Synechococcus elongatus PCC 7942 with a synthetic metabolic pathway. The strain into which the synthetic metabolic pathway was introduced produced 3.48 mM (0.265 g/L) 1,3-PDO and 14.3 mM (1.32 g/L) glycerol during 20 days of incubation. In this study, the productivities of 1,3-PDO were improved by gene disruption selected by screening with in silico simulation.Production directly from carbon dioxide by engineered cyanobacteria is one of the promising technologies for sustainable future. Previously, we have successfully achieved 1,3-propanediol  production using S. elongatus PCC 7942. A genome-scale model of S. elongatus PCC 7942 constructed by Knoop was modified by the addition of a synthetic metabolic pathway for 1,3-PDO production. Next, the metabolic flux distribution predicted by metabolic flux balance analysis (FBA) was used for in silico simulation of gene disruption. As a result of gene disruption simulation, NADPH dehydrogenase 1 (NDH-1) complexes were found by screening to be the most promising candidates for disruption to improve 1,3-PDO production. The effect of disruption of the gene encoding a subunit of the NDH-1 complex was evaluated in the 1,3-PDO-producing strain.First, a stoichiometric metabolic model was applied to prediction of cellular metabolic flux distribution in a 1,3-PDO-producing strain of ndhF1-null 1,3-PDO-producing strain showed the highest titers: 4.44 mM (0.338 g/L) 1,3-PDO and 30.3 mM (2.79 g/L) glycerol. In this study, we successfully improved 1,3-PDO productivity on the basis of in silico simulation of gene disruption.During 20 days of incubation, the The online version of this article (10.1186/s12934-017-0824-4) contains supplementary material, which is available to authorized users. Synechococcus elongatus PCC 7942 to produce 1,3-propanediol  by introduction of a synthetic metabolic pathway [Direct production of chemicals and fuels from carbon dioxide by cyanobacteria is one of the promising technologies to reduce carbon dioxide emissions and the dependence on fossil fuels , 2. Comp pathway . 1,3-PDO pathway . In the  pathway . Althoug pathway  among vaEscherichia coli. For screening of effective candidates for gene manipulation, various strategies have been established for prediction of the effects of a gene knockout, knockdown, and overexpression [Synechocystis sp. PCC 6803. So far, the metabolic models of some cyanobacteria used for bioproduction  have been constructed on the basis of an annotated genome sequence [Synechocystis sp. PCC 6803. FBA was used for validation of this genetic modification to increase theoretical productivity of butanol. Furthermore, some simulations based on FBA have predicted various metabolic modifications (knockout and overexpression) to increase theoretical production by cyanobacteria, for example, of biofuels  and succinic acid by Synechocystis sp. PCC 6803 [Synechococcus sp. PCC 7002 [In silico simulation is an powerful methodology to improve productivity with low experimental costs. The stoichiometric metabolic model not requiring kinetic parameters of metabolic reactions is comparatively applicable to analysis of whole-cell metabolism. Flux balance analysis (FBA) can predict metabolic flux distribution under constraint conditions . FBA usipression . Shastripression  were thesequence –32. AnfePCC 6803 –35 and bPCC 7002 . NonetheS. elongatus PCC 7942 constructed in our previous work [In this study, we applied FBA to prediction of metabolic flux distribution in a 1,3-PDO-producing strain of ous work . The canous work . The posAll the chemicals were purchased from Wako Pure Chemical Industries, Ltd.  unless specified otherwise. Restriction enzymes, phosphatase , ligase , and DNA polymerase  were used for cloning. Oligonucleotides were synthesized by Life Technologies Japan, Ltd. .S. elongatus PCC 7942 . Hereafter, this medium is referred to as the BG11 medium. The composition of the BG11 medium used in this study was the same as previously reported [A modified BG11 medium supplemented with 20 mM HEPES–NaOH (pH 7.5) for pH stabilization was used for cultivation of reported . Antibio730) was measured using Infinite 200 PRO . For determination of dry cell weight (DCW), cells in culture were harvested on the Ominipore™ membrane filter  by filtration under reduced pressure. Cell harvest filters were dried at 65 °C for 12 h. The weight differences before and after cell harvesting were determined as DCW in culture.The incubation conditions for 1,3-PDO production were the same as those described previously . Cell dendhD1, ndhD2, ndhD3, ndhD4, and ndhF1 were T2711–T2712, T3189–T3190, T2723–T2724, T2719–T2720, and T3208–3209, respectively. The primer sets for the downstream region of ndhD1, ndhD2, ndhD3, ndhD4, and ndhF1 were T3187–T3188, T3191–T3192, T2787–T2726, T2721–2722, and T2713–T2714, respectively. The amplified upstream and downstream regions except for ndhF1 were digested by KpnI–HindIII and HindIII–BamHI, respectively, and ligated into KpnI–BamHI sites of pTA703. Only for ndhF1, the upstream and downstream regions were digested with SalI–HindIII and HindIII–BamHI, respectively, and ligated into SalI–BamHI sites of pTA703. A chloramphenicol resistance cassette was amplified from pZA31-luc  by PCR using primers T2128–T2129. The amplified fragment digested with HindIII was ligated into the HindIII site of each plasmid harboring upstream and downstream regions of a target gene, thus creating plasmids pTA1811 (ndhD1 disruption), pTA1812 (ndhD2 disruption), pTA1641 (ndhD3 disruption), pTA1640 (ndhD4 disruption), and pTA1828 (ndhF1 disruption).Plasmids and primers used in this study are listed in Additional file The constructed plasmids  were used for gene disruption in TA1297 (wild-type strain), TA2984 , and TA3800 (glycerol-producing strain). TA2984 and TA3800 were constructed in our previous studies , 38. Theg, 10 min) and filtered using Minisart RC4 . Concentrations of glycerol and 1,3-PDO in filtered samples were determined using a high-performance liquid chromatograph  equipped with an autosampler , a SUGAR SP-G guard column , a SUGAR SP0810 column (Shodex), and a refractive index detector . Operating conditions were as follows: injection volume, 5 µL; mobile phase, MilliQ water; flow rate, 1.0 mL/min in isocratic mode; column temperature, 80 °C.Culture supernatants were centrifuged  as a solver.2/s) and growth light (100 μE/m2/s) provided by a digital LED source  were used for measurement of photosynthetic activity. Photon flux density was measured with IKS-27 . Respiratory activity was similarly measured in complete darkness.Chlorophyll concentrations were measured by extraction in methanol . Oxygen 730 of 1.0) was collected, and ATP was extracted from pelleted cells by resuspension in a 1% (w/v) trichloroacetic acid solution. After centrifugation at 20,000×g for 10 min at 4 °C, the supernatant was neutralized with 1 M Tris–acetate buffer (pH 7.8). The concentration of ATP in the neutralized sample was determined using the ATP determination kit .ATP extraction and measurement were basically performed by the method of Wang et al. . The appndhF1-null 1,3-PDO-producing strain) were measured by LC-QqQ-MS and GC-Q-MS analyses. Sample preparation and analysis were performed by the method described by Soma and Hanai [Intracellular metabolites of TA2984  and TA4058 . The additional information on the introduced synthetic metabolic pathway for 1,3-PDO production  was added to GSM_7942, resulting in a metabolic model of the 1,3-PDO-producing strain composed of 603 genes, 672 reactions, and 536 metabolites .We previously constructed a 1,3-PDO-producing strain, designated as TA2984, by introduction of a synthetic metabolic pathway. A four-step reaction of the introduced pathway converts DHAP, one of the metabolites in the Calvin cycle, into 1,3-PDO via glycerol  was composed of 74 strains that could not satisfy the constraint conditions and lower limit of 1,3-PDO production. The second group (triangles) included 254 strains showing a higher specific production rate of 1,3-PDO, but a null value of the specific growth rate. This may also mean that each gene disrupted in the 254 strains was essential for growth. It was likely that the disruption of any of the 328 genes classified into these two groups was not applicable to improvement of 1,3-PDO productivity. The metabolic flux distributions of the other 267 strains with single-gene disruption could show a certain specific growth rate and higher specific production rate of 1,3-PDO as compared with the experimental data. We screened the 267 candidates for more desirable ones using the product of the specific growth rate and specific production rate as an indicator. Out of the 267 candidates, 231 showed a lower product of the specific growth rate and specific production rate and were classified into the third group (squares).c oxidase were classified into the fourth group. Second, two cases indicating the same metabolic flux distribution were observed in different two-gene disruptions . These genes were annotated as genes encoding an enzyme catalyzing a serial reaction without any blanching pathway. The other six metabolic flux distributions were examined by single-gene disruption. Based on the value of the product of the specific growth rate and specific production, the disruption of 22 genes involved in the NDH-1 complex was found by screening as the most promising candidate for improvement of 1,3-PDO production among the 10 patterns of the fourth group.Finally, 36 genes were classified into the fourth group (circles) as promising candidates to be disrupted for increasing 1,3-PDO productivity  in our experiment. On the other hand, it was reported that four mutants in which each gene encoding the unique subunit in four NDH-1 complexes is disrupted can grow under photoautotrophic and ambient air conditions [NDH-1 complexes in cyanobacteria are involved in several cellular functions  and are subdivided into four variants  by function and subunit composition according to reverse-genetic studies . The phenditions –52. Indenditions . TherefondhB disruption, the gene knockout strains of ndhD1 , ndhD2 , ndhD3 , and ndhD4  based on the 1,3-PDO-producing strain (TA2984) were successfully constructed  showed the highest titers of 1,3-PDO  and glycerol  during 20 days of incubation  did not increase  was the lowest among the four gene knockout strains and TA2984 (Table ndhD4-null strain) showed the highest production rate of 1,3-PDO  produced from DHAP by engineered cyanobacteria [Previously, we have constructed a glycerol-producing strain TA3800) into which only the pathway converting DHAP to glycerol was introduced [00 into wbacteria –20, 38 abacteria , 2,3-butbacteria . AlthougndhF1 successfully increased metabolic flux of DHAP into the 1,3-PDO-producing pathway as expected on the basis of the in silico simulation  and TA2984  were measured. As the result, TA4058 showed lower respiratory activity in the dark and almost the same photosynthetic activity under saturated light conditions (1000 μE/m2/s) as compared with TA2984 , the photosynthetic activity of TA4058 was lower than that of TA2984, being consistent with the growth defect of TA4058  mainly produced by glycolysis and the TCA cycle is used for generating a proton gradient and H2O from O2. The generated proton gradient functions as a driving force of ATP synthase for ATP production. Whole