Large-scale Metabolic Networks Research Articles

In silico biomarker analysis of the adverse effects of perfluorooctane sulfonate (PFOS) exposure on the metabolic physiology of embryo-larval zebrafish

Perfluorooctane sulfonate (PFOS) is a ubiquitous pollutant in global aquatic ecosystems with increasing concern for its toxicity to aquatic wildlife through inadvertent exposures. To assess the likely adverse effects of PFOS exposure on aquatic wildlife inhabiting polluted ecosystems, there is a need to identify biomarkers of its exposure and toxicity. We used an integrated systems toxicological framework to identify physiologically relevant biomarkers of PFOS toxicity in fish. An in silico stoichiometric metabolism model of zebrafish (Danio rerio) was used to integrate available (published by other authors) metabolomics and transcriptomics datasets from in vivo toxicological studies with 5 days post fertilized embryo-larval life stage of zebrafish. The experimentally derived omics datasets were used as constraints to parameterize an in silico mathematical model of zebrafish metabolism. In silico simulations using flux balance analysis (FBA) and its extensions showed prominent effects of PFOS exposure on the carnitine shuttle and fatty acid oxidation. Further analysis of metabolites comprising the impacted metabolic reactions indicated carnitine to be the most highly represented cofactor metabolite. Flux simulations also showed a near dose-responsive increase in the pools for fatty acids and acyl-CoAs under PFOS exposure. Taken together, our integrative in silico results showed dyslipidemia effects under PFOS exposure and uniquely identified carnitine as a candidate metabolite biomarker. The verification of this prediction was sought in a subsequent in vivo environmental monitoring study by the authors which showed carnitine to be a modal biomarker of PFOS exposure in wild-caught fish and marine mammals sampled from the northern Gulf of Mexico. Therefore, we highlight the efficacy of FBA to study the properties of large-scale metabolic networks and to identify biomarkers of pollutant exposure in aquatic wildlife.

COVRECON: automated integration of genome- and metabolome-scale network reconstruction and data-driven inverse modeling of metabolic interaction networks.

One central goal of systems biology is to infer biochemical regulations from large-scale OMICS data. Many aspects of cellular physiology and organismal phenotypes can be understood as results of metabolic interaction network dynamics. Previously, we have proposed a convenient mathematical method, which addresses this problem using metabolomics data for the inverse calculation of biochemical Jacobian matrices revealing regulatory checkpoints of biochemical regulations. The proposed algorithms for this inference are limited by two issues: they rely on structural network information that needs to be assembled manually, and they are numerically unstable due to ill-conditioned regression problems for large-scale metabolic networks. To address these problems, we developed a novel regression loss-based inverse Jacobian algorithm, combining metabolomics COVariance and genome-scale metabolic RECONstruction, which allows for a fully automated, algorithmic implementation of the COVRECON workflow. It consists of two parts: (i) Sim-Network and (ii) inverse differential Jacobian evaluation. Sim-Network automatically generates an organism-specific enzyme and reaction dataset from Bigg and KEGG databases, which is then used to reconstruct the Jacobian's structure for a specific metabolomics dataset. Instead of directly solving a regression problem as in the previous workflow, the new inverse differential Jacobian is based on a substantially more robust approach and rates the biochemical interactions according to their relevance from large-scale metabolomics data. The approach is illustrated by in silico stochastic analysis with differently sized metabolic networks from the BioModels database and applied to a real-world example. The characteristics of the COVRECON implementation are that (i) it automatically reconstructs a data-driven superpathway model; (ii) more general network structures can be investigated, and (iii) the new inverse algorithm improves stability, decreases computation time, and extends to large-scale models. The code is available in the website https://bitbucket.org/mosys-univie/covrecon.

An automated workflow that generates atom mappings for large-scale metabolic models and its application to Arabidopsis thaliana.

