Bioinformatics tools facilitate and accelerate all steps along the Design–Build–Test cycle of synthetic biology, for the enhanced production of natural products in engineered microbes. Covering: 2000 to 2016Progress in synthetic biology is enabled by powerful bioinformatics tools allowing the integration of the design, build and test stages of the biological engineering cycle. In this review we illustrate how this integration can be achieved, with a particular focus on natural products discovery and production. Bioinformatics tools for the DESIGN and BUILD stages include tools for the selection, synthesis, assembly and optimization of parts (enzymes and regulatory elements), devices (pathways) and systems (chassis). TEST tools include those for screening, identification and quantification of metabolites for rapid prototyping. The main advantages and limitations of these tools as well as their interoperability capabilities are highlighted. These compounds are used in a wide range of interesting applications, ranging from pharmaceutical uses as drugs against many diseases to flavours and fragrances in food and personal care products. Their economic potential and the fact that they are originally synthesized by biological systems make natural products highly attractive targets for the advanced genetic engineering strategies of synthetic biology, with the aim of producing them more efficiently, in more amenable host species, from cheaper raw material, and potentially with the option of introducing added value and new functionalities by engineered modifications of the biosynthetic pathways. The necessary large-scale engineering of microbial production systems is only possible if it is supported by tailored computational tools at each of the stages of the engineering cycle  to be added to engineered biosynthetic pathways.Computational design tools are needed in order to identify the best combinations of enzymes, pathways, regulatory components, and chassis organisms leading to the efficient production of target natural products see . This in5 For this approach to be successful, tools are needed to systematically search for all possible pathways leading to a target compound, and to correctly prioritize them through a ranking algorithm based on predicted pathway's efficiency. BNICE and SimZyme6 are a collection of pathway design tools that apply a set of reaction rules to predict possible biosynthetic routes towards desired target compounds and then identify the candidate enzymes that might be coerced to catalyse the necessary reactions. This tool kit has been applied to predict possible biosynthetic pathways for target compounds starting from native metabolites; these are currently awaiting experimental verification. Other recent proposed tools for selecting the most promising enzymatic route towards a target include the Sympheny Biopathway Predictor7 developed by Genomatica; RouteSearch,8 based on atom mapping; and PathPred,9 based on the reaction patterns in the KEGG database. Work in this area is still in a very early stage: for instance, PathPred was used to look for alternative biosynthesis routes in the flavonoid pathways converting the plant pigment delphinidin into gentiodelphin.9 The system was able to predict new pathways in addition to known pathways, but most of them were found to be non-viable upon manual inspection because they required predicted reactions that are chemically infeasible. The main challenge of pathway prediction tools will be the automated prioritization of successful candidates from among the easily generated thousands of alternative pathways. Possible criteria for identifying a predicted pathway as likely to be efficient are diverse and several computational approaches to estimate pathway efficiency have been proposed.10 For instance, Metabolic Tinker11 prioritizes pathways based on thermodynamic feasibility; FindPath12 in addition considers pathway length and theoretical yield; RetroPath/XTMS14 scores enzyme performance based on predicted promiscuous activities and adds toxicity of intermediates to the ranking; and GEM-Path15 includes flux efficiency.In an extreme variant of this approach, instead of modifying natural pathways, newly assembled enzymatic routes can be explored to produce a target natural product in a chassis organism.18 For the computational prediction of the properties of ribosome binding sites (RBS), the situation is slightly better, and there is a class of tools for engineering binding sites to achieve desired translation rates in prokaryotic hosts.22 Unlike other regulatory elements , RBS sites are strongly influenced by their flanking sequence, including the 5′ end of their cognate open reading frame (ORF) and, in operons, the 3′ terminus of the previous ORF.23 For this reason, the design of RBS sites should ideally be done simultaneously to ORF sequence optimization, but currently no tools are available to do this. So far, RBS prediction has been used successfully to debug aberrant RBS sites mid-ORF during sequence optimization,24 to design bespoke RBS,25 and to optimize RBS library design for the engineering of E. coli pathways to increase riboflavin levels26 and NADPH recycling.27An important consideration when designing an engineered pathway is the selection of regulatory components. Pathway efficiency requires preventing flux imbalances, which would lead to the depletion of essential precursors or the accumulation of intermediates, which in turn could result in toxicity or feedback inhibition of the pathway. This can be achieved by the right selection of regulatory components including promoters and transcriptional terminators, and ribosome binding site, which control transcription and translation rates, respectively. The accurate prediction of promoter and terminator properties is not currently possible based on sequence data alone; instead libraries of promoters and terminators have been experimentally characterised and standardised to allow predictive selection. The necessary characterisation information is held in databases such as The Registry of Standardised Biological Parts (http://parts.igem.org/Main_Page), and in the primary literature.28 can assist in the selection of the optimal chassis strain for a specific natural product,30 and the subsequent optimization of chassis metabolism. Central to in silico approaches for chassis selection and optimization are constraint-based flux prediction approaches.31 The first requirement for the application of these approaches is the availability of comprehensive descriptions of the stoichiometry of all metabolic reactions in an organism, which can usually be inferred from genome annotations in combination with manual curation. The resulting models collate all known metabolic reactions – along with information on metabolic enzymes, transporters, and their encoding genes – in a principled format that is amenable to computational analysis.32 Such models have increased in scale, coverage and quality over the last 15 years and are now available for many organisms relevant to industrial biotechnology.34 Furthermore, protocols and automated computational pipelines for their construction have been published.37 To select suitable chassis strains for a particular natural product, the reactions of its biosynthetic pathway are added to the metabolic models of a collection of different potential hosts, and multi-objective optimization  is applied to predict which strain can achieve the optimal balance between biomass production and the production of the desired chemical.Generally, natural products of interest are not naturally produced by common industrial production microbes; instead their biosynthetic pathways need to be engineered for recombinant production in industry-compatible strains. A growing number of genome-scale metabolic models (GEMs), available at the BioModels repository,38 Constraint-based flux analysis can be used to predict which genes will be the most promising targets for this strategy, and a large number of tools have been developed to implement this approach.43Even when a predicted optimal strain has been chosen for the engineered production of a natural product, additional rounds of strain optimization are usually required to reach industrially viable production levels. For this task, the same constraint-based metabolic models serve as the starting point. The basic premise of strain optimization is to amend host metabolism such that metabolic flux is increased towards the target molecule whilst maintaining cellular growth. This commonly involves the implementation of gene knockouts or over-expressions to channel metabolic flux as required.3et al.45). Furthermore, many commercial gene synthesis vendors  provide their own optimization algorithms for use prior to submitting orders, which allows further specific optimization for their synthesis methodology. All the available design tools allow codon optimization and the definition of specific base patterns such as restriction enzyme recognition sequences that should be avoided, but the rationale and algorithm for codon optimization and the degree of consideration of other criteria vary widely between programs.When introducing an engineered pathway into a new chassis strain, the applied genetic manipulations are no longer restricted to producing new combinations of selected pathway parts and regulatory elements. Instead, as DNA synthesis is increasingly affordable, it is possible to design the sequence of each individual part before combining them into optimized devices. At each step, multi-objective optimization is needed to ensure that synthetic genes express successfully in a given host, including organism-specific codon-optimization, alleviation of secondary mRNA structure, as well as removal of intrinsic regulation , repeating sequences and homopolymeric tracts. Many pieces of freely available software can be combined in automated pipelines for sequence optimization, as shown in 46 and the inclusion of unique ends facilitates seamless assembly methods such as Gibson and the Ligase Cycling Reaction.48 The process of defining the correct sequence for all parts and their intended combinations can be remarkably complex and error-prone, particularly when multiple or combinatorial assembly is to be performed. Design tools such as j5,49 SnapGene  and Genome Compiler  have functions for schematic in silico pathway construction, which will automatically generate the required oligomer sequences including restriction sites, overhangs and linkers. Furthermore, “recipes” – instructions for the experimental order of assembly of parts in vitro – are produced by these tools in order to streamline the sequence ordering and experimental process. These design tools have functionality to design assemblies compatible with such protocols as GoldenGate,46 InFusion , Gibson47 and Gateway cloning , and functionality is constantly improving to support new assembly methods.Individual DNA parts will be assembled into larger constructs to produce biosynthetic pathways, and so the design of the part sequences should be compatible with the downstream assembly method . For instance, removing all BsaI restriction sites makes parts compatible with GoldenGate assembly and variations,4de novo pathways require chemical transformations not necessarily seen in nature . In these instances, directed evolution can be employed to engineer enzymes to improve their activity towards a predetermined reaction.50 This method involves a close interaction of designing and building (and ultimately testing), which require special computational tools that allow this direct connection between the stages of the engineering cycle. Directed evolution approaches generate variant libraries of a gene of interest, encoding an enzyme that is predicted to have at least some activity towards the desired reaction, and selects variants that exhibit an improved function. Iterative cycles of variation and selection can be employed until the desired fitness  is