Large-scale Metabolic Networks Research Articles

A U-system approach for predicting metabolic behaviors and responses based on an alleged metabolic reaction network.

BackgroundProgress in systems biology offers sophisticated approaches toward a comprehensive understanding of biological systems. Yet, computational analyses are held back due to difficulties in determining suitable model parameter values from experimental data which naturally are subject to biological fluctuations. The data may also be corrupted by experimental uncertainties and sometimes do not contain all information regarding variables that cannot be measured for technical reasons.ResultsWe show here a streamlined approach for the construction of a coarse model that allows us to set up dynamic models with minimal input information. The approach uses a hybrid between a pure mass action system and a generalized mass action (GMA) system in the framework of biochemical systems theory (BST) with rate constants of 1, normal kinetic orders of 1, and -0.5 and 0.5 for inhibitory and activating effects, named Unity (U)-system. The U-system model does not necessarily fit all data well but is often sufficient for predicting metabolic behavior of metabolites which cannot be simultaneously measured, identifying inconsistencies between experimental data and the assumed underlying pathway structure, as well as predicting system responses to a modification of gene or enzyme. The U-system approach was validated with small, generic systems and implemented to model a large-scale metabolic reaction network of a higher plant, Arabidopsis. The dynamic behaviors obtained by predictive simulations agreed with actually available metabolomic time-series data, identified probable errors in the experimental datasets, and estimated probable behavior of unmeasurable metabolites in a qualitative manner. The model could also predict metabolic responses of Arabidopsis with altered network structures due to genetic modification.ConclusionsThe U-system approach can effectively predict metabolic behaviors and responses based on structures of an alleged metabolic reaction network. Thus, it can be a useful first-line tool of data analysis, model diagnostics and aid the design of next-step experiments.

Computing autocatalytic sets to unravel inconsistencies in metabolic network reconstructions.

Genome-scale metabolic network reconstructions have been established as a powerful tool for the prediction of cellular phenotypes and metabolic capabilities of organisms. In recent years, the number of network reconstructions has been constantly increasing, mostly because of the availability of novel (semi-)automated procedures, which enabled the reconstruction of metabolic models based on individual genomes and their annotation. The resulting models are widely used in numerous applications. However, the accuracy and predictive power of network reconstructions are commonly limited by inherent inconsistencies and gaps. Here we present a novel method to validate metabolic network reconstructions based on the concept of autocatalytic sets. Autocatalytic sets correspond to collections of metabolites that, besides enzymes and a growth medium, are required to produce all biomass components in a metabolic model. These autocatalytic sets are well-conserved across all domains of life, and their identification in specific genome-scale reconstructions allows us to draw conclusions about potential inconsistencies in these models. The method is capable of detecting inconsistencies, which are neglected by other gap-finding methods. We tested our method on the Model SEED, which is the largest repository for automatically generated genome-scale network reconstructions. In this way, we were able to identify a significant number of missing pathways in several of these reconstructions. Hence, the method we report represents a powerful tool to identify inconsistencies in large-scale metabolic networks. The method is available as source code on http://users.minet.uni-jena.de/∼m3kach/ASBIG/ASBIG.zip. christoph.kaleta@uni-jena.de Supplementary data are available at Bioinformatics online.

Monte-Carlo modeling of the central carbon metabolism of Lactococcus lactis: insights into metabolic regulation.