Quantification of reaction fluxes of metabolic networks can help us understand how the integration of different metabolic pathways determines cellular functions. Yet, intracellular fluxes cannot be measured directly but are estimated with metabolic flux analysis (MFA), which relies on the patterns of isotope labeling of metabolites in the network. The application of MFA also requires a stoichiometric model with atom mappings that are currently not available for the majority of large-scale metabolic network models, particularly of plants. While automated approaches such as the Reaction Decoder Toolkit (RDT) can produce atom mappings for individual reactions, tracing the flow of individual atoms of the entire reactions across a metabolic model remains challenging. Here we establish an automated workflow to obtain reliable atom mappings for large-scale metabolic models by refining the outcome of RDT, and apply the workflow to metabolic models of Arabidopsis thaliana. We demonstrate the accuracy of RDT through a comparative analysis with atom mappings from a large database of biochemical reactions, MetaCyc. We further show the utility of our automated workflow by simulating 15 N isotope enrichment and identifying nitrogen (N)-containing metabolites which show enrichment patterns that are informative for flux estimation in future 15 N-MFA studies of A. thaliana. The automated workflow established in this study can be readily expanded to other species for which metabolic models have been established and the resulting atom mappings will facilitate MFA and graph-theoretic structural analyses with large-scale metabolic networks.

Identification of flux trade-offs in metabolic networks

Trade-offs are inherent to biochemical networks governing diverse cellular functions, from gene expression to metabolism. Yet, trade-offs between fluxes of biochemical reactions in a metabolic network have not been formally studied. Here, we introduce the concept of absolute flux trade-offs and devise a constraint-based approach, termed FluTO, to identify and enumerate flux trade-offs in a given genome-scale metabolic network. By employing the metabolic networks of Escherichia coli and Saccharomyces cerevisiae, we demonstrate that the flux trade-offs are specific to carbon sources provided but that reactions involved in the cofactor and prosthetic group biosynthesis are present in trade-offs across all carbon sources supporting growth. We also show that absolute flux trade-offs depend on the biomass reaction used to model the growth of Arabidopsis thaliana under different carbon and nitrogen conditions. The identified flux trade-offs reflect the tight coupling between nitrogen, carbon, and sulphur metabolisms in leaves of C3 plants. Altogether, FluTO provides the means to explore the space of alternative metabolic routes reflecting the constraints imposed by inherent flux trade-offs in large-scale metabolic networks.

Metabolic Brain Network Analysis With 18F-FDG PET in a Rat Model of Neuropathic Pain.

Neuropathic pain has been found to be related to profound reorganization in the function and structure of the brain. We previously demonstrated changes in local brain activity and functional/metabolic connectivity among selected brain regions by using neuroimaging methods. The present study further investigated large-scale metabolic brain network changes in 32 Sprague–Dawley rats with right brachial plexus avulsion injury (BPAI). Graph theory was applied in the analysis of 2-deoxy-2-[18F] fluoro-D-glucose (18F-FDG) PET images. Inter-subject metabolic networks were constructed by calculating correlation coefficients. Global and nodal network properties were calculated and comparisons between pre- and post-BPAI (7 days) status were conducted. The global network properties (including global efficiency, local efficiency and small-world index) and nodal betweenness centrality did not significantly change for all selected sparsity thresholds following BPAI (p > 0.05). As for nodal network properties, both nodal degree and nodal efficiency measures significantly increased in the left caudate putamen, left medial prefrontal cortex, and right caudate putamen (p < 0.001). The right entorhinal cortex showed a different nodal degree (p < 0.05) but not nodal efficiency. These four regions were selected for seed-based metabolic connectivity analysis. Strengthened connectivity was found among these seeds and distributed brain regions including sensorimotor area, cognitive area, and limbic system, etc. (p < 0.05). Our results indicated that the brain had the resilience to compensate for BPAI-induced neuropathic pain. However, the importance of bilateral caudate putamen, left medial prefrontal cortex, and right entorhinal cortex in the network was strengthened, as well as most of their connections with distributed brain regions.

GeneReg: a constraint-based approach for design of feasible metabolic engineering strategies at the gene level.

