation format did not impact the final product titer, allowing us to further develop cultivation methods by switching to fed-batch mode.We realized greater improvements in final product titer as well as improvements in production kinetics in the fed-batch mode using the ambr® 250 system. After administering an initial high-nutrient feed to increase biomass in the reactor, we reduced the feed rate to study indigoidine product formation during exponential phase growth Fig. . During This study implements genome scale MCS predictions via CRISPR interference that resulted in a strain where production was paired with growth. Pairing the final desired product with the carbon substrate for growth mimics native obligatory product formation such as ethanol production and results in high productivity at scale. Further, to our knowledge, there are no other reports where the production of a non-native molecule was shifted from stationary phase to exponential phase as a result of strain engineering.40 to growth decoupling41. Canonical approaches to growth coupling are FBA-based methods such as OptKnock42 and are conceptually different from MCS-based methods. Optknock and related methods  use bi-level optimization to identify gene edits, which increase flux to the target of interest around the solution space that maximizes growth. The MCS algorithm does not maximize growth and is unbiased towards any part of the solution space. Further, the MCS algorithm allows us to set boundaries for minimum demanded production and growth, thus providing strong coupling constrained MCS (cMCS) solution-sets. These optimization strategies make different assumptions on cellular metabolism and have been extensively reviewed elsewhere44. OptKnock related methods have been used in growth coupled strategies for primary alcohols  and organic acids . However, for a large subset of native metabolites, including amino acids , FBA-based approaches often result in “non unique” solutions, which are optimized for production but not necessarily for growth. In contrast, the recently delineated MCS-based approach8 provides strong coupled solutions sets for a large number of metabolites within a reasonable computation time and fit well with the downstream biological constraints for CRISPR/dCpf1 rewiring. A comparison of solution-sets generated with OptKnock  even when cells do not grow. Our approach relies on computation of cMCS to provide targets at the genome-scale levelach Fig.  also repWhile our engineered strains showed many desirable phenotypes, several aspects merit additional discussion. The predicted constrained minimal cut set (cMCS) demands zero flux through these reactions for strong growth coupling. We excluded two genes from the predicted gene set due to their essentiality. Of the remaining gene targets, our RNAseq and proteomic data indicates a partial gene knockdown, implying that a non-zero flux could occur through the predicted reactions. The resulting yield space for indigoidine production is now different from what was predicted by MCS algorithm Fig. . The yieE. coli33) would enable more efficient CRISPR mediated gene knockdown. Similar fold repression of targeted proteins by CRISPRi/dCas9 was recently reported38, suggestive of a limitation for existing CRISPR systems in P. putida. The plasmid-based CRISPRi system retained stable phenotype for 6 days, but can be further stabilized using genomic integration of the dCpf1/CRISPRi system or by developing multiplex gene deletion strategies55. Directly targeting proteins for degradation in a multiplex format56 could eventually be applied to prokaryotes57 and would sidestep the reliance on variable protein turnover kinetics. Additional reduction of competing reactions that draw on glutamate might only have a negligible impact on predicted indigoidine titer , while E. coli (0.4 mol/mol) and S. cerevisiae (0.079 mol/mol) are lower. Genome scale metabolic models are now available for over 500 bacterial and eukaryotic organisms62 and we can assess the extent to which such multi-gene engineering would be useful for a target across hosts.Selecting the best host-final product pair is crucial to developing the ideal production platform, and provides a key consideration in broadening our approach to additional studies. In earlier reports 66, may also overcome these limitations. We also do not take into consideration products or intermediates that may