reached. Traditionally, the necessary genetic diversity was achieved using random methods, primarily error-prone PCR53 or recombination,55 or site-directed mutagenesis (amongst others58). However, in the context of synthetic biology, gene synthesis59 approaches provide a means by which more rational strategies of protein engineering can be employed.A particular challenge for the engineering of natural products production involves those cases, where no suitable enzymes are available for a specific step within a pathway. This can be the case when the native enzyme that performs a particular transformation has not yet been identified, or when 61 and MUSCLE,62 can analyse patterns of sequence diversity and conservation within classes of proteins, which can inform about the site and type of mutations that are most likely to lead to improved functionality. If a 3D structure of the protein that serves as the evolutionary starting point is known, then the HotSpot Wizard tool,63 which integrates functional, structural and evolutionary data, can be used to identify potential target residues.Sequence alignment tools like Clustal50 the design tool GeneGenie64 can be used to guide the de novo synthesis of variant libraries. GeneGenie designs DNA sequences optimized for expression in a desired host, includes any sequences required for downstream cloning, and the mixed-base codon sequences. The resulting oligonucleotide sequences can then be synthesised and assembled using the SpeedyGenes method,59 which accommodates multiple and combinatorial variant sequences while at the same time implementing efficient enzymatic error correction, to create large but controlled libraries of variants, significantly reducing the “hands-on” time required for the experimental design.Having decided upon the target residues and type of variants to create,565 accumulation of unwanted intermediates, as well as unexpected pleiotropic consequences of the genetic manipulations. When focusing on quantitative profiling of changes in the composition of the growth medium , metabolomics can also provide highly useful flux constraints to include in genome-scale metabolic models, which increases their predictive power for improved strain designs. The models can be further supplemented by constraints based on quantitative transcriptome profiles, which in microbes can serve as an informative proxy for enzyme activity levels and thus pathway flux.66Following the construction of engineered microbial strains for natural product production, it is essential to characterize their phenotype in sufficient detail to provide informative feedback for the next iteration of the DESIGN stage. The major technological platform for this purpose are various molecular profiling methods, most importantly metabolomics. These methods not only help to characterize the production level of the target compound, but also allow a broad untargeted characterization of the metabolic state of the engineered microbe, which allows the detection of pathway bottlenecks,The development of computational tools for untargeted metabolomics is a mature area of research, and a large number of comprehensive software platforms have been made available in recent years . However674 or trackable gene traits,75 is necessary. Protocol languages for the automation of synthetic biology robotic platforms,76 such as the ones established by bio-foundries like Abolis, Zymergen, Ginkgo Bioworks, Amyris and SYNBIOCHEM, should facilitate the generalization of these pipelines. Moreover, integration of automated data analysis and machine-learning workflows into the protocols will ultimately provide the tools to seamlessly feed back from TEST into DESIGN.77 An area where rapid progress can be expected is the field of directed evolution for parts optimization; here, substantial datasets comprising quantitative sequence–activity information can often be obtained. In these cases, computational approaches, such as those implemented in the ProSAR software, can be adopted to infer predictive statistical models of sequence–activity relationships, to guide the next round of library design.80 Future improvements could include a more efficient mapping of an enzyme fitness landscape using machine learning algorithms, as has already been demonstrated in a related proof-of-concept for learning the sequence–activity relationship of DNA aptamers, using the Closed Loop Aptameric Directed Evolution (CLADE) approach.81Computational tools to automate the feedback from the molecular characterization of engineered strains in the TEST stage to the improved engineering strategies of the DESIGN stage are one of the major remaining gaps in the computational synthetic biology toolbox. Few convincing examples exist at the moment, and even when computational tools are used, these tend to be bespoke scripts for a specific project, rather than generalized pipelines. Existing design tools still require a better coupling to screening and selection technologies. The development of high-throughput approaches to that end, including targeted biosensors782 Of particular prominence for the establishment of computational standards is the Synthetic Biology Open Language (SBOL),83 an RDF-based standard for representing synthetic gene design that has been developed by an international consortium over recent years. The current release, SBOL v2.0,84 incorporates both structural and functional design features and integrates with systems biology modelling standards such as the Systems Biology Markup Language (SBML),85 providing a link between computational modelling (DESIGN) and wet-lab assembly (BUILD). SBOL is augmented with a visual representation, SBOL Visual86 which has the goal of standardising the visual representation of synthetic gene constructs, analogous to the standard representation of electronic circuits that enables electronic engineering. Moreover, optimization of the design process requires a better definition of constraints and objectives in a multiscale fashion. Such approaches would need to be matched by rapid prototyping systems for the BUILD stage exploring the design space efficiently. Similarly, autonomous and continuous learning from experimental test results needs to be enabled. Recently established bio-foundries, which are synthetic biology-based chemical manufacturers operating under tight and demanding constraints, serve as a critical testbed for computational tools at every step of the DESIGN–BUILD–TEST cycle and are key players in promoting the adoption of standard practices enabling software interoperability. It can be predicted that the experiences gained in these ambitious large-scale bio-engineering enterprises will rapidly diffuse to the wider synthetic biology community in the coming years.Efficient production of natural products in non-native chassis organisms is becoming more streamlined through the application of synthetic biology techniques. A growing range of computational tools is facilitating the synthetic biology engineering approach at each step of the process. However, the integration of DESIGN, BUILD and TEST tools is still one of the main challenges at present, and lack of interoperability between the bioinformatics tools is hindering a wider adoption of these tools by the community. Present requirements include a better standardisation to ensure interoperability between individual tools and seamless integration and traceability across the design/build/test stages. Several initiatives, like the NIST Synthetic Biology Standards Consortium, have recently been launched to address such standardisation issues.


Complex disorders including obesity, diabetes, fatty liver diseases, cardiovascular disease (CVD), and cancer are results from a combination of genetic, environmental, and lifestyle factors. The prevalence of such disorders has increased dramatically in the last two decades and there is an urgent need for the development of new prognostic tools for the treatment of such diseases. However, this requires a deep understanding of the underlying molecular mechanisms involved in the occurrence of the diseases. With the advances in high throughput technologies, biological components of cells can be measured with a very high resolution and these data can be used for investigating whole systems properties using a network-based approach. Systems medicine provides an integrative platform for studying the interactions between the biological components of the cell using a holistic approach and generating mechanistic explanations for the emergent systems properties  are important tools in systems biology and employed for simulating the metabolism of cells/tissues in different states  specific GEMs based on gene expression data and used their context-specific model to predict metabolic flux distributions in the brain tumor cells. The authors identified the GBM specific metabolic alterations and provided a comprehensive coverage of tumor metabolism. Moreover, flux distributions in glycolysis, glutaminolysis, TCA cycle, and lipid metabolism were predicted and validated by additional computational analysis and literature information.Tumor cells alter their metabolism to maintain their growth and proliferation. Zhang et al. evaluated the potential value of current major systems biology-based approaches, including genome wide association studies, gene regulatory networks, and protein–protein interaction networks and GEMs. The authors discussed how integrative analysis of personal multi-omics data may provide increased understanding of personal genotype–phenotype relationships. Moreover, the potential benefit of integrating different type of networks in increased understanding of the interactome is discussed. Recently, metabolic networks have been integrated with other biological networks and these networks have been used in the analysis if omics data obtained from different clinical conditions (Lee et al., Brown reviewed the challenges in the generation of SuperModels where computational models were integrated with various data types and analytics. The author has also discussed how these SuperModels may assist in the development of personalized and precision medicine.in vivo. Adiels et al. reviewed the recent kinetic studies and discussed how these studies may improve our understanding of impaired human lipoprotein metabolism. Even though, stable isotope labeling analyses have been highly targeted, in recent years, tools for the global non-targeted detection, quantification, and computational analysis of isotopic enrichment have become available. Weindl et al. discussed how such non-targeted stable isotope labeling analyses can be applied for systems medicine applications.Stable isotope assisted metabolomics techniques have been used in systems medicine for metabolic flux analysis and pathway discovery. It is essential to understand the pathophysiology of dyslipoproteinemia in humans since it has major role in the progression of complex diseases including CVD and diabetes. The use of tracers labeled with stable isotopes and mathematical modeling may provide a powerful tool for probing lipid and lipoprotein kinetics Shmelkov et al. described an approach to improve in silico identification of a comprehensive ensemble of targets for any drug weighted by the expression of those receptors in relevant tissues. Their approach may increase the sensitivity of target detection and allow for systematic integration of bioactivity/docking scores between drugs/compounds and proteins with the expression patterns of those proteins in human tissues.Finally, The work presented in this Research Topic addresses many of the recent progress in the applications of the GEMs is medical applications. The integration of GEMs with other biological networks and generation of whole cell models may foster the development of personalized medicine which may increase benefits and reduce risks for patients.AM and JN wrote the editorial together and approved its final 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.