MotivationLarge-scale metabolic models are widely used to design metabolic engineering strategies for diverse biotechnological applications. However, the existing computational approaches focus on alteration of reaction fluxes and often neglect the manipulations of gene expression to implement these strategies.ResultsHere, we find that the association of genes with multiple reactions leads to infeasibility of engineering strategies at the flux level, since they require contradicting manipulations of gene expression. Moreover, we identify that all of the existing approaches to design gene knockout strategies do not ensure that the resulting design may also require other gene alterations, such as up- or downregulations, to match the desired flux distribution. To address these issues, we propose a constraint-based approach, termed GeneReg, that facilitates the design of feasible metabolic engineering strategies at the gene level and that is readily applicable to large-scale metabolic networks. We show that GeneReg can identify feasible strategies to overproduce ethanol in Escherichia coli and lactate in Saccharomyces cerevisiae, but overproduction of the TCA cycle intermediates is not feasible in five organisms used as cell factories under default growth conditions. Therefore, GeneReg points at the need to couple gene regulation and metabolism to design rational metabolic engineering strategies.Availability and implementation https://github.com/MonaRazaghi/GeneReg Supplementary information Supplementary data are available at Bioinformatics online.

Scalable computation of intracellular metabolite concentrations

Quantitative characterization of intracellular metabolite concentrations is central to predictive models of cellular functions. Current constraint-based models of metabolism and macromolecular expression provide genotype-phenotype predictions without accounting for intracellular concentrations using simplifying assumptions and optimality principles. However, optimality principles do not generally apply under stress conditions, rendering these predictions unreliable. Incorporation of intracellular concentrations into constraint-based or kinetic models of cellular functions introduces nonlinearities that are computationally challenging to handle, limiting their applicability to small-scale biological networks. Here, we introduce tractable computational techniques to characterize intracellular metabolite concentrations within a constraint-based modeling framework. This model provides a feasible concentration set, which can generally be nonconvex and disconnected. We examine three approaches based on polynomial optimization, random sampling, and global optimization. We leverage the sparsity and algebraic structure of the underlying biophysical models to enhance the computational efficiency of these techniques. We then compare their performance in two case studies, showing that the global-optimization formulation exhibits more desirable scaling properties than the random-sampling and polynomial-optimization formulation, and, thus, is a promising candidate for handling large-scale metabolic networks.

Clustering analysis of tumor metabolic networks

BackgroundBiological networks are representative of the diverse molecular interactions that occur within cells. Some of the commonly studied biological networks are modeled through protein-protein interactions, gene regulatory, and metabolic pathways. Among these, metabolic networks are probably the most studied, as they directly influence all physiological processes. Exploration of biochemical pathways using multigraph representation is important in understanding complex regulatory mechanisms. Feature extraction and clustering of these networks enable grouping of samples obtained from different biological specimens. Clustering techniques separate networks depending on their mutual similarity.ResultsWe present a clustering analysis on tissue-specific metabolic networks for single samples from three primary tumor sites: breast, lung, and kidney cancer. The metabolic networks were obtained by integrating genome scale metabolic models with gene expression data. We performed network simplification to reduce the computational time needed for the computation of network distances. We empirically proved that networks clustering can characterize groups of patients in multiple conditions.ConclusionsWe provide a computational methodology to explore and characterize the metabolic landscape of tumors, thus providing a general methodology to integrate analytic metabolic models with gene expression data. This method represents a first attempt in clustering large scale metabolic networks. Moreover, this approach gives the possibility to get valuable information on what are the effects of different conditions on the overall metabolism.