or partial loss of cyclic electron flow and respiratory activity under the influence of the ndhF1 disruption may decrease cellular ATP content.The physiological role of cyclic electron flow is known to increase the ATP/NADPH ratio produced by photosynthetic electron flow . So, nexThe intracellular metabolites involved in a reaction with ATP seemed to reflect the ATP deficiency in TA4058. For example, ATP is consumed in reactions producing RuBP and ADP-glucose, and is produced in the reaction producing pyruvate. In ATP-consuming reactions, substrates (ribulose-5-phosphate: Ru5P and glucose-1-phosphate: G1P) and products (RuBP and ADP-glucose) were detected at high and low concentrations, respectively. Conversely, lower concentration of a substrate (phosphoenolpyruvate: PEP) and higher concentration of a product (pyruvate) were observed in an ATP-producing reaction. In particular, the metabolites around the glycogen synthesis pathway seemed to be affected by the ATP deficiency. Fructose-6-phosphate (F6P) is a metabolite of the branching point for glycogen synthesis from the Calvin cycle and is sequentially converted to glucose-6-phosphate (G6P), glucose-1-phosphate (G1P), and ADP-glucose. In this glycogen synthesis pathway, significant accumulation (more than fivefold) was observed for metabolites except for ADP-glucose produced with ATP consumption. This result suggests that glycogen synthesis is repressed in TA4058 caused by ATP deficiency. Because other studies showed that higher accumulation of glycogen is observed in stationary phase , 59, thendhF1 disruption in the simulation result  in heterotrophic organisms. We hypothesized that the repressed respiratory activity can decrease the metabolic flux in catabolic metabolism generating reducing power in photosynthetic organisms, too. The decreasing of 3-PG-consuming flux into glycolysis by the ult Fig.  was consndhF1 disruption. Hasunuma et al. [Synechocystis sp. PCC 6803 by overexpression of the flv3 gene involved in NADPH consumption. The strategy of acceleration of NADPH consumption also showed the energy balance to change cellular metabolism of cyanobacteria. In the present study, the metabolism changes intended to adapt to the energy imbalance (ATP deficiency) were applied to chemical production . Taking into consideration of the points mentioned above, ndhF1 disruption is a good strategy for enhancement of production of other chemicals  from metabolites in the Calvin cycle in cyanobacteria [Not only greater flux of 3-PG into the Calvin cycle but also repression of glycogen synthesis may increase the productivity values of 1,3-PDO and glycerol. These results indicate that the productivity improvement was caused by the metabolism adapted to energy imbalance induced by the a et al.  successfbacteria , 38, 55.ndhF1-null and wild type strain  from pyruvate than the wild type strain with the same pathway (0.191 g/L). It is difficult to explain clearly the reason why the suggestion based on our simulation result is different from their result, but one possible reason would be the driving force by ATP generation by the reaction from PEP to pyruvate to compensate the ATP deficiency in the ndhF1-null strain.The simulation result of the metabolic flux distribution of the ain Fig.  showed ta et al. , howeverRecently, Broddrick et al.  construcS. elongatus PCC 7942 were successfully improved by gene manipulation based on in silico simulation of gene disruptions. The best candidate for improvement of 1,3-PDO productivity was found to be the elimination of NDH-1 complexes involved in several cellular functions: respiration, cyclic electron flow, and carbon dioxide uptake  was found to produce 1.28-fold more 1,3-PDO  and 2.13-fold more glycerol  than TA2984 did (Fig. ndhF1 disruption may enhance the 3-PG-consuming flux into the Calvin cycle and can be suitable for 1,3-PDO and glycerol production (Additional file In this study, the productivity of 1,3-PDO and glycerol in did Fig. . The metAdditional file 1. Additional tables.Additional file 2. Additional figures.


Klebsiella pneumoniae and identify missing biosynthetic pathways in models of Mycobacterium tuberculosis. This work provides fundamental insights into the essentiality of organic cofactors and has implications for minimal cell studies as well as for modeling genotype-phenotype relations in prokaryotic metabolic networks.The composition of a cell in terms of macromolecular building blocks and other organic molecules underlies the metabolic needs and capabilities of a species. Although some core biomass components such as nucleic acids and proteins are evident for most species, the essentiality of the pool of other organic molecules, especially cofactors and prosthetic groups, is yet unclear. Here we integrate biomass compositions from 71 manually curated genome-scale models, 33 large-scale gene essentiality datasets, enzyme-cofactor association data and a vast array of publications, revealing universally essential cofactors for prokaryotic metabolism and also others that are specific for phylogenetic branches or metabolic modes. Our results revise predictions of essential genes in  •Seventy one biomass equations of manually curated genome-scale metabolic models are compared.•Eight classes of universally essential prokaryotic organic cofactors are proposed.•Conditionally essential organic cofactors are presented and discussed.•Klebsiella pneumoniae are revised.Gene essentiality predictions for •Mycobacterium tuberculosis is predicted.A missing essential pathway in models of  Indeed, it can be used in taxonomical classification  and is iin silico. This is performed through a growth reaction wherein necessary constituents are combined in stoichiometric amounts producing new biomass. Maximization of the flux through this reaction, the so-called Biomass Objective Function (BOF), is the most commonly used method for simulating growth phenotypes through the Flux Balance Analysis methodology  and multi-species comparative assays in determining  which components make up the cell's biomass, in contrast with the advanced picture in terms of the elemental composition . Genome-hodology .The BOF can be formulated as a direct biosynthesis from precursor metabolites ; biosyntThe utility of metabolic models is tied to the accuracy of the biomass composition used . Yet, mo22.1Bacillus subtilis and Escherichia coli), as shown in E. coli built in 2003. Another is the cluster that includes all the three models of the genus Methanosarcina, built after the first model, iAF692. However, the majority of models group independent of their phylogenetic relatedness. Most of the clusters are thus artifacts rather than representing underlying biology. For example, the node on the bottom of the tree  represents the three BOFs with the most unique compounds among all compounds represented in the 71 BOFs analyzed.We first extensively assessed biomass compositions in published prokaryotic GEMs. In total, 71 detailed biomass compositions were gathered, covering 9 phyla with 5 classes of Proteobacteria and one phylum of Archaea (Supplementary Dataset 1). To enable comparison across different models, we reconciled diverse nomenclatures and representation styles, ranging from lumped stoichiometry to reaction-level inclusion. This exercise resulted in 551 unique metabolites (nomenclature as per BiGG database ) that arE. coli in 2011  some which are represented in a high level form, tagged with the species’ initials (phosphoethanolamine is named pe_CB in C. beijerinckii and pe_HP in H. pylori). We also identified gaps, for example in the network of C. beijerinckii regarding the production of Spermine, Spermidine, Glutathione, Dimethylbenzimidazole and Adenosylcobalamin. Even under the rich media conditions used   was usedused see , whereinused see b attestiSynechocystis sp., between 29.4% and 32.8% of essential reactions were different when using an alternative biomass composition (Klebsiella pneumoniae) with the BOF of iAF1260 (E. coli), wherein 82 (67.7%) of the predictions were new essentials. The BOF of iAF1260 brings 19 new components that iYL1228 can produce    , the median presence of a gene increases to 50%  are annotated to functions related with biosynthesis of cofactors and prosthetic groups c. Moreovs to 50% d. This i2.4The true-positive  rate of cofactor-related essential genes of iYL1228 in γ-Proteobacteria when using the biomass composition of iAF1260 indicates organic cofactors as crucial but also missing biomass components in prokaryotic GEMs. To close this gap, we set out to identify universally essential cofactors (or classes thereof) for prokaryotes that will improve accuracy and comparability of GEMs. For this, we integrated multiple large-scale datasets a. The co2.5Mycobacterium tuberculosis iNJ661v , only one form could be included for the sake of simplicity. When a class of cofactors includes active and non-active interconvertible forms, the active forms should be preferred. A simple example case is the representation of flavins: FAD and FMN are the preferred active forms to be included in the BOF, oppositely to riboflavin, the non-active precursor. More details on the different approaches on modeling biomass compositions are discussed in the Supplementary Discussion.in vivo) predictions.The comparison of gene essentiality in different species has some limitations. These can occur due to biological causes, as the alternative presence of isozymes and transporters that introduce redundancy in different networks, and different media conditions where the large-scale assays were performed. Other limitations can occur due to errors and incompleteness in databases, which we examine further in the Supplementary Discussion. By overlapping several levels of evidence in the prediction of essential cofactors  our apprWe here propose a standardized and detailed core biomass composition for prokaryotes . This is a conservative proposal and thus includes only the three most prevalent lipid components as representative species , and these should be adapted according to the species being modeled. Our proposal excludes other non-universal macromolecules such as cell wall peptidoglycans (more details can be found in the Supplementary Discussion). We further suggest that the pipeline used here a can be The cofactors here identified as universally essential play fundamental roles in biochemistry. In most cases, they are related with the transfer of small units: hydride groups for NAD(P)(H), methyl groups for SAM, electrons for FAD and FMN, acyl groups for CoA and one-carbon units in C1 carriers. The two special cases of P5P and THMPP correspond to direct intervention in catalysis, stabilizing intermediate metabolites and assisting in the formation of new chemical bonds, respectively. Our classification of universally essential is conservative, excluding cofactors for which we found minor exceptions in the data analyzed, e.g. biotin  component is included in the BOF, it implies that this component needs to be provided, either through the biosynthetic pathway or via transport reactions. The construction of more complete and standardized BOFs will thus have a great impact not only in the predictions of essential genes but also in the construction of minimal media required for growth. Both applications are of utmost importance for identifying metabolic vulnerabilities of pathogens, being in fact the most common motivations to construct GEMs for those organisms.Overall, this work lays foundations for improving the definition of biomass composition in the current and future metabolic reconstructions – an important step towards biochemically more accurate models with higher predictive power. Moreover, it is the first large-scale systematization of essential metabolic organic cofactors for prokaryotes, which we hope will be useful for several fundamental and applied studies.44.1We searched for manually-curated GEMs of prokaryotes in four major online databases: BiGG , MetRxn The ModelSEED template for universal biomass components was obtained from the original publication .4.2Hierarchical clustering was performed using ‘pvclust’ R package  with bin4.3We chose five different GEMs by sampling high and low phylogenetic dissimilarity pairs in order to assess the impact of BOFs in predictions of essentiality a.4.4Simulations of maximum growth rates for single-deletions of reactions and genes were performed using Flux Balance Analysis (FBA) . All mod4.5Searches in DEG  were per4.6We extracted all enzyme-cofactor association data for prokaryotes using the Python SOAP access methods for BRENDA . Biosynt4.7All changes described in the main text were performed manually on the original SBML file for iNJ661v. To simulate Middlebrook media as used in the genome-scale experimental assay for validation of the predictions , new tra


