Complete Metabolic Network Research Articles

EtiBsu1209: A comprehensive multiscale metabolic model for Bacillus subtilis.

Genome-scale metabolic models (GEMs) have been widely used to guide the computational design of microbial cell factories, and to date, seven GEMs have been reported for Bacillus subtilis, a model gram-positive microorganism widely used in bioproduction of functional nutraceuticals and food ingredients. However, none of them are widely used because they often lead to erroneous predictions due to their low predictive power and lack of information on regulatory mechanisms. In this work, we constructed a new version of GEM for B. subtilis (iBsu1209), which contains 1209 genes, 1595 metabolites, and 1948 reactions. We applied machine learning to fill gaps, which formed a relatively complete metabolic network able to predict with high accuracy (89.3%) the growth of 1209 mutants under 12 different culture conditions. In addition, we developed a visualization and code-free software, Model Tool, for multiconstraints model reconstruction and analysis. We used this software to construct etiBsu1209, a multiscale model that integrates enzymatic constraints, thermodynamic constraints, and transcriptional regulatory networks. Furthermore, we used etiBsu1209 to guide a metabolic engineering strategy (knocking out fabI and yfkN genes) for the overproduction of nutraceutical menaquinone-7, and the titer increased to 153.94 mg/L, 2.2-times that of the parental strain. To the best of our knowledge, etiBsu1209 is the first comprehensive multiscale model for B. subtilis and can serve as a solid basis for rational computational design of B. subtilis cell factories for bioproduction.

Hierarchical Harmonization of Atom-Resolved Metabolic Reactions across Metabolic Databases.

Metabolic models have been proven to be useful tools in system biology and have been successfully applied to various research fields in a wide range of organisms. A relatively complete metabolic network is a prerequisite for deriving reliable metabolic models. The first step in constructing metabolic network is to harmonize compounds and reactions across different metabolic databases. However, effectively integrating data from various sources still remains a big challenge. Incomplete and inconsistent atomistic details in compound representations across databases is a very important limiting factor. Here, we optimized a subgraph isomorphism detection algorithm to validate generic compound pairs. Moreover, we defined a set of harmonization relationship types between compounds to deal with inconsistent chemical details while successfully capturing atom-level characteristics, enabling a more complete enabling compound harmonization across metabolic databases. In total, 15,704 compound pairs across KEGG (Kyoto Encyclopedia of Genes and Genomes) and MetaCyc databases were detected. Furthermore, utilizing the classification of compound pairs and EC (Enzyme Commission) numbers of reactions, we established hierarchical relationships between metabolic reactions, enabling the harmonization of 3856 reaction pairs. In addition, we created and used atom-specific identifiers to evaluate the consistency of atom mappings within and between harmonized reactions, detecting some consistency issues between the reaction and compound descriptions in these metabolic databases.

Dynamic metabolic network modeling of mammalian Chinese hamster ovary (CHO) cell cultures with continuous phase kinetics transitions

Cellular biological environments are difficult to analyse on the first principle basis. In this modeling work, a novel approach is presented which transits from a broad mechanistic description of the individual metabolic pathways to a simplified macro-kinetic model. The latter is characterized within the concept of metabolic flux analysis and associated elementary modes, which obey the constraints of phenotype. Unlike the piecewise-defined kinetic expressions with a discrete separate treatment of different cell phases, the Monod equation rate law is extended herein, allowing for a dynamic reversal of macro-reactions, and thus, a uniform continuous functionality from growth to death cultivation periods. The enzymatic biochemical reactions of complete metabolic network are defined to satisfy the composition of mammalian culture structure which reflects on stoichiometry. This affords us an opportunity to handle the density of the viable cells (biomass) along with central metabolisms’ transformations, rather than as a decoupled constituent function, as habitually assumed in the literature. The methodology of the extracellular metabolite measurements for the Chinese hamster ovary (CHO) cells is confronted, confirming the applicability in multi-scale biologically-relevant systems. Temporal species’ behavior reveals inflection points that switch among stages. In further extensions metabolic models have a potential to forecast the outcomes of growth medium perturbations, feeding protocols, and process parameters in order to facilitate the productivity and quality of biosimilars in biotechnology.

Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks.

BackgroundThe emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network.ResultsWe propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests, which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach.ConclusionThe exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of compounds. By considering the best combination of both graph-based and flux-based techniques, the Conquests python package advocates for a broader use of these compounds both to facilitate network curation and to promote a precise understanding of metabolic phenotype.

Sucrose metabolism in halotolerant methanotroph Methylomicrobium alcaliphilum 20Z.

Sucrose accumulation has been observed in some methylotrophic bacteria utilizing methane, methanol, or methylated amines as a carbon and energy source. In this work, we have investigated the biochemical pathways for sucrose metabolism in the model halotolerant methanotroph Methylomicrobium alcaliphilum 20Z. The genes encoding sucrose-phosphate synthase (Sps), sucrose-phosphate phosphatase (Spp), fructokinase (FruK), and amylosucrase (Ams) were co-transcribed and displayed similar expression levels. Functional Spp and Ams were purified after heterologous expression in Escherichia coli. Recombinant Spp exhibited high affinity for sucrose-6-phosphate and stayed active at very high levels of sucrose (K i =1.0±0.6M). The recombinant amylosucrase obeyed the classical Michaelis-Menten kinetics in the reactions of sucrose hydrolysis and transglycosylation. As a result, the complete metabolic network for sucrose biosynthesis and re-utilization in the non-phototrophic organism was reconstructed for the first time. Comparative genomic studies revealed analogous gene clusters in various Proteobacteria, thus indicating that the ability to produce and metabolize sucrose is widespread among prokaryotes.

Fermentation of oxidized hexose derivatives by Clostridium acetobutylicum.

BackgroundClostridium acetobutylicum fermentations are promising for production of commodity chemicals from heterogeneous biomass due to the wide range of substrates the organism can metabolize. Much work has been done to elucidate the pathways for utilization of aldoses, but little is known about metabolism of more oxidized substrates. Two oxidized hexose derivatives, gluconate and galacturonate, are present in low cost feedstocks, and their metabolism will contribute to overall metabolic output of these substrates.ResultsA complete metabolic network for glucose, gluconate, and galacturonate utilization was generated using online databases, previous studies, genomic context, and experimental data. Gluconate appears to be metabolized via the Entner-Doudoroff pathway, and is likely dehydrated to 2-keto-3-deoxy-gluconate before phosphorylation to 2-keto-3-deoxy-6-P-gluconate. Galacturonate appears to be processed via the Ashwell pathway, converging on a common metabolite for gluconate and galacturonate metabolism, 2-keto-3-deoxygluconate. As expected, increasingly oxidized substrates resulted in increasingly oxidized products with galacturonate fermentations being nearly homoacetic. Calculations of expected ATP and reducing equivalent yields and experimental data suggested galacturonate fermentations were reductant limited. Galacturonate fermentation was incomplete, which was not due solely to product inhibition or the inability to utilize low concentrations of galacturonate. Removal of H2 and CO2 by agitation resulted in faster growth, higher cell densities, formation of relatively more oxidized products, and higher product yields for cultures grown on glucose or gluconate. In contrast, cells grown on galacturonate showed reduced growth rates upon agitation, which was likely due to loss in reductant in the form of H2. The growth advantage seen on agitated glucose or gluconate cultures could not be solely attributed to improved ATP economics, thereby indicating other factors are also important.ConclusionsThe metabolic network presented in this work should facilitate similar reconstructions in other organisms, and provides a further understanding of the pathways involved in metabolism of oxidized feedstocks and carbohydrate mixtures. The nearly homoacetic fermentation during growth on galacturonate indicates further optimization of this and related organisms could provide a route to an effective biologically derived acetic acid production platform. Furthermore, the pathways could be targeted to decrease production of undesirable products during fermentations of heterogeneous biomass.