Large-scale metabolic interaction network\xa0of the mouse and human gut microbiota

The role of our gut microbiota in health and disease is largely attributed to the collective metabolic activities of the inhabitant microbes. A system-level framework of the microbial community structure, mediated through metabolite transport, would provide important insights into the complex microbe-microbe and host-microbe chemical interactions. This framework, if adaptable to both mouse and human systems, would be useful for mechanistic interpretations of the vast amounts of experimental data from gut microbiomes in murine animal models, whether humanized or not. Here, we constructed a literature-curated, interspecies network of the mammalian gut microbiota for mouse and human hosts, called NJC19. This network is an extensive data resource, encompassing 838 microbial species (766 bacteria, 53 archaea, and 19 eukaryotes) and 6 host cell types, interacting through 8,224 small-molecule transport and macromolecule degradation events. Moreover, we compiled 912 negative associations between organisms and metabolic compounds that are not transportable or degradable by those organisms. Our network may facilitate experimental and computational endeavors for the mechanistic investigations of host-associated microbial communities.

A Promising Method for Calculating True Steady-State Metabolite Concentrations in Large-Scale Metabolic Reaction Network Models.

The calculation of steady-state metabolite concentrations in metabolic reaction network models is the first step in the sensitivity analysis of a metabolic reaction system described by differential equations. However, this calculation becomes very difficult when the number of differential equations is more than 100. In the present study, therefore, we investigated a calculation procedure for obtaining true steady-state metabolite concentrations both efficiently and accurately even in large-scale network models. For convenience, a linear pathway model composed of a simple Michaelis-Menten rate law and two TCA cycle models were used as case studies. The calculation procedure is as follows: first solve the differential equations by a numerical method for solving initial-value problems until the upper several digits of the calculated values stabilize, and then use these values as initial guesses for a root-finding technique. An intensive investigation indicates that the S-system technique, finding roots in logarithmic space and providing a broader convergence region, is superior to the Newton-Raphson technique, and the algorithm using the S-system technique successfully provides true steady-state values with machine accuracy even with 1,500 differential equations. The complex-step method is also shown to contribute to shortening the calculation time and enhancing the accuracy. The program code has been deposited to https://github.com/BioprocessdesignLab/Steadystateconc.

Find_tfSBP: find thermodynamics-feasible and smallest balanced pathways with high yield from large-scale metabolic networks

Biologically meaningful metabolic pathways are important references in the design of industrial bacterium. At present, constraint-based method is the only way to model and simulate a genome-scale metabolic network under steady-state criteria. Due to the inadequate assumption of the relationship in gene-enzyme-reaction as one-to-one unique association, computational difficulty or ignoring the yield from substrate to product, previous pathway finding approaches can’t be effectively applied to find out the high yield pathways that are mass balanced in stoichiometry. In addition, the shortest pathways may not be the pathways with high yield. At the same time, a pathway, which exists in stoichiometry, may not be feasible in thermodynamics. By using mixed integer programming strategy, we put forward an algorithm to identify all the smallest balanced pathways which convert the source compound to the target compound in large-scale metabolic networks. The resulting pathways by our method can finely satisfy the stoichiometric constraints and non-decomposability condition. Especially, the functions of high yield and thermodynamics feasibility have been considered in our approach. This tool is tailored to direct the metabolic engineering practice to enlarge the metabolic potentials of industrial strains by integrating the extensive metabolic network information built from systems biology dataset.

A Protocol for Generating and Exchanging (Genome-Scale) Metabolic Resource Allocation Models.

In this article, we present a protocol for generating a complete (genome-scale) metabolic resource allocation model, as well as a proposal for how to represent such models in the systems biology markup language (SBML). Such models are used to investigate enzyme levels and achievable growth rates in large-scale metabolic networks. Although the idea of metabolic resource allocation studies has been present in the field of systems biology for some years, no guidelines for generating such a model have been published up to now. This paper presents step-by-step instructions for building a (dynamic) resource allocation model, starting with prerequisites such as a genome-scale metabolic reconstruction, through building protein and noncatalytic biomass synthesis reactions and assigning turnover rates for each reaction. In addition, we explain how one can use SBML level 3 in combination with the flux balance constraints and our resource allocation modeling annotation to represent such models.

In silico metabolic network analysis of Arabidopsis leaves.