Metabolic pathways are complex dynamic systems whose response to perturbations and environmental challenges are governed by multiple interdependencies between enzyme properties, reactions rates, and substrate levels. Understanding the dynamics arising from such a network can be greatly enhanced by the construction of a computational model that embodies the properties of the respective system. Such models aim to incorporate mechanistic details of cellular interactions to mimic the temporal behavior of the biochemical reaction system and usually require substantial knowledge of kinetic parameters to allow meaningful conclusions. Several approaches have been suggested to overcome the severe data requirements of kinetic modeling, including the use of approximative kinetics and Monte-Carlo sampling of reaction parameters. In this work, we employ a probabilistic approach to study the response of a complex metabolic system, the central metabolism of the lactic acid bacterium Lactococcus lactis, subject to perturbations and brief periods of starvation. Supplementing existing methodologies, we show that it is possible to acquire a detailed understanding of the control properties of a corresponding metabolic pathway model that is directly based on experimental observations. In particular, we delineate the role of enzymatic regulation to maintain metabolic stability and metabolic recovery after periods of starvation. It is shown that the feedforward activation of the pyruvate kinase by fructose-1,6-bisphosphate qualitatively alters the bifurcation structure of the corresponding pathway model, indicating a crucial role of enzymatic regulation to prevent metabolic collapse for low external concentrations of glucose. We argue that similar probabilistic methodologies will help our understanding of dynamic properties of small-, medium- and large-scale metabolic networks models.

KiMoSys: a web-based repository of experimental data for KInetic MOdels of biological SYStems.

BackgroundThe kinetic modeling of biological systems is mainly composed of three steps that proceed iteratively: model building, simulation and analysis. In the first step, it is usually required to set initial metabolite concentrations, and to assign kinetic rate laws, along with estimating parameter values using kinetic data through optimization when these are not known. Although the rapid development of high-throughput methods has generated much omics data, experimentalists present only a summary of obtained results for publication, the experimental data files are not usually submitted to any public repository, or simply not available at all. In order to automatize as much as possible the steps of building kinetic models, there is a growing requirement in the systems biology community for easily exchanging data in combination with models, which represents the main motivation of KiMoSys development.DescriptionKiMoSys is a user-friendly platform that includes a public data repository of published experimental data, containing concentration data of metabolites and enzymes and flux data. It was designed to ensure data management, storage and sharing for a wider systems biology community. This community repository offers a web-based interface and upload facility to turn available data into publicly accessible, centralized and structured-format data files. Moreover, it compiles and integrates available kinetic models associated with the data.KiMoSys also integrates some tools to facilitate the kinetic model construction process of large-scale metabolic networks, especially when the systems biologists perform computational research.ConclusionsKiMoSys is a web-based system that integrates a public data and associated model(s) repository with computational tools, providing the systems biology community with a novel application facilitating data storage and sharing, thus supporting construction of ODE-based kinetic models and collaborative research projects.The web application implemented using Ruby on Rails framework is freely available for web access at http://kimosys.org, along with its full documentation.

Variability of metabolite levels is linked to differential metabolic pathways in Arabidopsis's responses to abiotic stresses.

Constraint-based approaches have been used for integrating data in large-scale metabolic networks to obtain insights into metabolism of various organisms. Due to the underlying steady-state assumption, these approaches are usually not suited for making predictions about metabolite levels. Here, we ask whether we can make inferences about the variability of metabolite levels from a constraint-based analysis based on the integration of transcriptomics data. To this end, we analyze time-resolved transcriptomics and metabolomics data from Arabidopsis thaliana under a set of eight different light and temperature conditions. In a previous study, the gene expression data have already been integrated in a genome-scale metabolic network to predict pathways, termed modulators and sustainers, which are differentially regulated with respect to a biochemically meaningful data-driven null model. Here, we present a follow-up analysis which bridges the gap between flux- and metabolite-centric methods. One of our main findings demonstrates that under certain environmental conditions, the levels of metabolites acting as substrates in modulators or sustainers show significantly lower temporal variations with respect to the remaining measured metabolites. This observation is discussed within the context of a systems-view of plasticity and robustness of metabolite contents and pathway fluxes. Our study paves the way for investigating the existence of similar principles in other species for which both genome-scale networks and high-throughput metabolomics data of high quality are becoming increasingly available.