The IMGMD database is freely available, without any registration requirements, at http://imgmd.jiangnan.edu.cn/database.Genome-scale metabolic models (GSMMs) constitute a platform that combines genome sequences and detailed biochemical information to quantify microbial physiology at the system level. To improve the unity, integrity, correctness, and format of data in published GSMMs, a consensus IMGMD database was built in the LAMP (Linux + Apache + MySQL + PHP) system by integrating and standardizing 328 GSMMs constructed for 139 microorganisms. The IMGMD database can help microbial researchers download manually curated GSMMs, rapidly reconstruct standard GSMMs, design pathways, and identify metabolic targets for strategies on strain improvement. Moreover, the IMGMD database facilitates the integration of wet-lab and  Since the first GSMM of Haemophilus influenzae Rd was constructed in 19994, more than 300 GSMMs for over 100 organisms have been built6.Genome-scale metabolic models (GSMMs) are a kind of a mathematical model that integrates multiple types of omics data, such as genomics, transcriptomics, proteomics, and metabolomics. GSMMs can clarify the relations among genes, proteins, and reactions. Models can be used to describe all biochemical reactions, metabolites, and genes involved in the metabolism of a specific organism. They have been used to decipher metabolic, regulatory, and signaling networks at the whole-organism level9. Nonetheless, owing to differences in nomenclature10, integrity, and correctness11 as well as the format12 of published GSMMs, these models cannot be directly applied by other researchers. To reduce the manual labour needed for model construction and to increase the quality of GSMMs, some databases and tools, such as BIGG Models11 and MetaNetX13, have been developed. Nonetheless, they are limited by the quality and quantity of models. For example, BIGG Models includes only 80 high-quality GSMMs (as of 30 November 2016), which is far from the number of published models. In MetaNetX, only 24 (14.7%) models have been published, which were validated by experimental results (http://www.metanetx.org/cgi-bin/mnxweb/repository).GSMMs have been developed for over 15 years, and four steps are involved in their construction: creation of a draft model, manual refinement, conversion to a mathematical format, and network evaluationIn silico Microbial Genome-scale Metabolic Models (IMGMD) in the LAMP (Linux + Apache + MySQL + PHP) system. It provides a platform for integration and standardisation of all published microbial GSMMs. In IMGMD, users are not only able to browse and download standardised GSMMs but can also reconstruct GSMMs automatically. In addition to pathway mining and mutation library functions, users can access information that can guide pathway design and metabolic target identification.In this study, we built a database named in silico and in vivo metabolic engineering results, and accordingly, it provides guidance for target searches  has a user-friendly website for the following applications: (1) It can be used to download standardised GSMMs; this module integrates model-related information, such as gene–protein–reaction relations, genome information, and references Fig. . (2) It hes Fig. .Figure 1All web interfaces of the IMGMD database were tested in various browsers, such as Google Chrome, Mozilla Firefox, Internet Explorer, Opera, and Safari on Windows or Linux platforms. Despite minor differences in appearance, all tools functioned normally in all the tested browsers and on all platforms. Among the browsers tested, Google Chrome and Mozilla Firefox provided the best user experience. Hence, we recommend that users access the database using one of these two browsers.Saccharomyces cerevisiae as an example to demonstrate the use of ‘Search for organism’. All 8 S. cerevisiae models are returned. Then, by clicking on ‘Saccharomyces cerevisiae S288c’ for model iND750, a user can find detailed information about the organism , model , and reference , PSORTb23 was employed to predict protein subcellular localisation.Five steps are needed to construct a model in IMGMD: (1) choosing three models for reference; (2) uploading the genome sequence; 3) choosing a threshold ; (4) entering an e-mail address to receive results ; (5) submitting the job to the IMGMD database. Once the job is complete, the results contain three parts, including the model, transport proteins, and prediction of protein subcellular localisation  is a Web service that includes the RAST genome annotation tool. Based on the annotation results, a model for a specific organism can be reconstructed automatically. Given that the RAST service (http://rast.nmpdr.org/rast.cgi) can annotate only prokaryotes, ModelSEED has limited applicability to eukaryotes. Besides, model construction by ModelSEED will take a long time, according to the job numbers. IMGMD is also a web platform that serves for model construction. It is based on the results of genome homologous alignment. Users can upload a target organism’s genome sequence and choose relevant parameters. After submission of the job to IMGMD, results will be returned within 1 day. Nonetheless, a model constructed by IMGMD is a draft model. It still needs to be further processed to obtain a GSMM. The COBRA Toolbox is based on the Matlab platform, which is commonly used for model construction. The COBRA Toolbox requires users to have basic Matlab knowledge and an advanced computer configuration for model analysis , other pathways also could generate pyruvate. On the web page of pathway-mining results, information about the substrate and production can be linked to some metabolic databases, like KEGG29, ModelSEED24, ChEBI30, and PubChem31. Besides, on the page of detailed pathway information, reactions participating in a pathway are shown, including Reaction ID, Formula, Genes, Subsystem, and EC numbers 32 and Methanosarcina barkeri (iMG746)33, there were 8 and 12 pathways from glucose to pyruvate, respectively. (3) Pathways that generate highly valuable products may exist in typical organisms. To mine these potential pathways, users can choose all collected models for the search, and then choose reactions in which species and corresponding genes can serve as references for a target strain to guide strain design. Considering these three levels, the function of pathway mining may be useful in synthetic biology and systems metabolic engineering.In this module, users can explore metabolic pathways at three levels. (1) According to the input metabolites as substrates and products, total pathways from a substrate to product in a GSMM can be output. For example, in the 35. In IMGMD, a library that combines in vivo and in silico results to guide metabolic engineering was created.The pathway prediction tool enables new pathway design for metabolic engineering; additionally, the mutation library function can be used for optimisation of the host strain. It can help to identify targets that couple cell growth with product formation, e.g., targets for gene upregulation, downregulation, and gene deletioniAF1260, 217 results can be found. The effect of a knockout of b4025, which encodes glucosephosphate isomerase  in E. coli, the growth rate and production rate can be viewed on another webpage  were related to various knockout strategies, involving different algorithms, such as OptKnockhttp://systemsbiology.ucsd.edu/InSilicoOrganisms/OtherOrganisms, http://synbio.tju.edu.cn/GSMNDB/gsmndb.htm) and by scrutinizing the primary scientific literature , Microsoft Excel, Microsoft Word, or PDF. To read these models using the COBRA Toolbox, models in all formats were rewritten in the Excel format. Word and PDF files were manually transformed into Excel files. The SBML models were transformed into Excel, using the COBRA Toolbox on the Matlab platform. Nonetheless, during this process, some models written in SBML could not be read using the COBRA Toolbox. Eventually, 265 of total 328 (80.8%) published models were standardised in the Excel and SBML formats and can be downloaded from our database.E. coli model iAF126043, pyruvate was represented as pyr. In Saccharomyces cerevisiae model Yeast 1.044, and Yarrowia lipolytica model iNL89545, it was indicated by PYR and s_1277, respectively. In the IMGMD database, according to their unique IDs in various biochemical databases , the metabolites from different models were unified using IMGMD metabolite IDs. Then, 8367 metabolites from these different models were integrated. Additionally, 77.65% of metabolites can be linked to at least one of these databases  were collected by referring to information such as EC numbers, reaction descriptions, and formulas, in the other columns for the models. Finally, 17 of 19 (89.5%) GSMMs were filled out, except for models of Streptomyces lividans (GMD-TK24) and Pseudomonas putida (PpuMBEL1071). For the Formula column, metabolites were first replaced with the unified IMGMD metabolite IDs. For example, the reaction catalysed by alcohol dehydrogenase (EC: 1.1.1.1) was unified as ‘M00442[c] + M00003[c] 〈=〉 M00083[c] + M00079[c] + M00004[c]’. In all models, to arrange metabolites on the left and right of ‘〈=〉 ’ in order, a MATLAB script developed in our lab was used. Lastly, the reaction was unified as ‘M00003[c] + M00442[c] 〈=〉 M00004[c] + M00079[c] + M00083[c]’. If we ignore the cell compartments and do not count transport and exchange reactions, the database contains 21436 reactions.A list of reactions, including the Gene–Protein–Reaction relations for the models, should contain 15 columns of information, e.g., a reaction description, formula, and genesin vivo or in silico production, and in vivo or in silico growth rate is collected. Additionally, amino acid and nucleic acid sequences of related genes collected from the KEGG database are also stored in this Excel file.During the process of literature mining, mutation