Escherichia coli cells growing in a defined medium. We extend the methodology to account for correlations in protein expression arising from the co-regulation of genes and apply it to study the growth of independent Saccharomyces cerevisiae cells in two different growth media. We find the partitioning of flux between fermentation and respiration predicted by our model agrees with recent 13C fluxomics experiments, and that our model largely recovers the Crabtree effect , while FBA without proteomics constraints predicts respirative metabolism almost exclusively. The comparisons to the 13C study showed improvement upon inclusion of the correlations and motivated a technique to systematically identify inconsistent kinetic parameters in the literature. The minor secretion fluxes for glycerol and acetate are underestimated by our method, which indicate a need for further refinements to the metabolic model. For yeast cells grown in synthetic defined (SD) medium, the calculated broad distribution of growth rates matches experimental observations from single cell studies, and we characterize several metabolic phenotypes within our modeled populations that make use of diverse pathways. Fast growing yeast cells are predicted to perform significant amount of respiration, use serine-glycine cycle and produce ethanol in mitochondria as opposed to slow growing cells. We use a genetic algorithm to determine the proteomics constraints necessary to reproduce the growth rate distributions seen experimentally. We find that a core set of 51 constraints are essential but that additional constraints are still necessary to recover the observed growth rate distribution in SD medium.Using protein counts sampled from single cell proteomics distributions to constrain fluxes through a genome-scale model of metabolism, Population flux balance analysis (Population FBA) successfully described metabolic heterogeneity in a population of independent  No two living cells are exactly the same. Even cells from a clonal population with identical genomes living in the same environment will express proteins in different numbers simply due to the random nature of the chemistry involved in gene expression. The consequences of this stochastic gene expression are complex and not well understood, especially at the level of large reaction networks like metabolism. Here we investigate how variability in the copy numbers of metabolic enzymes affects how individual cells extract nourishment from their environment and grow. We model 100,000 independent yeast cells, each with their own set of enzyme copy numbers sampled from experimental distributions, and use flux balance analysis (FBA) to compute the optimal way that each cell can use its metabolic pathways—an approach we dubbed Population FBA. We find that enzyme variability gives rise to a wide distribution of growth rates, and several metabolic phenotypes—subpopulations relying on diverse metabolic pathways. Most importantly, we compare the predicted fluxes through the different pathways to experimental values; we find that Population FBA is able to correctly predict Crabtree effect, while traditional FBA, which lacks the proteomics constraints our method imposes, differs both qualitatively and quantitatively from experiment. A cell’s phenotype—its set of distinguishing observable traits—can be as much an emergent property of the cell’s environment and gene expression state as it is a result of the cell’s genotype. While some observables, like an organism’s response to Gram staining, can be immutable and tied to specific genes, others can be more fluid, varying from cell-to-cell with the random fluctuations in each cell’s molecular makeup –4. A celi.e. the flux into and out of each metabolite is balanced). Parsimonious FBA (pFBA) improves on the predicted flux distribution shown in13C medium. Sampling from protein distributions that have been rescaled for 13C medium allows us to predict such a curve . In eac−1, see Fig A9 and Fig A6 in The fast-growing peak was predominantly the result of limitation in glucose uptake . While slow-growing cells utilized glucose at rates below the maximum allowed rate; see Fig A5 and Fig A8 in E. coli [E. coli, for example, the glucose transporter ptsG might be expressed under the control of the lac system—the shape and location of any peak in the growth rate distributions of cells grown with and without IPTG  could then indicate whether glucose uptake limitation is responsible.A similar glucose-associated peak arose in work modeling the growth rate distribution of E. coli . In lighE. coli  could beE. coli [In order to identify the features of protein distribution which play important role in obtaining the shape of the distribution, we performed some control simulations. We randomly omitted 50% (267) and 66% (356) of the 535 available protein distributions and used the rest of the protein distributions to perform Population FBA. The resulting growth rate distributions are very similar to experimental growth rate distributions with a broad tail at slow growth rates and a peak at fast growth rates  respired little even under highly aerobic conditions. Using the wild-type yeast 7.6 model in 13C medium calculations were performed using our Population FBA methodology  were performed using our Population FBA methodology. The central metabolic fluxes of 1,000 simulated cells were compared to experimental values. The mean predicted flux through each of the experimentally characterized reactions showed strong qualitative agreement . One reaction in particular proved absolutely essential for prediction of significant fermentation in presence of oxygen and high glucose concentration or Crabtree effect. −1 hr−1. This is a key reaction in the electron transport chain within the mitochondria, and as a result of the low expression level of one of the proteins involved in catalyzing it  the simulations predicted a predominantly fermentative metabolismThe mean upper bound imposed on the ferricytochrome c reductase reaction, based on proteomics and kinetics data, was 1.20 mmol gDwt. Several other mitochondrial proteins were expressed in small numbers and had similarly limiting effects on oxidative phosphorylation, including PDX1 (expressed at approximately 232 per cell) which catalyzes the pyruvate dehydrogenase (PDH) reaction—an important step linking glycolysis and the TCA cycle, and CIT3 (approximately 356 per cell) which catalyzes the citrate synthase (CS) reaction which is part of the TCA cycle. In general the protein copy numbers involved in the fermentation pathway are considerably higher. Had these flux constraints not been imposed, the model would have predicted greater amounts of respirative metabolism (see FBA without Proteomics Constraints Spuriously Predicts Respiration in S. cerevisiae), and would have been unable to recover the experimentally observed behavior. Importantly, we note that the agreement we found between experimental and simulated fluxes could not have been realized without raising several dubious turnover rates that had initially been collected from the BRENDA database .Identical calculations  was important but not essential to the agreement we see between our simulated fluxes and the experimental ones. As examples, the glucose uptake and ethanol secretion reactions were overestimated without imposed correlations by about 1.6 and 4.5 mmol gDwt−1 hr−1, respectively. With correlated sampling, these errors fell to only about 0.8 and 3.1 mmol gDwt−1 hr−1 ; while our simulations do qualitatively predict small fluxes, we underestimate both production rates by a significant fraction. Acetate secretion, for example, was predicted to occur at a rate approximately 230-fold slower than was measured  was largely canceled. NADH and NADPH metabolism remains an active area of investigation [There are a few points of quantitative disagreement we observe between our predicted fluxes and those seen experimentally, most importantly in acetate and glycerol secretion, both of which were directly measured . The expured see . In ordetigation .13C modeled media  and the Analysis of sub-populations). The first two PCA components combined accounted for almost 85–95% of the total variability in pathway usage  in a manner similar to that described in   and relied more heavily on the efficiency of their mitochondrial oxidative phosphorylation machinery. This resulted is a slight drop-off in ethanol and CO2 production (decreased fermentation) and marked increase in oxygen uptake, flux through ATP synthase  (via methylenetetrahydrofolate dehydrogenase), eventually forming 10formylTHF, and 3) phosphorylated ADP producing formate and the THF necessary to complete the cycle. The net effect of these coupled mitochondrial and cytoplasmic pathways, as summarized in + molecules, the production of ATP, and the formation and subsequent excretion of formate and some ammonium  was associated with increased mitochondrial NADH production and subsequent oxidative phosphorylation. Interestingly, this behavior was also associated with increased succinate and formate production—metabolic byproducts usually made during fermentation  see Fig. The mainium see . While tnium see . Additioal study , 72. TheIn addition to some taken up from the modeled SD media, a significant portion of the glycine necessary to drive this system was found to originate from environmental citrate  would threonine be taken up at rates in between. We found that whether or not threonine was being heavily utilized correlated strongly with whether or not the protein-associated glyceraldehyde-3-phosphate dehydrogenase (GAPDH) or pyruvate decarboxylase (PDC) reaction bound was constraining glycolytic flux. When it was not constraining, the cells engaged in as much glycolysis as was needed to serve their energy requirements (NAD+ reduction and ADP phosphorylation) and threonine was utilized at its basal rate in order to cover protein synthesis. Conversely, when the GAPDH or PDC reaction bound was constraining glycolysis threonine uptake was high. We found that much of this threonine was used by the cells to form glycine  which in turn was used to fuel the glycine cleavage system . This enabled the cells to make up some of the capacity for NAD+ reduction and ADP phosphorylation that was lost due to their limited glycolytic flux. Because