Complementary elementary modes for fast and efficient analysis of metabolic networks

Metabolic pathway analysis facilitates understanding or designing a complex metabolic system and enables prediction of steady-state metabolic flux distributions. A serious problem of elementary mode (EM) or extreme pathway (Expa) analysis is that the computational time increases exponentially with an increase in network sizes, which makes the computation of the EMs/Expas expensive and infeasible for large-scale networks. To overcome such problems, we proposed a fast and efficient algorithm named complementary EM (cEM) analysis. To achieve the computational time improvement, we employ the EM decomposition method that explores EMs or linear combinations of them which are responsible for the metabolic flux distributions. Flux balance analysis (FBA) is used to determine possible ranges of metabolic flux distributions as the input data necessary for the EM decomposition method. The maximum entropy principle (MEP) is employed as an objective function for estimating the coefficients of cEMs. To demonstrate the feasibility of cEM analysis, we compared it with EM/Expa analysis by using two medium-scale metabolic networks of Escherichia coli and a genome-scale metabolic network of head and neck cancer cells. The cEM analysis greatly reduces the computational time and memory cost, exposing a new window for large-scale metabolic network analysis.

Systems biology approaches for studying the pathogenesis of non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) is a progressive disease of increasing public health concern. In western populations the disease has an estimated prevalence of 20%-40%, rising to 70%-90% in obese and type II diabetic individuals. Simplistically, NAFLD is the macroscopic accumulation of lipid in the liver, and is viewed as the hepatic manifestation of the metabolic syndrome. However, the molecular mechanisms mediating both the initial development of steatosis and its progression through non-alcoholic steatohepatitis to debilitating and potentially fatal fibrosis and cirrhosis are only partially understood. Despite increased research in this field, the development of non-invasive clinical diagnostic tools and the discovery of novel therapeutic targets has been frustratingly slow. We note that, to date, NAFLD research has been dominated by in vivo experiments in animal models and human clinical studies. Systems biology tools and novel computational simulation techniques allow the study of large-scale metabolic networks and the impact of their dysregulation on health. Here we review current systems biology tools and discuss the benefits to their application to the study of NAFLD. We propose that a systems approach utilising novel in silico modelling and simulation techniques is key to a more comprehensive, better targeted NAFLD research strategy. Such an approach will accelerate the progress of research and vital translation into clinic.

Flux-based classification of reactions reveals a functional bow-tie organization of complex metabolic networks

Unraveling the structure of complex biological networks and relating it to their functional role is an important task in systems biology. Here we attempt to characterize the functional organization of the large-scale metabolic networks of three microorganisms. We apply flux balance analysis to study the optimal growth states of these organisms in different environments. By investigating the differential usage of reactions across flux patterns for different environments, we observe a striking bimodal distribution in the activity of reactions. Motivated by this, we propose a simple algorithm to decompose the metabolic network into three subnetworks. It turns out that our reaction classifier, which is blind to the biochemical role of pathways, leads to three functionally relevant subnetworks that correspond to input, output, and intermediate parts of the metabolic network with distinct structural characteristics. Our decomposition method unveils a functional bow-tie organization of metabolic networks that is different from the bow-tie structure determined by graph-theoretic methods that do not incorporate functionality.

Establishment, in silico analysis, and experimental verification of a large-scale metabolic network of the xanthan producing Xanthomonas campestris pv. campestris strain B100

The γ-proteobacterium Xanthomonas campestris pv. campestris (Xcc) B100 synthesizes the polysaccharide xanthan, a commercially important viscosifier. Since the complete genome of Xcc B100 is available, systems biology tools were applied to obtain a deeper understanding of the metabolism involved in xanthan biosynthesis. A large-scale metabolic network was reconstructed and manually curated. The reconstructed network included 352 genes, 437 biochemical reactions, 10 transport reactions, and 338 internal metabolites. To use this network for flux balance analysis, the biomass composition of Xcc B100 was determined. The comprehensive model obtained was applied for in silico analyses to predict biomass generation and gene essentiality. Predictions were extensively validated by analyzing batch culture performance and by carbon balancing including xanthan production. Single gene deletion mutants causing deficiencies in the central carbohydrate metabolism were constructed to enforce major flux redistributions. The impact of xanthan production was studied in vivo and in silico, comparing the physiology of a gumD mutant, negative in xanthan production, with the original strain. The results indicate a redistribution of resources from xanthan to biomass, rather than a reduction in carbon uptake. With this high quality metabolic model, both systems biology analyses and synthetic biology reengineering of Xcc gained an important tool.