information is stored in an Excel file. Information such as organisms, models, genes, operations, All processed data are stored in a MySQL database and are available through a Web server built in the standard LAMP (Linux + Apache + MySQL + PHP) system to provide fast and secure data access. XAMPP for Linux 5.6.15 (https://www.apachefriends.org/index.html) was installed on CentOS Linux 5.8 (https://www.centos.org/download/). BLAST 2.2.28 (ftp://ftp.ncbi.nlm.nih.gov/blast/executables/release) and Python 2.7.11 (https://www.python.org/) were used for model auto-reconstruction, and a C++ script based on a depth optimisation algorithm was written to explore the metabolic pathways in a particular GSMM.The IMGMD database (http://imgmd.jiangnan.edu.cn/database) provides a platform that integrates the names of metabolites and metabolic reactions from common biochemical databases and existing model repositories. This database includes 328 models for 139 microorganisms and provides 265 standardised models for downloading. Based on a homologous sequence alignment method, models can be reconstructed automatically in the IMGMD database, which can accelerate the process of model construction. Furthermore, IMGMD provides a pathway mining tool for pathway design and a mutation library for strain optimisation.Compared with other GSMM databases, the IMGMD database is specific for microorganisms. It is user-friendly and feature-rich; accordingly, the scientific community can easily use and extend the knowledge base. Thus, IMGMD will be a useful database for the design phase of systems metabolic engineering. Future developments include integration of the COBRA Toolbox, which will allow users to directly simulate gene deletion or over-expression, on the IMGMD platform. Besides, the IMGMD database is maintained by our lab and will be updated annually, to keep pace with the advances of GSMMs.


Photorhabdus luminescens—the source of modern Lux reporters—while literature data has been used for LuxEC. Importantly, the data show clear evidence for previously unreported product inhibition for the LuxAB reaction. Model simulations show that predicted bioluminescent profiles can be very different from changes in gene expression, with transient peaks of light output, very similar to light output seen in some experimental data sets. By incorporating the calibrated model into a Bayesian inference scheme, we can reverse engineer promoter activity from the bioluminescence. We show examples where a decrease in bioluminescence would be better interpreted as a switching off of the promoter, or where an increase in bioluminescence would be better interpreted as a longer period of gene expression. This approach could benefit all users of Lux technology.The bacterial Lux system is used as a gene expression reporter. It is fast, sensitive and non-destructive, enabling high frequency measurements. Originally developed for bacterial cells, it has also been adapted for eukaryotic cells, and can be used for whole cell biosensors, or in real time with live animals without the need for euthanasia. However, correct interpretation of bioluminescent data is limited: the bioluminescence is different from gene expression because of nonlinear molecular and enzyme dynamics of the Lux system. We have developed a computational approach that, for the first time, allows users of Lux assays to infer gene transcription levels from the light output. This approach is based upon a new mathematical model for Lux activity, that includes the actions of LuxAB, LuxEC and Fre, with improved mechanisms for all reactions, as well as synthesis and turn-over of Lux proteins. The model is calibrated with new experimental data for the LuxAB and Fre reactions from  Bioluminescent reporters are used in many areas of biology as fast, sensitive and non-destructive measures of gene expression. They have been developed for bacteria, adapted now for other kinds of organisms, and recently been used for whole cell biosensors, and for real-time live animal models for infection without the need for euthanasia. However, users of Lux technologies rely on the light output being similar to the gene expression they wish to measure. We show that this is not the case. Rather, there is a nonlinear relationship between the two: light output can be misleading and so limits the way that such data can be interpreted. We have developed a new computational method that, for the first time, allows users of Lux reporters to infer accurate gene transcription levels from bioluminescent data. We show examples where a small decrease in light would be better interpreted as promoter being switched off, or where an increase in light would be better interpreted as promoter activity for a longer time. These genes, including their order (luxCDABE), are conserved in all lux systems of bioluminescent bacteria. An additional gene (luxF), with homology to luxA and luxB, is located between luxB and luxE in some species. The light emitting reaction, catalysed by the LuxAB complex, involves the oxidation of FMNH2 and the conversion of a long chain fatty aldehyde  to its cognate acid, with the emission of blue-green light. LuxC, D and E together form the fatty acid reductase complex, involved in a series of reactions that recycle the fatty acid back to aldehyde. In E. coli and other species, Fre has been shown to be the enzyme responsible for flavin reduction back to FMNH2.The lux operon contains genes for the bacterial bioluminescent reaction , 2: luxAGene expression can be measured by cloning a promoter of interest upstream of the lux operon, and interpreting the bioluminescence from bacteria containing such constructs as a measure of transcription , 4. ThisHowever, this light is an integrated signal of transcription, mRNA half-life, translation and protein turn-over, the bioluminescence reaction kinetics and substrate availability and cycling. As a consequence, absolute transcription activity cannot be directly inferred from the data generated. This is a limitation in the current use of Lux technologies. For example, some studies using Lux reporters have observed fluctuations in light output –16, and Photorhabdus luminescens LuxAB that we have purified. An important part of our approach is to fit the models directly to the time series experimental data, which can be thought of as a  factorial experiment with varying concentrations of both FMN and NADPH. This allows for the simultaneous inference of all parameters in complex kinetic models, which is an advantage over traditional chemical kinetics techniques that only use the maximal velocity.In this paper we show that detailed mathematical models for bioluminescence can be used to relate bioluminescence to promoter activity. We developed a new model that addresses limitations of previous work , whose s2 as an energy source. This is an energetically transient species with a short half-life. In vivo it is supplied to the luciferase complex for immediate consumption by the redox activity of the Fre enzyme, which converts NADPH and FMN to FMNH2 and NADP+. We measured the kinetics of the Fre reaction ; in all cases, the initial NADPH concentration was 200μM. The initial velocity of the reaction increased with increased FMN, leading to different kinetics in the three curves, with the steady state being reached more rapidly with increased FMN concentration.The luciferase reaction uses FMNHreaction  by recorμM, 10μM and 100μM  kinetic reactions, we combined components of the coupled Fre-LuxAB reaction. We measured light output arising from different initial concentrations of FMN: 10nM, 100nM, 1nd 100μM . For allto 400uM . Productth LuxAB , with onThe new mathematical model includes three chemical reactions: the Fre reaction for flavin recycling; the LuxAB reaction for light production; and the LuxEC reaction for aldehyde recycling. It also contains a further equation to describe the turnover dynamics of the Lux proteins:R0 is constant and ρ0 = τR0. Control of the bioluminescent reactions is assumed to lie with the most rapidly turned-over protein. This is modelled by setting P(t) to represent all of the Lux proteins and using the same rate of synthesis and turn-over for all proteins. The protein production T(t) encompasses transcription, translation and mRNA degradation and is supplied as a model input; it is assumed that the lux mRNA is at quasi steady state. The value of the protein turn-over rate γL has been inferred from Lux data as 0.378h−1  over the model fit previously reported  overnight cultures were grown aerobically at 37°C in in Tris Minimal Succinate medium  for plasmid maintenance Bacterial pellets were washed once in TMS without sodium succinate (TM) and resuspended in an equal volume of TM. These were diluted 1/50 into fresh TM supplemented with filter sterilized sugars (Glucose or glucose-6-phosphate) supplemented with Erythromycin (5μgml−1).The promoter of the Universal hexose transporter (rminator  using a N4220 pSB09 overniμl) were placed into the wells of a 96-clear-bottom microtiter plate (Porvair) and incubated at 37°C in a Tecan Genesis Pro microplate reader. Optical density (600nm), fluorescence (485ex/510em) and bioluminescence (RLU) readings were taken at 30 min periods over the course of the experiment.For growth and reporter gene measurements, replicate samples  decreases while cells are still in exponential phase (corresponding OD measurements). We estimate the turn-over rate for each such curve  by fitting a linear line to the log transformed data. The histogram of estimated values are shown in In order to estimate the kinetic parameters for all three reactions, we used a similar Bayesian approach based on Markov Chain Monte Carlo (MCMC), in which a sub-model relevant to each reaction is used along-with corresponding experimental data. We used an adaptive version of Metropolis-Hasting MCMC algorithm with global scaling  in orderλ = 100) for kinetic parameters, while more informative gamma priors ) for degradation rates. This reflects our a priori knowledge that the kinetic parameters (ratio of kinetic rate constants) are all positive real numbers. For those parameters which are common to the inference in both the Fre and LuxAB models, prior estimates for the LuxAB inference were generated by fitting exponential distributions to the posterior estimates derived from the the Fre inference.Unless otherwise specified, we used un-informative exponential priors (θ), we simulate the species of interest (stored in vector Y′), and use the corresponding experimental data in vector Y to give the likelihood function:We define the likelihood of the parameters, for any of the models using in τ, in case of Fre and LuxEC inference, while for LuxAB data, we estimated the noise variance from the replicates. Details on the derivation of the Gibbs step is provided in A separate Gibbs step is introduced for the sampling of noise precision n is an exponential distribution with mean value equal to the current height at the previous point n−1. At each step, a point is chosen at random, and a new height is proposed, as described in Green 1995 [A Monte Carlo approach was used to infer promoter activity from light readout. The promoter input function is modelled as a series of K heights at fixed positions. A Martingale prior distribution is used , so thateen 1995 . The likS1 Text(PDF)Click here for additional data file.