of the relatively small amount of threonine uptake allowed, its use was essentially always at its maximum when the cells engaged in this behavior. Flux variability analysis was performed on over 400 cells growing slower than than 0.4 hr−1. It confirmed that predicted threonine uptake is robust and not a randomly chosen flux distribution among many possible degenerate solutions  we chose for raising overly-constraining kcat values focused on identifying constraints that most strongly limited cellular growth rate. This has the benefit of keeping as many of the experimental parameters available intact, but it also raises some questions, the most important of which is whether the set of turnover rates that are doubled is the only set that would lead to a mean growth rate similar to the measured one. Similarly, are all of the turnover rates that are kept intact after the doubling procedure actually necessary? In order to address these questions, we implemented an Evolutionary Algorithm based approach for finding constraint sets that yield growth rate distributions that approximately recover the one seen experimentally. This method made use of the Micro Genetic Algorithm formalism, a type of genetic algorithm (GA) that uses relatively small numbers of “genomes” and dispenses entirely with “mutations” [Genetic algorithm for constraint selection).The main doubling algorithm  was lifted in some but not all of our GA runs. By comparing threonine usage across GA runs we found that when the GAPDH-associated kcat was raised the bimodality among slow-growing cells essentially disappeared; instead, at growth rates lower than about 0.3 hr−1, the modeled cells all took up threonine at its basal rate and none were found to utilize the glycine cleavage system , these studies have shown that proteins are expressed in varying numbers from cell to cell. In order to understand how any given cell’s protein expression state effects its behavior, and how that behavior relates to the overall behavior at the population level, these protein distributions must be sampled and realistic subpopulations of individual cells must be modeled. Our Population FBA approach provides such a method; allowing us to carry out the generation of realistic populations of cells and subsequent analysis of their intracellular fluxes and exchanges with the environment.With the development of single cell and micro-colony imaging experiments –38, 42, −1), and a dominant peak of fast-growing cells . Moreover, we find excellent agreement between experimental fluxomics data [Simulations of the steady-state growth rates attainable by the cells in our modeled populations gives rise to a distribution that is in excellent agreement with the experimentally observed growth rate distribution . In partics data  and the ics data , 41.13C experiments on wild-type yeast contained no amino acids in the media; our simulations showed that cells under these conditions depended on ammonium, sulfate, and phosphate salts taken up from the media. The gene knockouts that differentiated the the strains used in the 13C and SD experiments required the addition of uracil, leucine and histidine to the SD media. The SD media also contained an additional 17 amino acids, several of which were taken up and catabolized as a energy source. Despite the differences, both simulated populations displayed very similar growth rate distributions.The simulations presented here also allowed us to make quantitative predictions about the effects of growth in media where the main difference was the presence or absence of amino acids. The S. cerevisiae. One of the most important result of this study is that it underscores the need for imposing biologically realistic internal constraints in flux balance models. Without the types of constraints Population FBA imposes, the yeast 7.6 model gave fluxes, growth rates, and metabolic byproducts that were qualitatively and quantitatively inconsistent with the results of a 13C fluxomics study. Our study has shown that yeast populations exhibit the same types of cell-to-cell diversity in behavior that is coming to be recognized across the microbial world, and that although the particular sets of constraints that are necessary to recover the experimental growth rate distribution are not unique, any set that does recover the growth rate distribution also recovers the main metabolic behaviors we observed, including the Crabtree effect and the noted shift toward respiration seen among our fast-growing cells.We have employed our Population FBA methodology to study metabolic heterogeneity in S1 TextExtended figures and tables for further analysis and validation of experimental data, as well as further analysis of our results. The text also covers a more detailed explanation of our methods.(PDF)Click here for additional data file.S1 Filekcat values, final kcat values after doubling procedure in both SD and 13C media as well as scaling ratios to convert sampled SD protein count to 13C count. There is also listing of final kcat values obtained after 10 independent GA optimizations in SD media. Correlation matrix imposed while sampling distributions as well as its Cholesky decomposed factor is also provided.Model files contain all modifications to reflect gene deletions and media conditions. Parameter files with all shape and scale parameters for fluorescence distributions of proteins used in our simulations, along with their original (ZIP)Click here for additional data file.S2 Fileet. al. [Full parameter set acquired after removing all unreliable fluorescence distributions for protein abundances observed in Dénervaud et. al. , includi(ZIP)Click here for additional data file.S3 Filewww.escher.github.io, [JSON file for the metabolic model yeast 7.6 (y76Model.json) and corresponding metabolic map (y76Map.json) to be used with ESCHER ((ZIP)Click here for additional data file.


Synechocystis 6803 with growth kinetics, cell movements, and a light distribution function. The hybrid platform not only maps flux dynamics in cells of sub-populations but also predicts overall production titer and rate in PBRs.Genome-scale models (GSMs) are widely used to predict cyanobacterial phenotypes in photobioreactors (PBRs). However, stoichiometric GSMs mainly focus on fluxome that result in maximal yields. Cyanobacterial metabolism is controlled by both intracellular enzymes and photobioreactor conditions. To connect both intracellular and extracellular information and achieve a better understanding of PBRs productivities, this study integrates a genome-scale metabolic model of 2 mass transfer limitations. Second, fluxome in a single cyanobacterium may show stochastic changes due to random cell movements in PBRs. Third, insufficient light due to cell self-shading can activate the oxidative pentose phosphate pathway in subpopulation cells. Fourth, the model indicates that the removal of glycogen synthesis pathway may not improve cyanobacterial bio-production in large-size PBRs, because glycogen can support cell growth in the dark zones. Based on experimental data, the integrated GSM estimates that Synechocystis 6803 in shake flask conditions has a photosynthesis efficiency of ~2.7 %.Analysis of the integrated GSM demonstrates several results. First, cyanobacteria are capable of reaching high biomass concentration (>20 g/L in 21 days) in PBRs without light and COThe multiple-scale integrated GSM, which examines both intracellular and extracellular domains, can be used to predict production yield/rate/titer in large-size PBRs. More importantly, genetic engineering strategies predicted by a traditional GSM may work well only in optimal growth conditions. In contrast, the integrated GSM may reveal mutant physiologies in diverse bioreactor conditions, leading to the design of robust strains with high chances of success in industrial settings.The online version of this article (doi:10.1186/s12934-015-0396-0) contains supplementary material, which is available to authorized users. To enhance mass transfer, people often use CO2-enriched air in combination with high intensity mixing. Many models have been developed to understand how cyanobacterial physiological dynamics are influenced by the light intensity, CO2 supply, temperature, and geometry of PBRs  glucose and 4 g/L NaH13CO3 . The TBDMS (N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide) method ’ and ‘d-lactate [c] → d-lactate [external]’, which were respectively used to simulate glycogen storage/consumption and d-lactate production by an engineered cyanobacterial strain , and half-saturation constant for dissolved CO2, Km; f2 is a function of the cell’s local position l, biomass concentration X, and photon influx on the PBR surface vphoton,0. The linear optimization problem was solved by the MATLAB® (2012b) built-in function ‘linprog’ using the ‘simplex’ algorithm. To estimate the flux distribution in engineered cyanobacterial strains, we used the MOMA (minimization of metabolic adjustment) algorithm  represents dissolved CO2 concentration. This study assumed that pH was constant at 8.0, and that the dissolved CO2 and cell culture were homogeneous in PBRs. Because we assumed that cell metabolism was pseudo-steady in each interval  is the overall glycogen concentration in the PBR, in mmol/L; KLa is the mass transfer rate of CO2, in h−1; [CO2] represents the dissolved CO2 and HCO3− concentrations, in mmol/L; [CO2]* is the combined concentrations of dissolved CO2 and HCO3− in equilibrium with atmospheric CO2 , in mmol/L; and µapp, vglycogen,app, and v,appCO2 are fluxes determined previously. The updated values of the biomass concentration, dissolved CO2 concentration, etc., were then used to constrain the FBA model in the next interval. The kinetic parameters are given in Table KLa was determined by the following equation: [N is the rotation speed, in rpm; V is the shake flask volume, in mL; and L is the culture volume, in mL. MATLAB code of the integrated GSM is provided in Additional file The ODEs were resolved in their numerical discrete form (Euler-like integration scheme):mentclass2pt{minimquation: Table 1LiAdditional file 1. Flux distribution database.Additional file 2. Supplementary figures and tables.Additional file 3. MATLAB file for simulating cyanobacterial performance in PBRs.Additional file 4. Matrix of flux distribution database.