On the identifiability of metabolic network models

A major problem for the identification of metabolic network models is parameter identifiability, that is, the possibility to unambiguously infer the parameter values from the data. Identifiability problems may be due to the structure of the model, in particular implicit dependencies between the parameters, or to limitations in the quantity and quality of the available data. We address the detection and resolution of identifiability problems for a class of pseudo-linear models of metabolism, so-called linlog models. Linlog models have the advantage that parameter estimation reduces to linear or orthogonal regression, which facilitates the analysis of identifiability. We develop precise definitions of structural and practical identifiability, and clarify the fundamental relations between these concepts. In addition, we use singular value decomposition to detect identifiability problems and reduce the model to an identifiable approximation by a principal component analysis approach. The criterion is adapted to real data, which are frequently scarce, incomplete, and noisy. The test of the criterion on a model with simulated data shows that it is capable of correctly identifying the principal components of the data vector. The application to a state-of-the-art dataset on central carbon metabolism in Escherichia coli yields the surprising result that only 4 out of 31 reactions, and 37 out of 100 parameters, are identifiable. This underlines the practical importance of identifiability analysis and model reduction in the modeling of large-scale metabolic networks. Although our approach has been developed in the context of linlog models, it carries over to other pseudo-linear models, such as generalized mass-action (power-law) models. Moreover, it provides useful hints for the identifiability analysis of more general classes of nonlinear models of metabolism.

Random sampling of elementary flux modes in large-scale metabolic networks.

Motivation: The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set.Results: Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks.Availability: Source code for a cross-platform implementation in Python is freely available at http://code.google.com/p/emsampler.Contact: dmachado@deb.uminho.ptSupplementary information: Supplementary data are available at Bioinformatics online.

REVISITING MODULES (AND CENTERS) IN STAPHYLOCOCCUS AUREUS METABOLIC NETWORK WITH LINK CLUSTERING

Community structure analysis methods are important tools in modeling and analyzing large-scale metabolic networks. However, traditional community structure methods are mainly solved by clustering nodes, which results in each metabolite belonging to only a single community, which limits their usefulness in the study of metabolic networks. In the present paper, we analyze the community structure and functional modules in the Staphylococcus aureus (S. aureus) metabolic network, using a link clustering algorithm, and we obtain 10 functional modules with better biological insights, which give better results than our previous study. We also evaluate the essentiality of nodes in S. aureus metabolic networks. We suggest that link clustering could identify functional modules and key metabolites in metabolic networks.

Modelling cyanobacteria: from metabolism to integrative models of phototrophic growth

Cyanobacteria are phototrophic microorganisms of global importance and have recently attracted increasing attention due to their capability to convert sunlight and atmospheric CO(2) directly into organic compounds, including carbon-based biofuels. The utilization of cyanobacteria as a biological chassis to generate third-generation biofuels would greatly benefit from an increased understanding of cyanobacterial metabolism and its interplay with other cellular processes. In this respect, metabolic modelling has been proposed as a way to overcome the traditional trial and error methodology that is often employed to introduce novel pathways. In particular, flux balance analysis and related methods have proved to be powerful tools to investigate the organization of large-scale metabolic networks-with the prospect of predicting modifications that are likely to increase the yield of a desired product and thereby to streamline the experimental progress and avoid futile avenues. This contribution seeks to describe the utilization of metabolic modelling as a research tool to understand the metabolism and phototrophic growth of cyanobacteria. The focus of the contribution is on a mathematical description of the metabolic network of Synechocystis sp. PCC 6803 and its analysis using constraint-based methods. A particular challenge is to integrate the description of the metabolic network with other cellular processes, such as the circadian clock, the photosynthetic light reactions, carbon concentration mechanism, and transcriptional regulation-aiming at a predictive model of a cyanobacterium in silico.