Moreover, similar amounts of different enzymes might incur different costs for the cell, depending on enzyme-specific properties such as protein size and half-life. Here, we developed enzyme cost minimization (ECM), a scalable method for computing enzyme amounts that support a given metabolic flux at a minimal protein cost. The complex interplay of enzyme and metabolite concentrations, e.g. through thermodynamic driving forces and enzyme saturation, would make it hard to solve this optimization problem directly. By treating enzyme cost as a function of metabolite levels, we formulated ECM as a numerically tractable, convex optimization problem. Its tiered approach allows for building models at different levels of detail, depending on the amount of available data. Validating our method with measured metabolite and protein levels in E. coli central metabolism, we found typical prediction fold errors of 4.1 and 2.6, respectively, for the two kinds of data. This result from the cost-optimized metabolic state is significantly better than randomly sampled metabolite profiles, supporting the hypothesis that enzyme cost is important for the fitness of E. coli. ECM can be used to predict enzyme levels and protein cost in natural and engineered pathways, and could be a valuable computational tool to assist metabolic engineering projects. Furthermore, it establishes a direct connection between protein cost and thermodynamics, and provides a physically plausible and computationally tractable way to include enzyme kinetics into constraint-based metabolic models, where kinetics have usually been ignored or oversimplified.Bacterial growth depends crucially on metabolic fluxes, which are limited by the cell’s capacity to maintain metabolic enzymes. The necessary enzyme amount per unit flux is a major determinant of metabolic strategies both in evolution and bioengineering. It depends on enzyme parameters (such as  E. coli. This method could be used to quantify the enzyme cost of many other pathways and explain why evolution has selected some low-yield metabolic strategies, including aerobic fermentation in yeast and cancer cells. Furthermore, future metabolic engineering projects could benefit from our method by choosing pathways that reduce the total amount of enzyme required for the synthesis of a value-added product.“Enzyme cost”, the amount of protein needed for a given metabolic flux, is crucial for the metabolic choices cells have to make. However, due to the technical limitations of linear optimization methods, this cost has traditionally been ignored by constraint-based metabolic models such as Flux Balance Analysis. On the other hand, more detailed kinetic models which use ordinary differential equations to simulate fluxes for different choices of enzyme allocation, are computationally demanding and not scalable enough. In this work, we developed a method which utilizes the full kinetic model to predict steady-state enzyme costs, using a scalable and robust algorithm based on convex optimization. We show that the minimization of enzyme cost is a meaningful optimality principle by comparing our predictions to measured enzyme and metabolite levels in exponentially growing  PLOS Computational Biology Methods paper.This is a E. coli, efforts to exhaustively map metabolic networks are only nearing completion on the stoichiometric level. Our understanding of metabolic fluxes, their dynamic regulation and their connection to cell fitness is far from perfect /[A] < Keq where the reaction’s equilibrium constant Keq is determined by the Haldane relationship, B]/[A] < 1, i.e., ln[B] − ln[A] < 0, putting a linear boundary on the feasible region (B]/[A] ≈ Keq), the enzyme demand E2 approaches infinity. Beyond the boundary ([B]/[A] > Keq) no positive flux can be achieved (grey region). Such a threshold exists for each reaction , i.e., the driving force is positive whenever Q < Keq, and it vanishes if Q = Keq. How is this force related to enzyme cost? A reaction’s net flux is given by the difference v = v+ − v− of forward and backward fluxes, and the ratio v+/v− depends on the driving force as v+/v− = eΘ. Thus, only a fraction v/v+ = 1 − e−Θ of the forward flux acts as a net flux, while the remaining forward flux is canceled by the backward flux  and allosteric regulation. Thus, one must model these factors using the available kinetic information [E, forward catalytic constant −1), driving force , and on kinetic effects such as substrate saturation or allosteric regulation. If all active fluxes are positive, reversible rate laws like the Michaelis-Menten kinetics in The prediction of optimal metabolite and enzyme levels can be extended to models with general rate laws and complex network structures. In general, enzyme demand depends not only on driving forces and ormation , 40, or ormation , 42. Theηrev and ηkin  to emphasize this fact. The cost function is defined on the metabolite polytope To determine the enzyme demand of an entire pathway, we sum over all reactions: ld cause , 45. Thuq reflects a trade-off between fluxes to be realized and enzyme expression to be minimized, where the relation between fluxes and enzyme levels is not fixed, but depends on metabolite log-concentrations x. Wherever trade-offs exists in biology, it is common to assume that evolution converges to Pareto-optimal solutions [The cost function olutions , i.e. caenzyme cost minimization (ECM), is a convex optimization problem and can be solved with local optimizers. Enzyme demand and enzyme cost functions, for single reactions or pathways, are differentiable, convex functions on the metabolite polytope. This convexity holds for a variety of rate laws, including rate laws describing polymerization reactions [q(x) has a broad optimum as in KM values), can thus be handled in two ways: either, by using a simplified EMC function that does not require this parameter, or by relying on parameter values chosen by the workflow.The resulting method, which we call eactions , and eveη ≤ 1, simplified enzyme cost functions with fewer parameters can be obtained. For example, ηrev = 1 would imply an infinite driving force Θ → ∞ and a vanishing backward flux, ηkin = 1 implies full substrate saturation, as well as full allosteric activation and no allosteric inhibition . In these limiting cases, enzyme activity will not be reduced, and enzyme demand will be given by the capacity-based estimate Beyond minimizing the total enzyme cost, one can also use ECM to analyze the individual enzyme demands. When the metabolite levels are known, the demand can be directly calculated and each efficiency factor in kcat values, equilibrium constants, or even KM values), one may choose between different cost functions with different data requirements: EMC0 , EMC1 , EMC2 , EMC3 , and EMC4 functions . Details of the simplified EMC functions are given in Depending on the data available  and equilibrium constants, which need to be known. This factor approaches infinity as reactions reach equilibrium (i.e. where Θl → 0), which is what forces reactions away from equilibrium during cost minimization −1  is that they are based on reaction , 49 and n.ac.il/ . As in tlike MDF  and mTOWlike MDF  have beeηkin = 1. Just like the reversibility factor ηrev, the kinetic factors ηsat and ηreg can be used to define tighter constraints on metabolite levels. However, unlike ηrev, the kinetic terms may take various forms and contain many kinetic parameters. To obtain simple, but reasonable formulae in EMC3, we first consider rate laws in which enzyme molecules exist only in three possible states: unbound, bound to all substrate molecules, or bound to all product molecules. Metabolites affect the rate only through the mass-action terms S = ∏i(si/KMi) (for substrates) and P = ∏jpi/KMj (for products), and the degree of saturation is determined by ηsat = S/(1 + S + P), where the formula effectively has two Michaelis-Menten constants: one for substrates and one for products . EMC3 represents a balance between complexity and requirement for kinetic parameters, and is a practical cost function if simple, realistic rate laws are desired. The EMC4 functions, finally, represent general rate laws and ηkin can take many different forms depending on mechanism and order of enzyme-substrate binding. Again, for simplicity, we resort to analyzing only a small set of relatively general templates for EMC4, known as convenience kinetics [The next logical step is to relax the assumption that kinetics  or modulkinetics . NeverthE. coli central metabolism, containing three major pathways: glycolysis, the pentose phosphate pathway, and the TCA cycle (see −15 liters) [10-scale) corresponds to a typical fold error of 10RMSE = 2.6. In line with the measured protein levels, the predicted enzyme levels tend to be larger in glycolysis than in TCA and pentose phosphate pathway, reflecting the larger fluxes and less-favorable thermodynamics. All predictions including metabolite concentrations, thermodynamic forces and c/KM ratios can be found online at the accompanying website www.metabolic-economics.de/enzyme-cost-minimization/.To benchmark our optimality-based prediction of metabolite, we applied ECM to a model of  liters) . EMC4 prWe note that predicted enzyme levels become more accurate as more complex cost functions are used, with a prediction error decreasing monotonically from 1.35 with EMC0 to 0.42 with EMC4. The capacity-based enzyme cost (EMC1) assumes that enzymes operate at full capacity . Taking all of these aspects into account would create very detailed models but at the price of considerably increasing the demand for data. Here, we obtained a flexible modeling method by combining the two main modeling approaches, constraint-based and kinetic modeling, in a new way: with fixed metabolic fluxes, kinetic models are used to determine a cost-optimal state. The tiered approach in ECM allows for different levels of detail, which can easily be matched to the amount of existing data. The minimal requirement for running ECM is to have a metabolic network with given steady-state fluxes, while the maximal requirement would be a fully parameterized kinetic model. The method applies to individual metabolic