be toxic. Industrial processes use renewable carbon sources that may contain growth inhibitory byproducts67. To solve these issues, tolerance engineering68 or adaptive laboratory evolution (ALE)69 could be useful. Production paired growth also enables powerful strategies like ALE to be used for direct improvement of production. The indigodine production system described here is of immediate interest to the biotech industry, and our methodology provides an avenue for the rapid prototyping of other scalable microbial production systems.We propose using our workflow to calculate the % MTY for any given host-product as an early decision point to quickly filter for viable host-product pairs. It is likely that a given target will not only have different MTY limits across different hosts, but that the constraints will be different across targets. To show that our strategy is implementable for other targets, we calculated the maximum achievable yields for other product pairing regimes, and include the complete gene set required to implement two additional targets: a biodiesel precursor methyl ketonePseudomonas putida KT2440 genome scale metabolic model (GSMM) iJN146216 was used. Aerobic conditions with glucose as the sole carbon source were used to model growth parameters. The ATP maintenance demand and glucose uptake were 0.97 mmol ATP/gDW/h and 6.3 mmol glucose/gDW/h, respectively. Constrained minimal cut sets (cMCS) were calculated using the MCS algorithm8 available as part of CellNetAnalyzer (version 2018.2). Excretion of byproducts was initially set to zero, except for the reported overflow metabolites for secreted products specific to P. putida . Additional inputs including minimum demanded product yield  and minimum demanded biomass yield at 10 or 25% of maximum biomass yield were also specified in order to constrain the desired design space. The maximum size of MCS was kept at the default (50 metabolic reactions). Knockouts of export reactions and spontaneous reactions were not allowed. The algorithm computed all minimal combinations of reaction knockouts blocking all undesired flux distributions and maintaining at least one of the desired metabolic flux distributions. With the specifications used herein each calculated knockout strategy (cMCS) demands production of glutamine even when cells do not grow. All cMCS calculations were done using API functions of CellNetAnalyzer70 on MATLAB 2017b platform using CPLEX 12.8 as the MILP solver. A summary of 417 common metabolites with the respective number of cut sets and number of targeted reactions to satisfy coupling restraints is included  or Gurobi Optimizer 8.1 (http://www.gurobi.com/) as the linear optimization solver. Production envelope was obtained using the internal COBRA Toolbox function, productionEnvelope, and plotted for P. putida  was used to calculate the maximum theoretical yield (MTY) from reaction stoichiometry and redox balance and also for single-gene deletion analysis. Flux variability analysis (FVA) was used along with FBA to check for minimum and maximum glutamine or indigoidine flux under the identified cMCS strategy to confirm product obligatory growth. FVA was performed with maximization of biomass formation as the objective function and the proposed gene deletions in each cMCS strategy along with constraints that were used for cMCS calculations. A positive minimum and maximum flux through the exchange reaction for the metabolite of interest (glutamine or indigoidine) confirmed production obligatory growth. COBRA Toolbox v.3.0ida Fig.  as a fra72 as described (15 mM (NH4)2SO4, 47.9 mM Na2HPO4, 22 mM KH2PO4, 8.56 mM NaCl, 2 mM MgSO4, 100 µM CaCl2) with the following modifications. Carbon sources  were used at 56 mM unless indicated otherwise. Trace minerals were purchased from Teknova Inc  and used diluted 2000-fold.All chemicals and reagents were purchased from Sigma-Aldrich  unless mentioned otherwise. When cells were cultivated in a microtiter plate format, plates were sealed with a gas-permeable film . Tryptone and yeast extract were purchased from BD Biosciences . Engineered strains were grown on M9 Minimal MediaPseudomonas putida KT2440 was used as the host for strain engineering. All strains used in this study are described in Supplementary Table 73. Cells transformed with replicative plasmid DNA were allowed to recover at 25 °C for 2.5 h before plating on selective agar media at 23 ˚C for overnight incubation. Cells transformed with non-replicative (integrating) plasmids were allowed to recover for 4–8 h in LB media before plating on selective agar media at 23 ˚C for an additional 24 h. Kanamycin sulfate or gentamicin sulfate  was used at a concentration of 50 µg/mL or 30 µg/mL, respectively. Integration of the Escherichia coli galETKM operon or the heterologous indigoidine gene pathway was implemented using a kanamycin/sucrose-counterselection plasmid for allelic exchange74. After confirming sucrose resistance and kanamycin sensitivity by patching clones onto solid agar media, correct clones were confirmed for the genotype by colony PCR using Q5 Polymerase enzyme . The dCpf1/CRISPRi system was adapted for use in P. putida by subcloning an endonuclease dead Francisella tularensis subsp. Novicida cpf175 into a pBBR1 backbone and placed under the LacUV5 promoter. The synthetic gRNA array was constructed using gene synthesis techniques  and cloned into the dCpf1/CRISPRi backbone using isothermal DNA assembly. All plasmid constructs were verified with Sanger sequencing before transformation into P. putida.Glucose and organic acids from cell cultures were measured by an 1100 Series HPLC system equipped with a 1200 Series refractive index detector (RID)  and Aminex HPX-87H ion-exclusion column . 300 µL aliquots of cell cultures were removed at the specified time points during production and filtered through a spin-cartridge  with a 0.45-μm nylon membrane, and 10 μL of the filtrate was eluted through the column at 50 °C with 4 mM sulfuric acid at a flow rate of 600 μL/min for 30 min. Metabolites were quantified with an external standard calibration with authentic standards.P. putida with slight modifications as previously described76. Briefly, indigoidine is insoluble in most aqueous solutions and organic solvents except for dimethyl sulfoxide (DMSO) and dimethylformamide. We purified indigoidine by solubilizing all other materials with sequential resuspensions using different solvents. Cells were lysed by vortexing cells in 1% SDS and 100 mM NaCl and then centrifuged at 14,000 x g for 3 min. The supernatant was discarded and the pellet was washed with three rounds of methanol, isopropanol, water, ethanol, and hexane to remove contaminating proteins and metabolites. The pellet was allowed to dry overnight and then resuspended in DMSO at a final concentration of 1 mg/mL. Indigoidine purity was characterized by nuclear magnetic resonance. The sample was protected from direct exposure to sunlight to avoid photo-bleaching. A standard curve correlating indigoidine concentration to OD612 was generated using this reagent . Indigoidine was quantified by measuring the optical density (OD612) using a microplate reader  preheated to 25 °C and applying a standard curve generated from indigoidine. The equation used wasE. coli and P. putida biosynthetic indigoidine samples. The DMSO-solubilized cell lysate from wild-type P. putida does not contribute detectable signal when measured at OD612.To rapidly quantify indigoidine production in a high-throughput manner, a colorimetric assay was used as a proxy for indigoidine titer. Briefly, 100 µL of cells were pelleted by centrifugation at 20,000 x 600 to biomass conversion estimates77. 1.0 OD600 was converted to 0.38 g/L of dry cell weight.To correlate indigoidine yields with biomass yields, the dry cell weight was determined using OD78. RNA from trizol treated lysates were bound to a silica column  and its integrity confirmed using a Bioanalyzer RNA 6000 Nano assay . rRNA was removed from 100 ng of total RNA using Ribo-Zero(TM) rRNA Removal Kit . Stranded cDNA libraries were generated using the Illumina Truseq Stranded mRNA Library Prep kit. The rRNA depleted RNA was fragmented and reversed transcribed using random hexamers and SSII  followed by second strand synthesis. The fragmented cDNA was treated with end-pair, A-tailing, adapter ligation, and ten cycles of PCR amplification. Prepared libraries were quantified using KAPA Biosystem’s next-generation sequencing library qPCR kit  and run on a Roche LightCycler 480 real-time PCR instrument. Sequencing of the flowcell was performed on the Illumina NovaSeq sequencer using NovaSeq XP V1 reagent kits, following a 2x150nt indexed run protocol. Reported gene expression values are the total normalized transcripts per kilobase million (TPM). Volcano plots and statistical