Fermentation of oxidized hexose derivatives by Clostridium acetobutylicum

Clostridium acetobutylicum fermentations are promising for production of commodity chemicals from heterogeneous biomass due to the wide range of substrates the organism can metabolize. Much work has been done to elucidate the pathways for utilization of aldoses, but little is known about metabolism of more oxidized substrates. Two oxidized hexose derivatives, gluconate and galacturonate, are present in low cost feedstocks, and their metabolism will contribute to overall metabolic output of these substrates. A complete metabolic network for glucose, gluconate, and galacturonate utilization was generated using online databases, previous studies, genomic context, and experimental data. Gluconate appears to be metabolized via the Entner-Doudoroff pathway, and is likely dehydrated to 2-keto-3-deoxy-gluconate before phosphorylation to 2-keto-3-deoxy-6-P-gluconate. Galacturonate appears to be processed via the Ashwell pathway, converging on a common metabolite for gluconate and galacturonate metabolism, 2-keto-3-deoxygluconate. As expected, increasingly oxidized substrates resulted in increasingly oxidized products with galacturonate fermentations being nearly homoacetic. Calculations of expected ATP and reducing equivalent yields and experimental data suggested galacturonate fermentations were reductant limited. Galacturonate fermentation was incomplete, which was not due solely to product inhibition or the inability to utilize low concentrations of galacturonate. Removal of H2 and CO2 by agitation resulted in faster growth, higher cell densities, formation of relatively more oxidized products, and higher product yields for cultures grown on glucose or gluconate. In contrast, cells grown on galacturonate showed reduced growth rates upon agitation, which was likely due to loss in reductant in the form of H2. The growth advantage seen on agitated glucose or gluconate cultures could not be solely attributed to improved ATP economics, thereby indicating other factors are also important. The metabolic network presented in this work should facilitate similar reconstructions in other organisms, and provides a further understanding of the pathways involved in metabolism of oxidized feedstocks and carbohydrate mixtures. The nearly homoacetic fermentation during growth on galacturonate indicates further optimization of this and related organisms could provide a route to an effective biologically derived acetic acid production platform. Furthermore, the pathways could be targeted to decrease production of undesirable products during fermentations of heterogeneous biomass.

COMPARATIVE ANALYSIS OF METABOLIC NETWORKS IN MESOPHILIC AND THERMOPHILIC ARCHAEA METHANOGENS BASED ON MODULARITY

Metabolic networks are useful representations of the metabolic capabilities of cells. A comparison of metabolic networks across species is essential to better understand how evolutionary pressures shape these networks. By comparing the set of reactions that are expected to occur in an organism with the set of reactions in reference metabolic pathways, it is possible to infer the main metabolic functions of an organism. In this paper, the metabolic networks of the mesophilic archaeon Methanosarcina acetivorans and the thermophilic archaeon Methanopyrus kandleri have been reconstructed based on the KEGG LIGAND database, followed by four topological statistical analyses of the nodes in the two networks to compare their metabolic networks. The values of average degree and characteristic path length are very small but clustering coefficient is relatively large. The results show that the complete metabolic networks of M. acetivorans and M. kandleri possessed "small-world" network properties. Then we used Girvan–Newman modular algorithm to identify hub modules and compared hub modules with non-hub modules, respectively. The results show that M. kandleri metabolic network has a better modular organization than the M. acetivorans network. M. acetivorans includes 39 modules, 25 modules of them are independent, and 15 modules are functionally pure. On the other hand, M. kandleri includes 30 modules. Among them, there are 20 independent modules, and 14 of them are functionally pure. These results further indicated that the present approach for identifying modules yields modules that have biologically significant functions. We also identified hub modules of the metabolic networks and found that these hub modules are carbohydrate metabolism and amino acid metabolism. The conclusions obtained from such studies provide a broad overview of the similarities and differences between organism's metabolic networks. These will be very helpful for further research on thermostability of methanogens.