Escherichia coli. However, for several applications, smaller metabolic (core) models are needed. Using a recently introduced reduction algorithm and the latest E. coli genome-scale reconstruction iJO1366, we derived EColiCore2, a model of the central metabolism of E. coli. EColiCore2 is a subnetwork of iJO1366 and preserves predefined phenotypes including optimal growth on different substrates. The network comprises 486 metabolites and 499 reactions, is accessible for elementary-modes analysis and can, if required, be further compressed to a network with 82 reactions and 54 metabolites having an identical solution space as EColiCore2. A systematic comparison of EColiCore2 with its genome-scale parent model iJO1366 reveals that several key properties  of the central metabolism are preserved in EColiCore2 while it neglects redundancies along biosynthetic routes. We also compare calculated metabolic engineering strategies in both models and demonstrate, as a general result, how intervention strategies found in a core model allow the identification of valid strategies in a genome-scale model. Overall, EColiCore2 holds promise to become a reference model of E. coli’s central metabolism.Genome-scale metabolic modeling has become an invaluable tool to analyze properties and capabilities of metabolic networks and has been particularly successful for the model organism  Escherichia coli. The first E. coli genome-scale model, iJE660iJR904iAF1260iJO1366E. coli network reconstruction. Since extensive and reliable data on the biochemistry and genome is available for this model organism, the reconstructed genome-scale models turned out to be exceptionally accurate and proved to be invaluable to gain insights into the metabolism of E. coli or to identify intervention strategies for a targeted modification of the metabolism for biotechnological applications1Constraint-based and stoichiometric modeling has become a standard tool for analyzing key properties and capabilities of metabolic networks and numerous studies have demonstrated the power and wide applicability of this approach12456891011NetworkReducer, a network reduction algorithm that allows the derivation of stoichiometrically consistent submodels from genome-scale modelsNetworkReducer has been demonstrated before, the goal of the current contribution is to derive a widely applicable core model of the central metabolism of E. coli from a reference genome-scale network and to analyze its properties in relation to the genome-scale parent model.However, the increasing size and complexity of genome-scale models have set restrictions on the computational feasibility of some stoichiometric modeling techniques. For example, exhaustive elementary modes analysisE. coli have been used in the literature. However, almost all of them were derived in a bottom-up manner and are therefore not related to and consistent with a corresponding genome-scale model. One exception is the E. coli core model presented by Orth et al.EColiCore1, was manually derived from the genome-scale model iAF1260 for educational purposes. Whereas EColiCore1 perfectly fits the demands it was developed for, the stoichiometric properties are closely related but not fully consistent with its genome-scale parent model iAF1260NetworkReduceriAF1260 having the same scope as EColiCore1. However, even this core model has several drawbacks. First of all, it was not derived from the latest reference genome-scale model iJO1366. Secondly, it lacks some central metabolic pathways such as the Entner-Doudoroff and the methylglyoxal pathway. Furthermore, we wanted to protect a larger set of phenotypes in the core model including feasibility of growth on a selection of standard substrates as well as producibility of standard (fermentation) products by E. coli. One major goal of the present contribution was therefore to derive a stoichiometric model of the central metabolism of E. coli from iJO1366 that fulfills these requirements and therefore holds the potential to become a reference model of the central metabolism of E. coli accessible for all types of constrained-based modeling. Another central goal of this study is to systematically compare key properties of the derived core model, EColiCore2, with those from its genome-scale parent model iJO1366. This analysis, which is the first of this kind, includes a comparison of the solution space with respect to flux ranges, reaction essentialities, and production capabilities. We demonstrate that EColiCore2 is capable to describe several key properties of iJO1366 including those that were not explicitly accounted for. Generally, as intended, the derived core model reflects the degrees of freedom in the central metabolism of E. coli very well while neglecting redundancies along biosynthetic routes. A full enumeration of the complete set of elementary modes or of metabolic intervention strategies becomes possible in EColiCore2. Using ethanol as a relevant product, we also compare metabolic engineering intervention strategies in the core and the genome-scale model and demonstrate how the core model can even be used as a basis for the identification of metabolic engineering strategies with higher cardinalities in the genome-scale model that were computationally not tractable before.Several different small-scale models of the central metabolism of E. coli core model we used the recently introduced network reduction algorithm NetworkReducer which takes a given large-scale metabolic network as input and derives a smaller subnetwork while keeping desired features of the full network intactk is described by a corresponding set of linear inequalities:For deriving the s denoting the number of protected functions and phenotypes. In a first (pruning) step, NetworkReducer iteratively removes non-protected reactions and metabolites while checking that none of the protected functions are violated. Afterwards, an optional network compression step can be applied which, for example, lumps reaction sequences (unbranched pathways) into single reactions. This second operation further reduces the size of the network but does not change the space of feasible steady-state flux distributions. For further details on NetworkReducer we refer to the original workwith iJO1366 was used as the gold standard for a well-curated genome-scale stoichiometric network model for E. coli’s metabolism. We introduced some minor modifications to iJO1366 prior to the application of the network reduction routine:We added external metabolites for all reactions where compounds are secreted into or taken up from the environment. This allows a more convenient consideration of mass balances of those metabolites  but does not change the solution space.Further, we allowed reaction R_nadh16tpp (NADH dehydrogenase) to be reversible as otherwise succinate excretion is obligatory under anaerobic conditions  excretion became essential reactions because they are needed to consume formate and glyoxylate formed as byproducts of some synthesis pathways for biomass precursors. This behavior would not reflect reality. In fact, in the genome-scale model, there are different routes that consume these metabolites , however, these pathways would be lost in the reduction process. To avoid such an undesired behavior of the reduced model, we demanded that growth without formate excretion and malate synthase is possible and replaced metabolites M_glx_c (glyoxylate) and M_for_c (formate) in the reactions R_GLYCTO4, R_GLXCL, R_AMPMS2, R_DB4PS, R_FTHFLi, R_GTPCI and R_GTPCII2 by auxiliary metabolites M_glxBIO_c and B_forBIO_c, respectively. These changes reflect the assumption that consumption of glyoxylate and formate produced during biomass synthesis is realized via these reactions. Importantly, this modification does not produce new steady-state flux distributions, it only excludes those solutions that would render malate synthase and formate (or hydrogen) production essential.When applying the The latest reconstruction iJO1366 model will be denoted by ECGS  throughout the manuscript. Flux bounds were set as in the original model iJO1366, in particular, a maximal substrate uptake rate of 10 mmol/gDW/h was used (if not stated otherwise) and the ATP maintenance demand was set to 3.15 mmol/gDW/h. The flux range of all reactions with unknown capacities were set to  for reversible and  for irreversible reactions.For clarity, the slightly modified NetworkReducer. As the goal is to construct a core model that represents the central metabolism of E. coli in detail while neglecting redundancies in anabolic synthesis pathways we protected the reactions and metabolites involved in glycolysis, pentose phosphate pathway, Entner-Doudoroff pathway, tricarboxylic acid cycle, methylglyoxal pathway and key reactions of the electron transport chain. In addition, six major anaplerotic reactions (including the glyoxylate shunt) and two NAD(P) transhydrogenase reactions were protected. To account for biomass synthesis we demanded that the reaction R_Ec_biomass_iJO1366_core_53p95M of ECGS must be maintained. In total, 65 reactions and their reactants have been protected in the central metabolism as listed in the Next we specified the protected parts and phenotypes of the genome-scale model to be retained by NetworkReducer to protect not only parts but also behaviors or phenotypes. We therefore demand that the reduced network must allow for growth on four representative carbon substrates  with at least 98% of the maximal growth rate achievable with the ECGS model  are not included in this set. Here we exploit the possibility of GS model . This enGS model . For allCellNetAnalyzerEColiCore2) and the compressed pruned (EColiCore2comp) networks are provided in SBML format in CellNetAnalyzer projects on the repository website of CellNetAnalyzer (http://www2.mpi-magdeburg.mpg.de/projects/cna/repository.html).The network reduction and all other calculations were done with NetworkReducer to the iJO1366-based genome-scale model ECGS as described in the Methods section , we obtained EColiCore2 (in the following abbreviated by ECC2), a core model of E. coli’s metabolism where all the specified protected parts and phenotypes have been maintained. ECC2 contains 499 reactions (thereof 198 reversible) and 486 internal as well as 33 external metabolites  preserves major metabolic phenotypes. Therefore, we consider ECC2 as a qualified solution suitable for further in-depth analysis.We emphasize that network ECC2 is not the only possible solution and other subnetworks can solve the reduction problem