BackgroundDuring the last decades, we face an increasing interest in superior plants to supply growing demands for human and animal nutrition and for the developing bio-based economy. Presently, our limited understanding of their metabolism and its regulation hampers the targeted development of desired plant phenotypes. In this regard, systems biology, in particular the integration of metabolic and regulatory networks, is promising to broaden our knowledge and to further explore the biotechnological potential of plants.ResultsThe thale cress Arabidopsis thaliana provides an ideal model to understand plant primary metabolism. To obtain insight into its functional properties, we constructed a large-scale metabolic network of the leaf of A. thaliana. It represented 511 reactions with spatial separation into compartments. Systematic analysis of this network, utilizing elementary flux modes, investigates metabolic capabilities of the plant and predicts relevant properties on the systems level: optimum pathway use for maximum growth and flux re-arrangement in response to environmental perturbation. Our computational model indicates that the A. thaliana leaf operates near its theoretical optimum flux state in the light, however, only in a narrow range of photon usage. The simulations further demonstrate that the natural day-night shift requires substantial re-arrangement of pathway flux between compartments: 89 reactions, involving redox and energy metabolism, substantially change the extent of flux, whereas 19 reactions even invert flux direction. The optimum set of anabolic pathways differs between day and night and is partly shifted between compartments. The integration with experimental transcriptome data pinpoints selected transcriptional changes that mediate the diurnal adaptation of the plant and superimpose the flux response.ConclusionsThe successful application of predictive modelling in Arabidopsis thaliana can bring systems-biological interpretation of plant systems forward. Using the gained knowledge, metabolic engineering strategies to engage plants as biotechnological factories can be developed.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0347-3) contains supplementary material, which is available to authorized users.

A new parametric method to smooth time-series data of metabolites in metabolic networks

Mathematical modeling of large-scale metabolic networks usually requires smoothing of metabolite time-series data to account for measurement or biological errors. Accordingly, the accuracy of smoothing curves strongly affects the subsequent estimation of model parameters. Here, an efficient parametric method is proposed for smoothing metabolite time-series data, and its performance is evaluated. To simplify parameter estimation, the method uses S-system-type equations with simple power law-type efflux terms. Iterative calculation using this method was found to readily converge, because parameters are estimated stepwise. Importantly, smoothing curves are determined so that metabolite concentrations satisfy mass balances. Furthermore, the slopes of smoothing curves are useful in estimating parameters, because they are probably close to their true behaviors regardless of errors that may be present in the actual data. Finally, calculations for each differential equation were found to converge in much less than one second if initial parameters are set at appropriate (guessed) values.

Control of fluxes in metabolic networks.

Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism.

Editorial: Engineering Synthetic Metabolons: From Metabolic Modeling to Rational Design of Biosynthetic Devices.