Mathematical Modelling of Polyamine Metabolism in Bloodstream-Form Trypanosoma brucei: An Application to Drug Target Identification

We present the first computational kinetic model of polyamine metabolism in bloodstream-form Trypanosoma brucei, the causative agent of human African trypanosomiasis. We systematically extracted the polyamine pathway from the complete metabolic network while still maintaining the predictive capability of the pathway. The kinetic model is constructed on the basis of information gleaned from the experimental biology literature and defined as a set of ordinary differential equations. We applied Michaelis-Menten kinetics featuring regulatory factors to describe enzymatic activities that are well defined. Uncharacterised enzyme kinetics were approximated and justified with available physiological properties of the system. Optimisation-based dynamic simulations were performed to train the model with experimental data and inconsistent predictions prompted an iterative procedure of model refinement. Good agreement between simulation results and measured data reported in various experimental conditions shows that the model has good applicability in spite of there being gaps in the required data. With this kinetic model, the relative importance of the individual pathway enzymes was assessed. We observed that, at low-to-moderate levels of inhibition, enzymes catalysing reactions of de novo AdoMet (MAT) and ornithine production (OrnPt) have more efficient inhibitory effect on total trypanothione content in comparison to other enzymes in the pathway. In our model, prozyme and TSHSyn (the production catalyst of total trypanothione) were also found to exhibit potent control on total trypanothione content but only when they were strongly inhibited. Different chemotherapeutic strategies against T. brucei were investigated using this model and interruption of polyamine synthesis via joint inhibition of MAT or OrnPt together with other polyamine enzymes was identified as an optimal therapeutic strategy.

Systems medicine and metabolic modelling

Several complex diseases are caused by the malfunction of human metabolism, and deciphering the underlying molecular mechanisms can elucidate their aetiology. Systems biology is an integrative approach combining experimental and computational biology to identify and describe the molecular mechanisms of complex biological systems. Systems medicine has the potential to elucidate the onset and progression of complex metabolic diseases through the use of computational approaches. Advances in biotechnology have resulted in the provision of high-throughput data, which provide information about different metabolic processes. The systems medicine approach can utilize such data to reconstruct genome-scale metabolic models that can be used to study the function of specific enzymes and pathways in the context of the complete metabolic network. In this review, we outline the importance of genome-scale models in systems medicine and discuss how they may contribute towards the development of personalized medicine.

A systems biology framework for modeling metabolic enzyme inhibition of Mycobacterium tuberculosis

BackgroundBecause metabolism is fundamental in sustaining microbial life, drugs that target pathogen-specific metabolic enzymes and pathways can be very effective. In particular, the metabolic challenges faced by intracellular pathogens, such as Mycobacterium tuberculosis, residing in the infected host provide novel opportunities for therapeutic intervention.ResultsWe developed a mathematical framework to simulate the effects on the growth of a pathogen when enzymes in its metabolic pathways are inhibited. Combining detailed models of enzyme kinetics, a complete metabolic network description as modeled by flux balance analysis, and a dynamic cell population growth model, we quantitatively modeled and predicted the dose-response of the 3-nitropropionate inhibitor on the growth of M. tuberculosis in a medium whose carbon source was restricted to fatty acids, and that of the 5'-O-(N-salicylsulfamoyl) adenosine inhibitor in a medium with low-iron concentration.ConclusionThe predicted results quantitatively reproduced the experimentally measured dose-response curves, ranging over three orders of magnitude in inhibitor concentration. Thus, by allowing for detailed specifications of the underlying enzymatic kinetics, metabolic reactions/constraints, and growth media, our model captured the essential chemical and biological factors that determine the effects of drug inhibition on in vitro growth of M. tuberculosis cells.

Cybernetic modeling based on pathway analysis for Penicillium chrysogenum fed-batch fermentation

A macrokinetic model employing cybernetic methodology is proposed to describe mycelium growth and penicillin production. Based on the primordial and complete metabolic network of Penicillium chrysogenum found in the literature, the modeling procedure is guided by metabolic flux analysis and cybernetic modeling framework. The abstracted cybernetic model describes the transients of the consumption rates of the substrates, the assimilation rates of intermediates, the biomass growth rate, as well as the penicillin formation rate. Combined with the bioreactor model, these reaction rates are linked with the most important state variables, i.e., mycelium, substrate and product concentrations. Simplex method is used to estimate the sensitive parameters of the model. Finally, validation of the model is carried out with 20 batches of industrial-scale penicillin cultivation.