equally well. However, by definition, all alternative networks share the set of protected parts (metabolites and reactions) and protected phenotypes. Further, by altering the order of removed reactions, we found that differences in alternative reduced networks occur mainly in alternative routes for the synthesis of biomass precursors. Our focus was to obtain a core model that (a) represents possibility of anaerobic production of lactate, succinate and ethanol was demanded, their maximal production rates correspond exactly to those of ECGS . The maximal product for formate, hydrogen, and acetate in ECC2 compare also well with what is known (and expected) from experimental observations, however, they are much higher in ECGS (iJO1366 for aerobic growth on glucose is 2.93 (but only 2.0 in ECC2). We refrained from demanding that ECC2 reproduces these extremely high product yields as they are not expected to be of relevance under physiological conditions .The demanded biomass yields for the different substrates are achieved by ECC2. For all substrates, the maximal biomass yields in ECC2 are even above 99.9% of the genome-scale maximum except for aerobic growth on acetate where 98.8% of the maximal biomass yield of ECGS is achieved . Regardi in ECGS . For exaNetworkReducer to ECC2 generates an even more reduced network, ECC2comp, where coupled reactions  are lumped into overall reactions. The lumped reactions can be classified into two different categories. The vast majority (399) are reactions that are fully coupled (form an enzyme subset) with the biomass synthesis reaction. In addition, 21 reactions of the ECC2 model involved in chains of transport reactions (secreting metabolites from the cytosol via periplasm to the extracellular compartment) have been lumped as well. The compression thus further reduced the number of reactions and metabolites down to 79 reactions and 61 internal and 33 external metabolites. Initially, ECC2comp contained 25 degrees of freedom as ECC2, however, for better handling, we split up the reversible exchange reactions for carbon dioxide (CO2), acetate, and succinate into two irreversible (uptake and excretion) reactions respectively thereby increasing the number of reactions to 82 and the degrees of freedom to 28  and biomass byproducts compared to ECC2 and ECGS. However, the adjusted stoichiometric coefficients and the new precursors reflect the metabolites and their amount needed to produce all the building blocks contained in the original biomass synthesis reaction of ECC2 and ECGS . The growth rate was fixed to its near optimal value (0.98 h−1) and for all remaining reactions the minimal and maximal flux values were calculated for both networks. With ECC2 being a subnetwork of ECGS, it follows that the flux range of a reaction in ECC2 will be contained in the flux range of this reaction in ECGS. For 290 out of the 499 reactions in ECC2 (58.1%) a (close to) perfect agreement with the corresponding flux ranges in ECGS can be observed (difference less than 0.1 mmol/gDW/h) and for additional 153 reactions (30.7%) a good agreement can be found   . For onlmax) of the mutant is still above 95% of the wild type μmax. If μmax is reduced by more than 5% but growth still possible with more than 5% of the wild type μmax then the reaction is considered growth-limiting but not essential. Finally, if μmax falls below 5% of the maximal growth rate in the wild type, the reaction is considered to be lethal.Reaction essentialities for growth represent another important functional network property. We focused on reaction essentialities for growth on glucose. Separately for each reaction we fixed the rate to zero and maximized the growth rate. A reaction knockout is considered to be without significant effect if the maximal possible growth rate . For each internal metabolite a corresponding excretion reaction was temporarily introduced and then maximized.2 fixation would be conducted by E. coli which is unlikely as such a behavior has, to our knowledge, not been reported yet for these metabolites.Similar as for reaction essentialities, the maximal yield of any metabolite in ECC2 can never be greater than in ECGS since all feasible flux distributions in ECC2 are also possible in ECGS. For the vast majority of metabolites 77.8%,  we foundProduction envelopes (PEs) are projections of the solution space of feasible steady-state flux distributions on a two-dimensional plane. The growth rate is typically projected on the x-axis and a selected product synthesis (excretion) rate on the y-axis. PEs can be computed by means of sequential FVA where the growth rate is incremented in discrete steps from its minimum to its maximum and the minimal and maximal product synthesis rate is computed for each step. It should be noted again that, since ECC2 is a sub-network of ECGS, any PE of ECC2 will be contained in the corresponding PE of ECGS.2 release under aerobic conditions. It can be seen that the maximal growth and CO2 production rates are well represented by the reduced model while some deviations occur for the lower boundaries of CO2 release. For example, while the genome-scale model ECGS allows for CO2 fixation with acetate and succinate as substrates this behavior is not reflected by ECC2. The second and third row show the PEs for growth versus synthesis rates of typical fermentation products with glucose as substrate under any and under exclusively anaerobic conditions, respectively. Except for acetate, the PEs are either identical or, for the case of ethanol production under anaerobic conditions, show a large overlap confirming that the maximal yields of those products in ECGS are also reachable by ECC2. As was already mentioned above, whether the predicted maximal acetate yield with ECGS of almost 3 mol acetate per mole glucose is a realistic value remains questionable. In particular, homoacetic acid fermentation in E. coli (without incorporation of heterologous pathways) as predicted by ECGS has, to our knowledge, not been observed so far. Therefore, we did not protect this property in ECC2 and consider this deviation rather as correction than as restriction. The last row in A selection of representative PEs is given in As one goal of the whole reduction process, with the reduced degrees of freedom in ECC2 and ECC2comp, these models become accessible for the enumeration and subsequent analysis of elementary modes (EM). In the following we will discuss the EM of ECC2 keeping in mind that, as already mentioned, the EM are equivalent for both networks  and storage of EM from ECC2comp will require less than 20% of the memory needed to store the EM of ECC2 (82 vs. 499 reactions). A comparison of the set of EM from ECC2 and ECGS is not possible. So far, the computation of EM for genome-scale models is limited to approaches that compute the shortest EMIn the following we want to study how metabolic engineering strategies identified in the ECC2 model relate to the corresponding intervention strategies computed in ECGS. As a relevant product we chose ethanol which has previously been used as a “benchmark” case in other studies279293027−1.Here we consider production of ethanol from glucose under anaerobic conditions (oxygen uptake rate set to zero). The maximal glucose uptake rate was increased to 18.5 mmol/gDW/h to reflect the higher glucose conversion rates under anaerobic conditions. We excluded pseudo (transport and excretion) reactions from the list of possible knockout targets leaving only reactions that can be targeted by genetic modifications as knockout candidates. We designed the MCS problem as follows: the engineering goal with respect to product formation was formulated by demanding a minimal product yield of 1.4 mol ethanol per mol glucose consumed while growth should be feasible with a minimal rate of 0.05 hFor these constraints, we calculated the complete set of MCS with up to ten reaction deletions for both models, ECGS and ECC2. In total, we found 2176 MCS in ECC2 and 44066 MCS in the genome-scale network ECGS . Each MCECC2 and a cut set of ECGS with MECGS. We first consider the case that we are given one specific MCS of ECC2 (denoted by M*ECC2) and classify each single MECGS with respect to M*ECC2 , hence, the knockouts of M*ECC2 are also sufficient in ECGS to obtain the desired phenotype. An MCS of ECGS belongs to subclass A.2 if it contains all reactions of M*ECC2 complemented by further targets exclusively contained in the ECGS (and not in the ECC2) network to block additional unfavorable pathways in ECGS. Importantly, if the given M*ECC2 is not a valid MCS in ECGS (no MECGS of type A.1 exists for M*ECC2), then some MECGS of type A.2 must exist: knocking out the targets from M*ECC2 and all reactions from ECGS that are not contained in ECC2 (as they were deleted during the reduction process) will lead to a solution space in ECGS that is identical to the one obtained when deleting only M*ECC2 in ECC2. Clearly, not all the reactions exclusively contained in ECGS need normally be deleted to block flux distributions feasible in ECGS but not in ECC2; a minimal set of reaction deletions will be sufficient which is then combined with M*ECC2. We can thus conclude that any MCS of ECCS, that is not a cut set in ECGS, can always be extended to a valid MCS of type A.2 in ECGS. Hence, for a given M*ECC2, there is always either one MECGS of type A.1 or one or several MECGS of type A.2. The other two possible subclasses A.3 and A.4 arise if M*ECC2 is complemented by deletions of at least one additional reaction from ECC2 either without  or may not (B.1) be combined with deletions of reactions exclusively contained in the ECGS network.Cut sets in ECGS which are not supersets of the given Mgiven cut set M#ECC2 in ECC2 with respect to each cut set in ECGS. Every cut set of ECGS belongs then to exactly one subclass. Notably, for different cut sets from ECC2, a certain cut set M#ECGS can be of class A or B. If it is of type A.1 or A.2 for one particular M*ECC2, then it cannot be of class A for any other MCS of ECC2 . If it is of type A.3 or A.4 for M*ECC2 then it can be of type A.3 or A.4 for other MCS of ECC2 (but it cannot change from A.3 to A.4 or vice versa). Of special interest are MCS of ECGS that are exclusively of type B for all MCS of ECC2 and thus not a superset (extension) of any M*ECC2. Those cut sets of ECGS involve deletion patterns in the ECC2 part that were not allowed in cut sets of ECC2 because they would violate some of the specified desired phenotypes, for example, if some of the reactions knocked-out would be essential for growth or product synthesis in ECC2 (but not in ECGS). For this reason, such MCS in ECGS may, in the extreme case, comprise fewer interventions than the smallest MCS in ECC2 . All relationships between cut sets discussed above hold true not only for ECGS and ECC2 but for any pair of a (full) metabolic network and a subnetwork of it.So far we discussed the relationships between one must exist in ECGS) will have at least 11 reaction deletions. 