The Editorial on the Research Topic Engineering synthetic metabolons: from metabolic modelling to rational design of biosynthetic devices Synthetic Biology is an emerging discipline that enjoys rapidly increasing popularity. However, there is no widely accepted definition for the term Synthetic Biology, because the field of Synthetic Biology is multifaceted and rapidly moving. As viewed from its underlying principle, Synthetic Biology can be defined as the “application of engineering principles to biological systems.” It reaches from pathway engineering in living systems, i.e., the introduction of functional biochemical pathways into existing organisms (see featured research topic review by Proschel et al. and references cited therein), and the design of vesicle-based multicompartmented biochemical reactors and protocells (see featured research topic review by Schmitt et al. and references cited therein) to the creation of entirely synthetic cells with reproductive potential that is encoded by synthetic genes [with the work of Gibson et al. (2008, 2010) that attracted the greatest publicity in the recent years]. Likewise, the definition provided at http://syntheticbiology.org covers these two extremes in that Synthetic Biology is defined as “(A) the design and construction of new biological parts, devices, and systems and (B) the re-design of existing, natural biological systems for useful purposes.” This also means that Synthetic Biology is not limited to living systems but also comprises the combination and re-design of biological parts into artificial bioreactors. In this respect, Synthetic Biology is highly interdisciplinary, bringing together molecular biology with biophysics, material sciences, bioengineering, and computational approaches. Besides the invaluable role in helping biochemists understand the metabolic complexity of living systems and the extent to which observations from existing metabolic engineering strategies match predictions from large-scale modeling (see, e.g., the featured research topic article by Basler et al.), models are crucial for the rational design of synthetic systems (see, e.g., the featured research topic article by Elbinger et al.). Metabolic modeling has the capacity to guide the biochemical layout of engineered pathways in vivo and the design of artificial bioreactors in vitro. For instance, computational modeling can play an important role for the selection of individual enzyme isoforms for engineered metabolic pathways. Vice versa, in vitro studies in artificial bioreactors has advanced our understanding of biochemically complex processes, such as starch biosynthesis, as summarized in the featured research topic review by O’Neill and Field. On the other hand, molecular dynamics simulations help to understand complex biochemical and biophysical processes such as enzymatic catalysis, molecular interactions of biomolecules (see featured research topic reviews by Horn and Sticht and Gros et al.), or membrane transport (see featured research topic articles by Rog et al., Bertelshofer et al. and Sun et al.). The knowledge gained by these simulations can be exploited for the structural and molecular design of synthetic systems, i.e., for the choice of artificial protein or nucleic acid scaffolds for the spatial modular organization of artificial systems (see featured research topic review by Proschel et al. and references cited therein). This research topic focuses on the importance of computational modeling for the rational design of synthetic systems on all these levels. Consequently, a wide variety of approaches is being covered in this issue, from pathway engineering in eukaryotic cells to molecular dynamics simulations of transport processes. The modeling approaches range from partial differential equations, allowing predictions of spatiotemporal concentration of metabolites in a biochemical microreactor (see featured research topic original research article by Elbinger et al.), to steady-state equations, testing the effect of metabolic engineering strategy in large-scale metabolic networks (see, e.g., the featured research topic article by Basler et al.). A new metabolic engineering strategy for the glucosinolate pathway that increases dihomomethionine levels without increasing the levels of leucine-derived side products was experimentally validated in Nicotiana benthamiana (see featured research topic original research article by Crocoll et al. and references to previous attempts therein). Sharing of modeling results and ensuring reproducibility of model-data integration requires setting and following a set of standards, which are currently established in the context of metabolomics research (see featured research topic review by Hill et al.). The majority of the articles covered in this research topic article is contributed by members of the interdisciplinary project SynBio that is funded by the emerging fields initiative of the Friedrich-Alexander-Universitat Erlangen-Nurnberg (Germany).

Efficient Modeling of MS/MS Data for Metabolic Flux Analysis.

Metabolic flux analysis (MFA) is a widely used method for quantifying intracellular metabolic fluxes. It works by feeding cells with isotopic labeled nutrients, measuring metabolite isotopic labeling, and computationally interpreting the measured labeling data to estimate flux. Tandem mass-spectrometry (MS/MS) has been shown to be useful for MFA, providing positional isotopic labeling data. Specifically, MS/MS enables the measurement of a metabolite tandem mass-isotopomer distribution, representing the abundance in which certain parent and product fragments of a metabolite have different number of labeled atoms. However, a major limitation in using MFA with MS/MS data is the lack of a computationally efficient method for simulating such isotopic labeling data. Here, we describe the tandemer approach for efficiently computing metabolite tandem mass-isotopomer distributions in a metabolic network, given an estimation of metabolic fluxes. This approach can be used by MFA to find optimal metabolic fluxes, whose induced metabolite labeling patterns match tandem mass-isotopomer distributions measured by MS/MS. The tandemer approach is applied to simulate MS/MS data in a small-scale metabolic network model of mammalian methionine metabolism and in a large-scale metabolic network model of E. coli. It is shown to significantly improve the running time by between two to three orders of magnitude compared to the state-of-the-art, cumomers approach. We expect the tandemer approach to promote broader usage of MS/MS technology in metabolic flux analysis. Implementation is freely available at www.cs.technion.ac.il/~tomersh/methods.html

Avoiding the Enumeration of Infeasible Elementary Flux Modes by Including Transcriptional Regulatory Rules in the Enumeration Process Saves Computational Costs.