Toward the automated generation of genome-scale metabolic networks in the SEED

BackgroundCurrent methods for the automated generation of genome-scale metabolic networks focus on genome annotation and preliminary biochemical reaction network assembly, but do not adequately address the process of identifying and filling gaps in the reaction network, and verifying that the network is suitable for systems level analysis. Thus, current methods are only sufficient for generating draft-quality networks, and refinement of the reaction network is still largely a manual, labor-intensive process.ResultsWe have developed a method for generating genome-scale metabolic networks that produces substantially complete reaction networks, suitable for systems level analysis. Our method partitions the reaction space of central and intermediary metabolism into discrete, interconnected components that can be assembled and verified in isolation from each other, and then integrated and verified at the level of their interconnectivity. We have developed a database of components that are common across organisms, and have created tools for automatically assembling appropriate components for a particular organism based on the metabolic pathways encoded in the organism's genome. This focuses manual efforts on that portion of an organism's metabolism that is not yet represented in the database. We have demonstrated the efficacy of our method by reverse-engineering and automatically regenerating the reaction network from a published genome-scale metabolic model for Staphylococcus aureus. Additionally, we have verified that our method capitalizes on the database of common reaction network components created for S. aureus, by using these components to generate substantially complete reconstructions of the reaction networks from three other published metabolic models (Escherichia coli, Helicobacter pylori, and Lactococcus lactis). We have implemented our tools and database within the SEED, an open-source software environment for comparative genome annotation and analysis.ConclusionOur method sets the stage for the automated generation of substantially complete metabolic networks for over 400 complete genome sequences currently in the SEED. With each genome that is processed using our tools, the database of common components grows to cover more of the diversity of metabolic pathways. This increases the likelihood that components of reaction networks for subsequently processed genomes can be retrieved from the database, rather than assembled and verified manually.

Dynamic Metabolic Modeling for a MAB Bioprocess

Production of monoclonal antibodies (MAb) for diagnostic or therapeutic applications has become an important task in the pharmaceutical industry. The efficiency of high-density reactor systems can be potentially increased by model-based design and control strategies. Therefore, a reliable kinetic model for cell metabolism is required. A systematic procedure based on metabolic modeling is used to model nutrient uptake and key product formation in a MAb bioprocess during both the growth and post-growth phases. The approach combines the key advantages of stoichiometric and kinetic models into a complete metabolic network while integrating the regulation and control of cellular activity. This modeling procedure can be easily applied to any cell line during both the cell growth and post-growth phases. Quadratic programming (QP) has been identified as a suitable method to solve the underdetermined constrained problem related to model parameter identification. The approach is illustrated for the case of murine hybridoma cells cultivated in stirred spinners.

Metabolic network driven analysis of genome-wide transcription data from Aspergillus nidulans

Genome-wide transcription analysis of Aspergillus nidulans grown on different carbon sources and a reconstruction of the complete metabolic network of this filamentous fungi are presented.

In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data

A significant goal in the post-genome era is to relate the annotated genome sequence to the physiological functions of a cell. Working from the annotated genome sequence, as well as biochemical and physiological information, it is possible to reconstruct complete metabolic networks. Furthermore, computational methods have been developed to interpret and predict the optimal performance of a metabolic network under a range of growth conditions. We have tested the hypothesis that Escherichia coli uses its metabolism to grow at a maximal rate using the E. coli MG1655 metabolic reconstruction. Based on this hypothesis, we formulated experiments that describe the quantitative relationship between a primary carbon source (acetate or succinate) uptake rate, oxygen uptake rate, and maximal cellular growth rate. We found that the experimental data were consistent with the stated hypothesis, namely that the E. coli metabolic network is optimized to maximize growth under the experimental conditions considered. This study thus demonstrates how the combination of in silico and experimental biology can be used to obtain a quantitative genotype-phenotype relationship for metabolism in bacterial cells.