12 MCS of ECGS are of type A.3 and thus extensions of MCS from ECC2 involving additional reaction deletions only in the ECC2 part. Finally, 1,070 MCS of ECGS establish relationships of type A.4. All found 1,082 MCS in ECGS of class A are supersets of 18 distinct MCS of ECC2. For each of the 4 MCS in ECC2 with cardinality 5 there exist MCS in network ECGS with 9 reaction deletions which include all deletions of the respective MCS of network ECC2 and additional deletions. Further, for 14 MCS in ECC2 with cardinality 6 there exist superset MCS in ECGS with two to four additional reaction deletions. For the remaining 2,154 MCS of ECC2, no supersets have been found among the calculated MCS in network ECGS. Since, as explained above, MCS of type A.1 or A.2 must exist, this indicates that the corresponding MCS in ECGS have cardinalities above ten. Lastly, 44,066 − 1,082 = 42,984 of the MCS of cardinality up to ten in ECGS are not a superset of any MCS in ECC2, hence, are exclusively of type B.1 (221) or B.2 (42763).We analyzed the relationships between the calculated MCS in ECC2 and ECGS for the ethanol case study  with restop-down approach, we could initially combine the knockouts of the seed MCS with knockouts of all reactions from ECGS that are not contained in ECC2. As already discussed, this would result in a valid but, most likely, non-minimal cut set. A constrained minimal cut set for ECGS can afterwards be found by iteratively removing knockout candidates from this cut set and validating a posteriori if the undesired phenotype is still blocked. If the removal of a reaction from the cut set violates the target constraints this reaction is reinserted and the cut set is eventually identified as a valid MCS if no further reaction can be removed.The fact that any MCS found in the reduced network ECC2 will be a subset of one or several corresponding MCS in the full network ECGS can be used to construct MCS for the genome-scale model. The strategy is to augment a “seed” MCS from ECC2 by additional knockouts to obtain valid MCS for ECGS. Although all cut sets of type A.2, A.3, and A.4  could, iseed of reaction knockouts for ECGS and the corresponding reactions are inactivated in the ECGS model. Subsequently, all MCS in this model are computed but allowing knockouts only in reactions contained in the ECGS but not in the ECC2 model. By excluding all reactions from ECC2 as knockout candidates for ECGS, the remaining search space is considerably reduced (here by 499 reactions). The union of the seed MCS together with the MCS found in the ECGS network yields a valid MCS for the full ECGS network. In principle, this procedure can be used to determine all MCS in ECGS of type A.2 (and MCS of type A.1 can be easily identified beforehand).A “bottom-up” or “expansion” approach would tackle the problem of finding smallest MCS in a more directed manner: Again, a MCS of ECC2 is used as a We pursued the expansion approach to identify additional knockout strategies for ECGS based on the MCS from ECC2. Each of the 2176 MCS of ECC2 was used as seed in ECGS and corresponding MCS for ECGS with up to 15 additional reaction deletions were identified. Since the (up to 10) reactions of the respective seed MCS from ECC2 are inactivated prior to the computation of corresponding superset MCS in ECGS, the computed MCS can contain up to 25 interventions. The results of these calculations can be summarized as follows. For 1020 (of the 2176) seed MCS from ECC2 we found at least one MCS in ECGS with maximal 15 additional reaction deletions. For 1156 seeds no MCS could be identified in ECGS indicating that the allowed 15 reaction deletions do still not suffice to obtain valid MCS for network ECGS. If the MCS of ECC2 with cardinality 5 are used as seeds, at least 8 additional knockouts (of reactions only contained in ECGS) were necessary to achieve a valid MCS in ECGS . However, the smallest MCS results from seeds of cardinality 6 where it is sufficient to delete 5 additional reactions resulting in valid MCS for network ECGS with cardinality 11. Recall here that we predicted before that the smallest MCS of type A.2 would need at least 11 reaction deletions as no MCS of this type A.2 exist up to cardinality 10. The biggest MCS found in this way for network ECGS had as much as 23 reaction deletions: 8 from the seeds and 15 additional knockouts. Again, all additional knockouts were restricted to ECGS reactions not contained in ECC2.We exemplarily discuss specific knockout strategies that have been identified by the different approaches. A specific MCS identified in the reduced network ECC2 suggests deletion of the six reactions i) acetate kinase (R_ACK), ii) phosphogluconate dehydrogenase (R_GND), iii) hexokinase (R_HEX1), iv) D-lactate dehydrogenase (R_LDH_D), v) pyruvate formate lyase (R_PFL), and vi) glucose-6-phosphate isomerase (R_PGI) (we denote this MCS by M1). This knockout set is a valid MCS in ECC2 but does not yet enforce the desired phenotypic behavior (coupling of growth with ethanol synthesis) in network ECGS. However, by enumerating MCS up to size ten in ECGS we found a valid MCS of ECGS that extends M1 with two further reaction deletions, namely acetaldehyde dehydrogenase (R_ACALD) and either xylose isomerase (R_XYLI2) or hexokinase (HEX7). Reaction R_ACALD is contained in network ECC2 whereas reactions R_XYLI2 and R_HEX7 are only part of ECGS. These two MCS are thus of type A.4 and cannot be found with the described expansion approach starting with M1 as a seed in ECGS. However, if we used the above MCS M1 from ECC2 as a seed in ECGS and allow only reactions from ECGS that are not contained in network ECC2 as knockout candidates, at least six additional reaction knockouts are necessary to obtain a valid MCS. For example, additional deletions of R_XYLI2 or HEX7 together with NAD dependent aldehyde dehydrogenase (R_ALDD2x), NADP dependent aldehyde dehydrogenase (R_ALDD2y), citrate lyase (R_CITL), gluconokinase (R_GNK) and acetyl-CoA:hexanoate-CoA transferase (R_HXCT) result in a valid MCS for ECGS and is found by our procedure. These knockout strategies reveal that the deletion of R_ACALD (in the ECC2 subnetwork) is equivalent to the knockout of R_ALDD2x, R_ALDD2y, R_CITL, R_GNK and R_HXCT in the ECGS network.adhE)) was identified as a knockout candidate in several MCS in ECGS but never in ECC2 (where it is the only pathway to produce ethanol). Some alternative ethanol synthesis pathways must therefore exist in ECGS that, with the suggested knockout strategies, would lead to a high ethanol yield. In fact, in ECGS, acetaldehyde can be derived from acetyl-CoA and glycine that are condensed to L-2-Amino-3-oxobutonate (R_GLYAT) which is then reduced to L-allo-threonine via reaction R_ATHRDHr and finally split to acetaldehyde and glycine by R_THRA2i. However, since the acetaldehyde dehydrogenase (adhE) is part of the main route for ethanol production in E. coli and since adhE knockout mutants are known to show no relevant ethanol excretion at all32Generally, elimination of the R_ACALD reaction  MCS in ECGS require a huge and unrealistic number of knockouts or if a calculation of (large sets of) MCS for a given intervention problem is not feasible at all in the ECGS model, it would thus be practical to start with a MCS calculated in the core network ECC2. If experimental implementation of all knockouts contained in the MCS of ECC2 is not sufficient, one may use our expansion algorithm to expand the seed MCS from the ECC2 model to obtain a valid MCS for the ECGS model (whose existence is guaranteed).To summarize, our case study showed that metabolic intervention strategies can be exhaustively enumerated in the reduced network ECC2. The MCS found in the ECC2 network for enhancing ethanol synthesis show significant overlap with the smallest MCS identifiable in ECGS. Discrepancies mainly arise due to (a) additional pathways in ECGS having a zero or low ethanol yield (which must be deleted in ECGS) and (b) additional high-yield ethanol synthesis pathways (which give rise to new intervention strategies in ECGS). For a product of the central metabolism such as ethanol, we expect that a large number of MCS found in ECC2 are sufficient to reach the desired production phenotype. In fact, successful E. coli that is stoichiometrically consistent to and maintains several key properties of the most recent and well-curated genome-scale model of this organism (iJO1366). In addition, for the first time, we systematically compared key properties of the derived metabolic core model and of its genome-scale parent model.The central goal of this contribution was the development of a reference metabolic core model of the central metabolism of NetworkReducer algorithm together with a list of protected parts and phenotypes. This list was carefully compiled to ensure that the core model reflects the degrees of freedom in the central metabolism of E. coli while it was allowed to neglect redundancy along biosynthetic routes. The derived EColiCore2 network is a subnetwork of iJO1366 still comprising 499 reactions and 486 internal metabolites. However, ECC2 is small enough for detailed studies that would not be possible in the genome-scale model. In particular, all elementary modes for growth on the included substrates can be calculated on a standard personal computer in reasonable time. In addition, by compressing (mainly biosynthetic) linear reaction chains to condensed overall reactions a compressed network version of ECC2, ECC2comp, was derived which contains 82 reactions and 54 internal metabolites. The solution space of ECC2comp is equivalent to ECC2 since every steady state flux distribution in ECC2comp can be uniquely mapped to one of ECC2 and vice versa. ECC2comp might be more practical and efficient for some applications. For example, less working memory is required for sophisticated calculations. Further, the reduced number of reactions and metabolites allows for a clear and concise graphical representation of the whole network . These examples demonstrate the trade-off in analyzing a core vs. a full genome-scale network: unrealistic or overoptimistic behaviors represented in the genome-scale model  can be excluded in a core model, however, unknown and non-intuitive pathways of possibly higher relevance might be missed when focusing only on the well-examined part of the metabolism.We performed a comprehensive analysis and comparison of different properties of ECC2 and its genome-scale parent model ECGS to demonstrate that ECC2 is a reasonable reduction of ECGS. ECC2 preserves key phenotypic behaviors and properties and allows for the analysis of major metabolic fluxes and the elucidation of engineering strategies in the central metabolism of We also studied the value of the core model for calculating relevant metabolic engineering strategies by means of a case study with ethanol as product of interest. As expected, cut sets found in ECC2 for overproduction of ethanol are usually not sufficient to completely block all potentially undesired phenotypes (with low product yield) in ECGS but already indicate key targets (and combinations thereof) which are also relevant for ECGS. Analysis of the smallest cut sets in ECGS showed that non-intuitive intervention strategies may exist in the genome-scale model which either target additional unfavorable routes that were not contained in the ECC2 model or/and redirect carbon flux to hidden (unknown) product synthesis pathways in the full network. While the cut sets of the ECGS model are complete and ensure that the desired phenotypes are really achieved in the full model, the relevance of some of the found alternative targets  and pathways is questionable. Generally, one can expect that intervention strategies calculated in ECC2 for products of the central metabolism  will have a better predictivity (most relevant pathways for their production are contained in the model) than those calculated for products of the anabolism where relevant - favorable and unfavorable - pathways might have been removed in the pruning step.We further clarified the relationships between cuts sets of the full and of a subnetwork. With respect to a given subnetwork (here ECC2) one can distinguish two major classes of cut sets in the full (ECGS) model: a cut set in ECGS is either a superset of one  cut sets found in ECC2 , or it is not a superset of any cut set in ECC2 and therefore involves other deletion patterns in the ECC2 subnetwork possibly combined with further knockouts in reactions exclusively contained in ECGS. Whereas cut sets of the latter type can therefore not be obtained or deduced from ECC2, we demonstrated that cut sets in ECGS that are supersets of cut sets from ECC2 can always be obtained by expanding seed cut sets in ECC2. Thus, ECC2 can initially be used to find intervention strategies leading to desired phenotypes in the core model. If a particular cut set was experimentally not successful (the mutant shows still an unfavorable phenotype in the experiment) or if one is interested to determine (some or more) full intervention strategies in the ECGS model, the respective knockout strategy of ECC2 can be used as a seed and then be extended by further knockouts to a valid MCS for ECGS. We demonstrated that this approach can be used to determine thousands of additional valid MCS for ECGS with higher cardinalities which could not be calculated before.E. coli are well reflected in the reduced model.A major advantage of ECC2 is that it is a strict submodel of its genome-scale parent model ECGS so that results from ECC2 can be directly related to ECGS. In fact, this property was used to determine cut sets in the ECGS model from seed cut sets calculated in ECC2. Furthermore, all flux distributions in ECC2 are valid solutions in ECGS and, likewise, all EM of ECC2 are equally valid in ECGS. Hence, analyzing ECC2 means analyzing a subset of the feasible phenotypes of ECGS and ECC2 was derived in such a way that metabolic fluxes in the central metabolism of As desired, ECC2 allows for the full enumeration of EM and metabolic engineering strategies and it is readily usable for educational purposes due to its appropriate scope, size, and clarity. If certain pathways or/and metabolites contained in ECGS but not in the ECC2 model become of interest in a particular application, they might be added to the core model which should still keep the model size low enough to perform detailed calculations (e.g. EM enumeration). Likewise, if one wants to protect other phenotypes from the genome-scale model, the reduction process can be repeated with adapted constraints.E. coli, similar to its well-curated and evolved genome-scale parent model iJO1366.With the results presented in this study, we believe that ECC2 holds the potential to become a reference metabolic model for the central metabolism of How to cite this article: Hädicke, O. and Klamt, S. EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model. Sci. Rep.7, 39647; doi: 10.1038/srep39647 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


The physiology of cells, determined by all extracellular production and consumption rates, intracellular reaction rates and the growth rate, is of fundamental interest in such different fields of the life sciences as microbiology, biotechnology and medical applications. In this Crystal Ball, I emphasize the importance of metabolic reaction rates, that is fluxes, for our understanding of metabolic network operation and envisage a time in which fluxes can be changed at will. Indeed, the ultimate goal of many fields of the life sciences is to understand cell physiology and hence the flux distribution, which characterizes the metabolic state of a cell. Based on this information, it will be possible to design strategies, for example, to avoid certain phenotypes (applications in infection biology and cancer research) or to redirect fluxes .I argue that we are now in the lucky position that we can measure extra‐ and intracellular fluxes, that we have well‐characterized genetic tools for changing enzyme activities, and that we have fermentation capabilities to maintain optimal environmental conditions for a microorganism. Rationally changing a flux, however, requires a solid understanding of how the interplay of all mentioned aspects (and more) result in the observed flux distributions.Before describing some of these developments, we have to agree on the importance of the goal to precisely manipulate cellular fluxes. In many research projects, fluxes are in fact ignored, despite the ultimate goal implicitly being to understand metabolic network operation. For example, complex interactions between signalling pathways is often studied while ignoring cell physiology, although many cellular processes are growth rate dependent. And in industrial biotechnology, often the titre of the product of interest is communicated as this value quantifies overall performance of the biocatalytic reaction, implicitly even the product production rate (i.e. the flux towards the product) assuming a defined experimental time. Of course, when the fermentation is reported in the context of the entire process, titre is central; as it considers physical parameters including product solubility, the yield of the product on a given substrate and product inhibition of the biocatalyst besides other aspects. However, this is not the only critical value relevant to the process.−1) and not specific rates normalized to the cell dry weight used (e.g. gproduct per gCDW per hour) are reported. When manipulating enzyme activities (e.g. using a stronger promoter or a more active enzyme variant), one implicitly aims for a change in flux at a given network node; the subsequent outcome of this flux change being a change in product concentration. A focus on end‐point assays potentially misses interesting clones that show a significant improvement in production rate, hence achieve the maximal titre long before the end‐point. However, there currently is no facile method to observe cellular fluxes on a broad scale.In Metabolic Engineering, we propose to execute rational strain engineering, with the performance of the  cell in focus. Still, often end‐point concentrations  metabolism may be simple or almost impossible, depending on the cell and environment studied. Pathway redundancy and the lack of experimental measurements, which could be otherwise used to close metabolite mass balances might hamper identifiability of particular fluxes. For example, the flux through glycolysis in lactic acid bacteria (a linear pathway from glucose to lactate) can be resolved by simple flux balancing, while aerobic glucose metabolism in http://seva.cnb.csic.es/) give an indication of the effort of the research community. One has not to be too optimistic to state that we are slowly but surely entering the area of writing the genetic code, as compared to only reading it.With the concepts of Synthetic Biology, orthogonality, modularity, but foremost standardization, we can finally progress towards truly rational strain engineering. The rate of transcription and translation can be changed significantly and – importantly – in a generally predictable manner. For example, the affinity of the ribosome via the 16S rRNA to ribosome binding sites was computed, promoters characterized in detail, and other genetic elements were introduced to deliver standardized tools that facilitate flux rerouting . While many strategies including transporter overexpression failed, 15 years ago Peter Ruhdal Jensen and colleagues reported an E. coli with a 1.7‐fold increased glucose uptake rate  in Pseudomonas putida , but notably produces only biomass and CO2, with no ‘overflow’ metabolites encountered  and activated beta‐OH‐fatty acids (i.e. ACP‐fatty acid) resulted in a strain with 55% theoretical yield of rhamnolipid on sugar, a flux increase of 300% in the dTDP‐rhamnose pathway, and 50% increase in de novo fatty acid synthesis.The many aspects that ultimately define the flux distribution in a given metabolic network are so complex that considerable effort is still needed to achieve, for example, a significantly increased glucose uptake rate (actually a good idea in many whole‐cell catalysts, as the substrate uptake rate determines the maximal achievable specific flux towards the product and influences the space time yield, e.g. gConsidering fluxes in many more biotechnological projects and even life science research in general will certainly yield great rewards, and many surprises are waiting for us. We have many of the tools at hand, and are developing many more; now, we must use them to their full effect to illuminate metabolism.