Predicting outcomes of steady-state 13C isotope tracing experiments using Monte Carlo sampling

BackgroundCarbon-13 (13C) analysis is a commonly used method for estimating reaction rates in biochemical networks. The choice of carbon labeling pattern is an important consideration when designing these experiments. We present a novel Monte Carlo algorithm for finding the optimal substrate input label for a particular experimental objective (flux or flux ratio). Unlike previous work, this method does not require assumption of the flux distribution beforehand.ResultsUsing a large E. coli isotopomer model, different commercially available substrate labeling patterns were tested computationally for their ability to determine reaction fluxes. The choice of optimal labeled substrate was found to be dependent upon the desired experimental objective. Many commercially available labels are predicted to be outperformed by complex labeling patterns. Based on Monte Carlo Sampling, the dimensionality of experimental data was found to be considerably less than anticipated, suggesting that effectiveness of 13C experiments for determining reaction fluxes across a large-scale metabolic network is less than previously believed.ConclusionsWhile 13C analysis is a useful tool in systems biology, high redundancy in measurements limits the information that can be obtained from each experiment. It is however possible to compute potential limitations before an experiment is run and predict whether, and to what degree, the rate of each reaction can be resolved.

Generic Enzymatic Rate Equation*

Kinetic modeling of large-scale metabolic network require a generic enzymatic rate equation.In the generic form,kinetic parameters are clear and precise enough to correlate to experimental data and construct a database.Such a uniform form is easy to deal with arbitrary number of substrates and products in computation of dynamic modeling.The generic rate equation is symmetrical in both directions of reversible reaction and formally exact under the quasi-steady state condition.Here presented the rigorous derivation of generic rate equation from further three classical enzymatic rate equations and discussed the characters and uses of the generic form.

Two-stage flux balance analysis of metabolic networks for drug target identification

BackgroundEfficient identification of drug targets is one of major challenges for drug discovery and drug development. Traditional approaches to drug target identification include literature search-based target prioritization and in vitro binding assays which are both time-consuming and labor intensive. Computational integration of different knowledge sources is a more effective alternative. Wealth of omics data generated from genomic, proteomic and metabolomic techniques changes the way researchers view drug targets and provides unprecedent opportunities for drug target identification.ResultsIn this paper, we develop a method based on flux balance analysis (FBA) of metabolic networks to identify potential drug targets. This method consists of two linear programming (LP) models, which first finds the steady optimal fluxes of reactions and the mass flows of metabolites in the pathologic state and then determines the fluxes and mass flows in the medication state with the minimal side effect caused by the medication. Drug targets are identified by comparing the fluxes of reactions in both states and examining the change of reaction fluxes. We give an illustrative example to show that the drug target identification problem can be solved effectively by our method, then apply it to a hyperuricemia-related purine metabolic pathway. Known drug targets for hyperuricemia are correctly identified by our two-stage FBA method, and the side effects of these targets are also taken into account. A number of other promising drug targets are found to be both effective and safe.ConclusionsOur method is an efficient procedure for drug target identification through flux balance analysis of large-scale metabolic networks. It can generate testable predictions, provide insights into drug action mechanisms and guide experimental design of drug discovery.