analysis was conducted using the Geneious Differential Expression package in Geneious Prime (www.geneious.com). P-values were calculated assuming a binomial distribution and a random sampling model using Fisher’s exact test was applied. All raw data is available through NCBI-SRA associated with NCBI-Bioproject or via JGI (refer to Data Availability below).Total RNA was prepared by Trizol-based RNA extractiong and flash frozen with liquid nitrogen at − 80 °C until ready for processing. Cells were lysed in 100 mM NaHCO3 using 0.1 mm glass beads in a Biospec Beadbeater  with 60 s bursts at maximum power and repeated three times. Cell lysates were cooled on ice between each round. The clarified supernatant was harvested by centrifugation at 20,000 x g. The lysate protein concentration was estimated following the manufacturer’s directions for the BCA method . Proteins were quantified for analysis using a SRM-targeted proteomic assay80. The SRM methods and data are available at Panoramaweb [https://panoramaweb.org/genome-scale-rewiring-indigoidine.url].A targeted SRM (selected reaction monitoring) method was used to quantify relative levels of pathway proteins in samples under the various tested conditions in a 60 mL cultivation format. At the time points indicated, 1 mL of each sample was pelleted by centrifugation at 20,000  x P. putida Ec.galETKM strains for growth in M9 minimal salt media with galactose had a long initial adaptation phase of around 96–120 h before cultures showed turbidity. All cultures were incubated with shaking at 200 rpm and 30 °C. To prepare cells for pathway induction, M9 adapted cultures were back-diluted to a starting OD600 of 0.1, at which point IPTG and arabinose were added as appropriate. Production cultures grown in 24-well deep-well plates  inoculated into a 200 µL culture volume and incubated InFors Multitron HT Double Stack Incubator Shaker  set to 999 rpm linear shaker, and 70% humidity. For shake flask experiments, 60 mL cultures were grown in 250 mL unbaffled Erlenmeyer shake flask and incubated at 200 rpm with orbital shaking. For all experiments, the indigoidine pathway was induced with 0.3% w/v l-arabinose, and dCpf1 mediated gene repression was induced with 500 µM IPTG. Production assays were performed in independent biological triplicate and repeated at least twice, except for the scale-up experiments (described below), which were performed in biological duplicate. Standard error or standard deviation from the mean are shown as indicated and were calculated using GraphPad Prism 8. Preparation of cellular growth curves are described in , and 50 mL overnight pre-culture in the same media. Feeding solution contained 100 g/L glucose, 300 mM NH4+ along with l-arabinose and kanamycin. The temperature was held constant at 30 °C. The bioreactor pH was monitored using the Hamilton EasyFerm Plus PHI VP 225 Pt100  and was maintained at a pH of 7 using 10 M sodium hydroxide. Dissolved oxygen concentration was monitored using Hamilton VisiFerm DO ECS 225 H0.Batch experiments were performed using a 2 L bioreactor equipped with a Sartorius BIOSTAT B® fermentation controller , fitted with two Rushton impellers fixed at an agitation speed of 800 rpm. Initial reactor volume was 1 L M9 Minimal Media . The vessels were filled with 150 mL M9 minimal salt media containing 10 g/L glucose as carbon source. Temperature was maintained at 30 ˚C throughout the fermentation process and agitation was set constant to 1300 rpm. Airflow was set constant to 0.5 VVM based on the initial working volume and pH was maintained at 7.0 using 4 N NaOH. Reactors were inoculated manually with 5 mL of pre-culture cell suspension. After an initial batch phase of 12 h, cultures were fed with a concentrated glucose feed solution  by administering feed boluses every 2 h restoring glucose concentrations to 10 g/L (feed parameters: 3.1 min @ 50 mL/h). After observing glucose accumulation, feed addition was paused and resumed at reduced feed rates when glucose levels dropped below 10 g/L (1 min @ 50 mL/h). Experiments with a continuous feeding regime were initially fed at 1.3 mL/h (0.3 mL/h after seeing glucose accumulation). Samples were taken 1–2 times every day (2 mL) and stored at −20 °C. The ambr® 250 runtime software and integrated liquid handler was used to execute all process steps unless stated otherwise.Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4