pathways and, theoretically, entire metabolic networks. No matter if we model exponentially growing cells, microbial cells in stationary phase, or non-growing eukaryotic cells, the sum of enzyme costs per unit flux is a meaningful objective for pathways used by the cell. Although similar approaches exist in dynamic modeling , 56 and 1. Solving the enzyme optimality problem in metabolite space One way of modeling the cost and benefit of enzymes is to study kinetic models and to treat enzyme levels as free variables to be optimized. However, this calculation can be hard because enzyme profiles may lead to one, several, or no steady states, and the resulting optimality problem can be non-convex. By fixing fluxes and using metabolite concentrations as our primary variables, we drastically simplify this optimization problem. Flux directions and the second law of thermodynamics impose constraints that define a set of feasible metabolite profiles, the metabolite polytope. This polytope is used here as a space for screening, sampling, and optimizing metabolic states; accordingly bounds on metabolite concentrations or driving forces can be easily formulated as linear constraints. Using log-concentrations as free variables, and given a (steady and non-steady) flux distribution, we can parametrize the set of metabolic states very easily: we simply consider all feasible metabolite profiles and compute, for each of them, the corresponding enzyme profile by taking the inverse rate laws. With enzyme levels as free variables, parameterizing the set of metabolic states would be much more complicated.2. Convexity The metabolite polytope not only provides a good search space, but it also facilitates optimization because enzyme cost is a convex function of the metabolite log-concentrations , it wou3. Separable rate laws disentangle individual enzyme cost effects To assess how different physical factors shape metabolic states, we focused on separable rate laws, which lead to a series of easily interpretable, convex cost functions. The terms in these functions represent specific physical factors and require different kinetic and thermodynamic data for their calculation. By neglecting some of the terms, one obtains different approximations of the true enzyme cost. The more terms are considered, the more precise our predictions about metabolic states becomes . Fo for MDF , pathway for MDF , and Con for MDF . Thermod for MDF , 57, 58 5. Kinetics-based flux cost functions for flux balance analysis Accordingly, results from ECM can be used to improve flux analysis [kcat values and enzyme sizes) could be adjusted by dividing them by efficiency factors obtained from our workflow. In FBA , flux cost or enzyme demand are linear functions of the fluxes. Enzyme Cost Minimization allows us to compute plausible prefactors for this formula from detailed knowledge of enzyme kinetics: by rearranging ηrev = ηkin = 1 and that cost is expressed in terms of protein mass in Daltons Apw = vpw/q.analysis , 23 by d6. Embedding ECM into flux analysis Furthermore, ECM could be “embedded” into FBA by screening a finite set of possible flux distributions, characterizing each of them by quantitative cost (using ECM) and choosing the most cost-favorable mode. Since we now know that any metabolic state that has maximal specific rate is an elementary flux mode [lux mode , it woulLactococcus lactis, which uses the exact same enzyme expression profile for completely different anaerobic growth modes [Lactococcus lactis experiments. This stands in contrast to aerobic fermentation in E. coli, which seems to be explained well by predictable shifts in protein allocation [ECM is based on the central assumptions that the metabolic states of cells are cost-optimized and that cost arises from cellular protein levels. Both assumptions are of course debatable. There is ample evidence that cells assume apparently sub-optimal states in order to maintain robust homeostasis or to gain metabolic flexibility for addressing future challenges . For exath modes : slow grlocation .minimal protein investment that would be required to support a certain metabolic state. In metabolic engineering, such predicted investments may be used to rule out potential, but uneconomical metabolic pathways. On the other hand, ECM can be used as a background model to be compared to more complicated optimality-based cell models. Such comparisons can allow us to quantify the impact of other fitness objectives in units of “protein cost”, to learn which objectives can best explain cellular behavior, and to describe non-optimality as a deviation from a presumable cost-optimal state.As these examples show us, the importance that certain cells attribute to saving on protein costs is highly variable and, in some cases, can be negligible: for instance, when protein levels are already low or when protein demands change quickly and unpredictably. Moreover, random fluctuations in protein levels will be tolerable as long as the impact on fitness is not very high. Nevertheless, we think that a simple principle of cost optimality as in ECM can be a useful heuristics. On the one hand, it can reveal the h(E)). In particular, metabolite levels may be under additional constraints or optimality pressures because they appear in pathways outside our model, which may favor high or low levels of the metabolites. Also chemical molecule properties, such as hydrophobicity or charge, may affect the preferable metabolite levels in cells [b(c)(x) that penalize low ATP levels  and sufficient substrate levels (for saturation efficiency). Trade-offs between these requirements, together with predefined bounds, will shape the optimal metabolite profiles [kcat values, we may expect a strong pressure on sufficient driving forces and substrate levels.profiles : in a li3. Enzyme profiles reflect global effects of enzyme usage If enzyme profiles follow a cost-benefit principle, costly enzymes should provide large benefits. Such a correspondence has been predicted, for example, from kinetic models in which flux is maximized at a fixed total enzyme investment [vestment : in optiBeyond the analysis of central metabolism, ECM can be applied to select candidate pathways in metabolic engineering projects. A prediction of enzyme demands or specific activities  or non-stationary (e.g. from dynamic 13C labeling experiments [l ⋅ vl > 0), and thus l = 0). Close to these faces, enzyme cost goes to infinity.A metabolic network with given flux directions, equilibrium constants, and metabolite bounds defines the eriments ). If theE, its forward catalytic constants x. While the enzyme level and are concentration-dependent, unitless, and can vary between 0 and 1. The reversibility factor ηrev depends on the driving force , and the equilibrium constant is required for its calculation. The saturation factor ηsat depends directly on metabolite levels and contains the KM values as parameters. Allosteric regulation yields additive or multiplicative terms in the rate law denominator, which in our example can be captured by a separate factor ηreg. In general, ηsat and ηreg can be combined into one kinetic factor ηkin, as depicted in According to  factors . In Fig v, and contains the terms from the rate law in inverse form multiplied by the enzyme burden hE. The left-hand part of the equation, ηkin can be split into 1/ηsat ⋅ 1/ηreg. By omitting some of these factors, one can construct simplified enzyme cost functions with higher specific rates, or lower enzyme demands  that are non-linear, but convex over E. Some EMC functions are strictly convex  holds with a < sign instead of ≤), while others are not (e.g. EMC2). The most simplified EMC functions are actually constant (as in EMC0 and EMC1). To find an optimal state, we choose an EMC function and minimize the total enzyme cost within the metabolite polytope. Optimal metabolite profiles, enzyme profiles, and enzyme costs are obtained by solving the enzyme cost minimization (ECM) problemEnzyme cost minimization (ECM) uses a metabolic network, a flux profile q (defined in q(x) and the metabolite polytope itself are convex, ECM is a convex optimization problem. The optimal enzyme levels depend on external conditions and have to be recalculated after any change in external metabolite levels. There are cases where q(x) is convex, but not strictly convex, and therefore λ is a small, heuristically chosen weighing factor .E. coli model, ATP is one such important boundary metabolite: if we allowed for a lower ATP level, ATP could be produced at a lower enzyme cost because of the more favorable driving forces. If we do not fix the ATP concentration, but define an allowed range, ECM would choose the lowest possible ATP level; thus, if the allowed ATP range is too broad, no meaningful predictions can be expected. In a model of ATP-consuming biosynthesis pathways, the situation would be exactly the opposite: here, it is a high ATP level that would lead to higher driving forces and to lower enzyme requirements. Whole-cell models contain both types of pathways—ATP-producing and ATP-consuming ones. In such a model, ECM could predict some meaningful compromise, i.e. an intermediate ATP level that minimizes the enzyme cost of ATP production plus the enzyme cost of biosynthesis. Since in our central metabolism model there are only 3 reactions that produce or consume ATP, it is unlikely that so few reactions would be representative of the cost tradeoff between the dozens of enzymes that use ATP in the full metabolic network. Therefore, we chose to fix the ATP level to its measured value (∼3 mM).A key point in ECM is the choice of metabolite levels on the model boundary. If we predefine all these metabolite levels, our pathway will be “isolated” from the rest of the network, and any information about the surrounding network can be safely ignored. In our E. coli’s proteome, this effect is probably not so severe. In any case, this problem can be easily fixed by imposing constraints or fixed concentrations for central metabolites such as pyruvate .Our use of small-scale pathway models, which ignore most of the metabolic network, is therefore justified: as long as we predefine all metabolic fluxes and all