Despite the significant progress made in recent years, the computation of the complete set of elementary flux modes of large or even genome-scale metabolic networks is still impossible. We introduce a novel approach to speed up the calculation of elementary flux modes by including transcriptional regulatory information into the analysis of metabolic networks. Taking into account gene regulation dramatically reduces the solution space and allows the presented algorithm to constantly eliminate biologically infeasible modes at an early stage of the computation procedure. Thereby, computational costs, such as runtime, memory usage, and disk space, are extremely reduced. Moreover, we show that the application of transcriptional rules identifies non-trivial system-wide effects on metabolism. Using the presented algorithm pushes the size of metabolic networks that can be studied by elementary flux modes to new and much higher limits without the loss of predictive quality. This makes unbiased, system-wide predictions in large scale metabolic networks possible without resorting to any optimization principle.

TEFMA: computing thermodynamically feasible elementary flux modes in metabolic networks.

: Elementary flux modes (EFMs) are important structural tools for the analysis of metabolic networks. It is known that many topologically feasible EFMs are biologically irrelevant. Therefore, tools are needed to find the relevant ones. We present thermodynamic tEFM analysis (tEFMA) which uses the cellular metabolome to avoid the enumeration of thermodynamically infeasible EFMs. Specifically, given a metabolic network and a not necessarily complete metabolome, tEFMA efficiently returns the full set of thermodynamically feasible EFMs consistent with the metabolome. Compared with standard approaches, tEFMA strongly reduces the memory consumption and the overall runtime. Thus tEFMA provides a new way to analyze unbiasedly hitherto inaccessible large-scale metabolic networks. https://github.com/mpgerstl/tEFMA CONTACT: : christian.jungreuthmayer@boku.ac.at or juergen.zanghellini@boku.ac.at Supplementary data are available at Bioinformatics online.

Discovery of substrate cycles in large scale metabolic networks using hierarchical modularity.

BackgroundA substrate cycle is a set of metabolic reactions, arranged in a loop, which does not result in net consumption or production of the metabolites. The cycle operates by transforming a cofactor, e.g. oxidizing a reducing equivalent. Substrate cycles have been found experimentally in many parts of metabolism; however, their physiological roles remain unclear. As genome-scale metabolic models become increasingly available, there is now the opportunity to comprehensively catalogue substrate cycles, and gain additional insight into this potentially important motif of metabolic networks.ResultsWe present a method to identify substrate cycles in the context of metabolic modules, which facilitates functional analysis. This method utilizes elementary flux mode (EFM) analysis to find potential substrate cycles in the form of cyclical EFMs, and combines this analysis with network partition based on retroactive (cyclical) interactions between reactions. In addition to providing functional context, partitioning the network into modules allowed exhaustive EFM calculations on smaller, tractable subnetworks that are enriched in metabolic cycles. Applied to a large-scale model of human liver metabolism (HepatoNet1), our method found not only well-known substrate cycles involving ATP hydrolysis, but also potentially novel substrate cycles involving the transformation of other cofactors. A key characteristic of the substrate cycles identified in this study is that the lengths are relatively short (2–13 reactions), comparable to many experimentally observed substrate cycles.ConclusionsEFM computation for large scale networks remains computationally intractable for exhaustive substrate cycle enumeration. Our algorithm utilizes a ‘divide and conquer’ strategy where EFM analysis is performed on systematically identified network modules that are designed to be enriched in cyclical interactions. We find that several substrate cycles uncovered using our approach are not identified when the network is partitioned in a more generic manner based solely on connectivity rather than cycles, demonstrating the value of targeting motif searches to sub-networks replete with a topological feature that resembles the desired motif itself.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0146-2) contains supplementary material, which is available to authorized users.