Construction of kinetic models for metabolic reaction networks: Lessons learned in analysing short-term stimulus response data

Construction of dynamic models of large-scale metabolic networks is one of the central issues in the engineering of living cells. However, construction of such models is often hampered by a number of challenges, for example, data availability, compartmentalization and parameter identification coupled with design of in vivo perturbations. As a solution to the latter, short-term perturbation experiments are proposed and are proven to be a useful experimental method to obtain insights into the in vivo kinetic properties of the metabolic pathways. The aim of this work is to construct a kinetic model using the available experimental data obtained by short-term perturbation experiments, where the steady state of a glucose-limited anaerobic chemostat culture of Saccharomyces cerevisiae was perturbed. In constructing the model, we first determined the steady-state flux distribution using the data before the glucose pulse and the known stoichiometry. For the rate expressions, we used approximative linlog kinetics, which allows the enzyme–metabolite kinetic interactions to be represented by an elasticity matrix. We performed a priori model reduction based on timescale analysis and parameter identifiability analysis allowing the information content of the experimental data to be assessed. The final values of the elasticities are estimated by fitting the model to the available short-term kinetic response data. The final model consists of 16 metabolites and 14 reactions. With 25 parameters, the model adequately describes the short-term response of the cells to the glucose perturbation, pointing to the fact that the assumed kinetic interactions in the model are sufficient to account for the observed response.

Soft constraints-based multiobjective framework for flux balance analysis

The current state of the art for linear optimization in Flux Balance Analysis has been limited to single objective functions. Since mammalian systems perform various functions, a multiobjective approach is needed when seeking optimal flux distributions in these systems. In most of the available multiobjective optimization methods, there is a lack of understanding of when to use a particular objective, and how to combine and/or prioritize mutually competing objectives to achieve a truly optimal solution. To address these limitations we developed a soft constraints based linear physical programming-based flux balance analysis (LPPFBA) framework to obtain a multiobjective optimal solutions. The developed framework was first applied to compute a set of multiobjective optimal solutions for various pairs of objectives relevant to hepatocyte function (urea secretion, albumin, NADPH, and glutathione syntheses) in bioartificial liver systems. Next, simultaneous analysis of the optimal solutions for three objectives was carried out. Further, this framework was utilized to obtain true optimal conditions to improve the hepatic functions in a simulated bioartificial liver system. The combined quantitative and visualization framework of LPPFBA is applicable to any large-scale metabolic network system, including those derived by genomic analyses.

Evaluating the integration of proteomic data for the prediction of intracellular fluxes after knockout experiments

So far, few large scale kinetic models of metabolic networks have been successfully constructed. The main reasons for this are not only the associated mathematical complexity, but also the large number of unknown kinetic parameters required in the rate equations to define the system. In contrast to kinetic models, the constraint-based modelling approach bypasses these difficulties by using basically only stoichiometric information with certain physicochemical constraints to delimit the solution space without large fitted parameter sets. Although these constraint-based models are highly relevant to predict feasible steady-state fluxes under a diverse range of genetic and environmental conditions, the steady-state assumption may oversimplify cellular behaviour and cannot predict time-course profiles. To overcome these problems, combining these two approaches appears as a reasonable alternative to modelling large-scale metabolic networks. On the other hand, several of the experimental data required for model construction are often rare and in this way it is usually assumed that the enzyme concentrations are constant.In this work, we used a central carbon metabolic network of E. coli to investigate whether including high throughput enzyme concentration data into a kinetic model allows improved predictions of metabolic flux distributions in response to single knockouts perturbations. For this purpose, an E. coli model, based on results obtained from flux balance analysis (FBA) and approximate lin-log kinetics was constructed. The intracellular fluxes distributions, obtained using this model, were compared with published in vivo measurements.

Genome‐scale metabolic networks

During the last decade, models have been developed to characterize cellular metabolism at the level of an entire metabolic network. The main concept that underlies whole-network metabolic modeling is the identification and mathematical definition of constraints. Here, we review large-scale metabolic network modeling, in particular, stoichiometric- and constraint-based approaches. Although many such models have been reconstructed, few networks have been extensively validated and tested experimentally, and we focus on these. We describe how metabolic networks can be represented using stoichiometric matrices and well-defined constraints on metabolic fluxes. We then discuss relatively successful approaches, including flux balance analysis (FBA), pathway analysis, and common extensions or modifications to these approaches. Finally, we describe techniques for integrating these approaches with models of other biological processes.