metabolite levels at the pathway boundary, pathways can be modeled separately and the models can later be combined without any adjustment. This makes the ECM approach fully modular. All the input parameters  are directly obtained from measurements without any further tuning. By setting all relevant fluxes and boundary metabolite concentrations to their measured values, we isolate our submodel from any effects that the surrounding network might have on predicted enzyme cost. Finally, there may be metabolites that are “free” in a model, but that affect enzymes that are not in the model . By neglecting these enzymes, we ignore some of the complex compromises between them, and the predicted metabolite concentration may be wrong. In our specific case of central metabolism, whose enzymes comprise a very large fraction of q) and choose a tolerable cost qtol . This defines a tolerable region in Evolution could tolerate non-optimal enzyme costs; this tolerance depends on population dynamics and can sometimes be quite significant, e.g. in small isolated communities. To compute realistic tolerance ranges for the ECM problem, we start from the optimum . Errors or uncertainties in these constants will cause errors or uncertainties in the predicted enzyme profiles. To estimate these uncertainties, we considered a joint distribution of all model parameters, describing both the uncertainties of individual parameters and the correlations between dependent parameters. This probability distribution was directly obtained from parameter balancing ; then we sampled metabolite profiles in a much wider range, by sampling convex combinations of extreme points in the metabolite polytope . As shown in effective parameters, which may differ from “ideal” parameters, e.g., by crowding effects. However, since measured parameter values are usually incomplete and inconsistent, parameter balancing [To predict enzyme and metabolite levels in metabolic pathways we developed an automated workflow . In a coalancing  is used alancing .E. coli central metabolism  were obtained from the BRENDA database , and a complete, globally consistent parameter set was determined by parameter balancing. During ECM, all metabolite levels were limited to predefined ranges, and the levels of cofactors and some other metabolites were fixed at experimentally known values. To compute tolerances for predicted metabolite and enzyme levels, we defined an acceptable enzyme cost, one percent higher than the minimal value, and determined ranges for metabolite levels that agree with this cost limit. The enzyme cost function accounts for protein composition, giving different costs to different amino acids. However, models with equal cost weights for all proteins, or with size-dependent protein costs yielded similar results (results are provided on the website). Data, model, and MATLAB code for ECM can be obtained from www.metabolic-economics.de/enzyme-cost-minimization/.The model shown in S1 Text(PDF)Click here for additional data file.


Escherichia coli polyculture collectively expressing 15 exogenous or modified pathway enzymes from diverse plants and other microbes. This synthetic consortium-based approach enables the functional expression and connection of lengthy pathways while effectively managing the accompanying metabolic burden. The de novo production of specific anthocyanin molecules, such as calistephin, has been an elusive metabolic engineering target for over a decade. The utilization of our polyculture strategy affords milligram-per-liter production titers. This study also lays the groundwork for significant advances in strain and process design toward the development of cost-competitive biochemical production hosts through nontraditional methodologies.Fermentation-based chemical production strategies provide a feasible route for the rapid, safe, and sustainable production of a wide variety of important chemical products, ranging from fuels to pharmaceuticals. These strategies have yet to find wide industrial utilization due to their inability to economically compete with traditional extraction and chemical production methods. Here, we engineer for the first time the complex microbial biosynthesis of an anthocyanin plant natural product, starting from sugar. This was accomplished through the development of a synthetic, 4-strain  To efficiently express active extensive recombinant pathways with high flux in microbial hosts requires careful balance and allocation of metabolic resources such as ATP, reducing equivalents, and malonyl coenzyme A , as well as various other pathway-dependent cofactors and precursors. To address this issue, we report the design, characterization, and implementation of the first synthetic 4-strain polyculture. Division of the overexpression of 15 enzymes and transcription factors over 4 independent strain modules allowed for the division of metabolic burden and for independent strain optimization for module-specific metabolite needs. This study represents the most complex synthetic consortia constructed to date for metabolic engineering applications and provides a new paradigm in metabolic engineering for the reconstitution of extensive metabolic pathways in nonnative hosts. Microbial communities are ubiquitous in nature. In much the same way that multicellular eukaryotic organisms have evolved to contain specialized organelles that work together to seamlessly perform specialized tasks, communities of unicellular organisms have evolved similar divisions within their populations, such that microbial consortia amount to more than simply a sum of individual parts –5. These–The study and application of natural microbial consortia have been topics of interest in the scientific literature for decades –8; howev–9–de novo production of flavan-3-ols and anthocyanidin-3-O-glucosides in microbial hosts , the predominant red pigment in strawberries  represents a major milestone for the de novo production of phenylpropanoic acids , while the remaining 8 were targeted for caffeic acid production (TAL and HpaBC overexpression). The effect of the endogenous gene supplementation plasmid pCS-TPTA was also tested but did not show significant titer improvements for any of the tested combinations  and one caffeic acid production strain (R2) from the initial screen were subjected to further optimization to determine the full potential of these modules in monoculture. Through course optimization of the induction point, inducer concentration, production temperature, and carbon source, the highest-titer production to date was realized for both p-coumaric and caffeic acids at 2.51 ± 0.03 and 1.03 ± 0.02 g/liter, respectively  intermediate, was observed from all caffeic production modules both on solid as well as in liquid media. This observation is consistent with current literature and is assumed to be the primary factor limiting caffeic acid production  was conserved, with the fraction of the new module being varied to result in the first account of a functional synthetic four-strain polyculture. This microbial consortium enabled, for the first time outside of plants, the production of the anthocyanidin-3-O-glucoside callistephin from glucose (Our previous successes using polycultures for the production of flavonoids motivated the further application of this technology to expand what is currently possible  pathway . Transfo glucose .P < 0.05) of callistephin from glucose was achieved across a wide range of inoculation ratios, while conserving the optimum fermentation conditions from previous experiments.Addition of two additional enzyme overexpressions, MatBC, to the previously published C5 module further highlights the flexibility of the polyculture platform for rapid expansion and modification. These enzymes enable the uptake of externally supplemented malonate and subsequent activation to malonyl coenzyme A , a key and limiting substrate for the chalcone synthase (CHS) enzyme . Signifide novo production of various late-pathway flavonoid metabolites demonstrates the power of such approaches over traditional monoculture metabolic engineering efforts  is key to minimizing difficulty in this area. We do envision future applications, especially with products that are membrane associated or lipophilic, where significant effort will need to be performed to ensure transport to and from strain modules. Additionally, in systems where transport must be engineered, this limitation can also be used as an advantage to limit substrate availability to promiscuous enzymes in off-target strain modules.de novo production of high-value specialty flavonoids and anthocyanin chemicals.We have shown high sensitivity to small (less than 5%) changes in initial community composition, which readily facilitates fine-tuning of consortium productivity but could also lead to many unknowns upon scale-up to fed-batch, high-cell-density cultures. Additionally, as subsequent microbial modules are appended to the polyculture, its further expansion is limited by a fixed total biomass to be distributed across all modules. This shortcoming of polyculture-based strategies is minimized when an obvious rate-limiting module is identified, but it can be more pronounced when multiple limiting/bottlenecking modules exist and deplete space available for the more efficient modules in the pathway. Despite these limitations, we have demonstrated the ability of our polyculture-based approach to outperform previously reported efforts to enable the de novo production of flavan-3-ols for the first time outside of the native plant hosts. Further expanding on this polyculture theme, we incorporated a fourth module (Antho) containing the anthocyanadin synthase (ANS) and 3-O-glycosyltransferase (3GT) genes. Using all four modules, we also showcased, for the first time outside of plants, the production of the anthocyanidin-3-O-glucoside callistephin from glucose. This complicated feat was facilitated by the modularity of the polyculture platform, which conserves the genetic optimization of each module and only requires basic fermentation optimization to achieve peak production. The use of this polyculture-based approach has enabled a system complexity only rivaled by a few other success stories in the metabolic engineering literature , we demonstrate the terature , 34. OurE. coli DH5α was used to propagate all plasmids. BL21star(DE3), BL21star(DE3)ΔsucC ΔfumC  on pTrc-RgTALsyn  to create the constitutive expression plasmid pXylA. The PxylA promoter, although xylose inducible in B. megaterium, was shown previously to be constitutively expressed in E. coli  in pXPA-FapO-eGFP (PGAP promoter) (We replaced the T7-lac feature on pETM6 with the P E. coli . To thisromoter)  at restr10.1128/mBio.00621-17.1TEXT S1 TEXT S1, DOCX file, 0.05 MB.gBlock sequence for cloning pXylA. Download Copyright © 2017 Jones et al.2017Jones et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the d-thiogalactopyranoside). In the case of the phenylpropanoic acid production strains, 125-ml nonbaffled shake flasks containing 25 ml of medium were used to confirm small-scale screening studies, allow for more frequent sampling, and limit evaporation effects on final titer.The small-scale cultivation protocol was adapted from Jones et al.  with onlO-glucosides, where 518 nm was used. A representative high-performance liquid chromatography (HPLC) chromatogram for anthocyanin quantification is shown in t test with a 95% confidence interval.Analysis methods were slightly adapted from Jones et al. . A 25-μl10.1128/mBio.00621-17.2FIG S1 FIG S1, PDF file, 0.2 MB.HPLC chromatogram for callistephin quantification. Traces for the commercially available standard (Std) and 4-strain polyculture supernatant are shown. Download Copyright © 2017 Jones et al.2017Jones et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the


T. erythraeum and used genome-scale modeling approaches to investigate carbon and nitrogen fluxes as well as growth and equilibrium population composition.Computational, genome based predictions of organism phenotypes has enhanced the ability to investigate the biological phenomena that help organisms survive and respond to their environments. In this study, we have created the first genome-scale metabolic network reconstruction of the nitrogen fixing cyanobacterium T. erythraeum with 971 reactions, 986 metabolites, and 647 unique genes. We then used data from previous studies as well as our own laboratory data to establish a biomass equation and two distinct submodels that correspond to the two cell types formed by T. erythraeum. We then use flux balance analysis and flux variability analysis to generate predictions for how metabolism is distributed to account for the unique productivity of T. erythraeum. Finally, we used in situ data to constrain the model, infer time dependent population compositions and metabolite production using dynamic Flux Balance Analysis. We find that our model predicts equilibrium compositions similar to laboratory measurements, approximately 15.5% diazotrophs for our model versus 10-20% diazotrophs reported in literature. We also found that equilibrium was the most efficient mode of growth and that equilibrium was stoichiometrically mediated. Moreover, the model predicts that nitrogen leakage is an essential condition of optimality for T. erythraeum; cells leak approximately 29.4% total fixed nitrogen when growing at the optimal growth rate, which agrees with values observed in situ.We created a genome-scale reconstruction of T. erythraeum colonies. Our predictions match both in situ and laboratory data, indicating that stoichiometry of metabolic reactions plays a large role in the differentiation and composition of different cell types. In order to realize the full potential of the model, advance modeling techniques which account for interactions between colonies, the environment and surrounding species need to be developed.The genome-metabolic network reconstruction allows us to use constraints based modeling approaches to predict growth and optimal cellular composition in The online version of this article (doi:10.1186/s12918-016-0383-z) contains supplementary material, which is available to authorized users. Trichodesmium is a genus of filamentous diazotrophic (nitrogen fixing) cyanobacteria that not only flourishes in this environment but provides bio-available nitrogen for surrounding species. Trichodesmium is responsible for fixing roughly 100 TgNy-1 of nitrogen annually   = 0.12 × [(A565 − A592) − 0.20 × (A455 − A592)] [Phycoerythrin is a protein-pigment complex that is abundant in  A592)] .Lipids were extracted using the Bligh and Dyer method . Chlorop18:C16:C14, etc.) was assumed to be the same distribution for every lipid subclass. Otherwise, lipid pathways were consistent with the methods described in the Cyanothece ATCC51422 model  = [0.01776(A646.6 − A750) + 0.00734(A663.6 − A750)].The only chlorophyll pigment o Harris . First, μL cells concentrated from 250 mL to 2 mL via filtration overnight and phycocyanin was measured in the resulting medium according to established techniques [2.70 mg.mL lysozyme was used to disrupt 740 chniques , 73.μL was disrupted using 2.70 mg/mL lysozyme overnight at 37 °C/proteinase K for 2 h at 55 °C instead of bead disruption. The concentration of DNA and RNA were assessed spectrophotometrically using 1 cm path-length extinction coefficients [DNA and RNA were extracted using MoBio UltraClean Microbial Isolation Kits  with 50 mL cells concentrated to 2 mL, of which 740 Leptolyngbya, Anabaena, Synechococcus, and Cyanothece to identify unannotated genes. Where these organisms failed to predict genome similarity, proteins displaying the function as the unannotated, or insufficiently annotated, reaction were BLASTed against all available organisms. This resulted in similarities to the organisms in Table Nitrosococcus oceani, Pleurocapsa sp. PCC 7327, Zymomonas mobilis subsp. NRRL B-12526, Amborella trichopoda, Dehalococcoides mccartyi VS, Nostoc sp. PCC 7524, and Candidatus Nitrosophaera gargensis. Photosynthesis was modeled similarly to other cyanobacterial genome scale reconstructions [and operator; redundant genes were assigned an or operator). These were done with consultation of KEGG [2, H2O, N2, etc.) [T. erythraeum genome and were non-essential, therefore they were removed. Irreversibility was assumed where databases indicated significant energetic unfavorability (such as in redox reactions) or where other models demonstrated consistent unidirectionality. Reactions were finally elementally and charge balanced using built-in COBRA functionality in conjunction with the PubChem Database [The genome scale network reconstruction was based on the publically available genome sequence  and an aructions , 59, 80  of KEGG , BioCyc  of KEGG , BRENDA  of KEGG , and Cya of KEGG . Finally2, etc.) . Manual Database . Small mDatabase .in situ data found in literature [2 and ammonium while exporting glycogen, diatomic oxygen, and diatomic nitrogen. Available nutrients, trace metals, and ions which are allowed to be used for growth were restricted according to the YBC-II medium formulation. Table Cyanothece sp. ATCC 51142, another photosynthetic diazotroph [-1 and maintenance related ATP was set so that the growth rate matched literature derived physiological data [-1 and 67.2 mmol (g DW h)-1 for diazotrophs by matching the growth rate in the dFBA model to the growth rate measured in literature. This is significantly higher than other reported values, but is justified by the lack of substantial light restrictions. It is assumed that light uptake restrictions and maintenance ATP are both contained within this constraint. All other nitrogen and carbon sources except for those specified in Table The genome scale metabolic network was used to predict fluxes using FBA . The conterature , 29. Twoterature , nitrogeazotroph , at 544 FBA and FVA were conducted using COBRA toolbox functions . The optThe cell-specific uptake and export rules as well as the seawater constraints generated a suite of constraints of the form :The cell-specific uptake and export rules as well as the seawater constraints generated a suite of constraints of the form:Diazotroph Cell Bounds:Phtotoautotroph Cell Bounds:It should be noted that seawater ingredients are assumed to be replete in this model.Steady state was assumed to make the model solvable:Essentially, this means that mass is conserved and all inputs exit either through the biomass equation or through export. The second major assumption was that the only outputs would be biomass, glycogen, or ammonium; energy or mass balancing mechanisms, such as through organic acid spilling, were assumed to be negligible because of efficient, exponential growth conditions.Gene deletions, reaction deletions, and Dead End reaction analysis were run using these constraints. These problem formulations were solved using the COBRA toolbox  in conju-1 h-1 for the photoautotroph and 67.2 mmol ATP (g DW)-1 h-1 for the diazotroph.dFBA was executed with the aid of the Dynamic Multispecies Metabolic Modeling (DyMMM) framework as a template . Both thμ and Δt are non-negative) by:For the purpose of this program, the final constraint is estimated , total growth rate (μT), and fixed nitrogen yield (YN → Env) were calculated by:Adjusted values are calculated for final concentrations and, during the next iteration, the new initial values are the final values from the previous iteration. This ends at a defined maximum time (400 h) and cell fractions are calculated as a function of biomass of cell type Since multiple steady states exist for population composition, growth rate, and substrate release rate, randomized initial cell concentrations  were generated using MatLab over 1000 iterations. Also, FBA was run individually on each cell type once more to determine the growth rate of each cell type in replete  conditions.T. erythraeum on different nitrogen sources was assessed by “opening” reaction boundaries for exchange reactions corresponding to the nutrient and assuming the nutrient was in excess. Then, the dFBA simulation was run assuming equilibrium starting composition. The growth profiles and growth rates were recorded in the same way as previous simulations. Where proteomics data did not infer transport of a particular compound, a simple, diffusion style transporter was temporarily added to the model.The hypothetical viability of