Structure Of Metabolic Networks Research Articles

A System Identification Based Framework for Genome-Scale Metabolic Model Validation and Refinement

Due to the scale and complexity of genome-scale metabolic models (GEMs), it has been recognized that one of the major challenges of metabolic network modeling is the evaluation and refinement of GEMs. Currently, besides assessing its size and connectivity, the standard approach for GEM validation is to compare model predictions with experimental data under different conditions. For well-characterized organisms, this point-matching approach works well, because their metabolic network structures have been well-studied and well-defined. However, we show in this work that, for a less studied organism, such an approach did not work well as the combination of multiple model errors resulted in good agreement between the model predictions and experimental data over multiple points. To address it, we propose a system identification (SID) based framework for GEM validation and refinement. The validation and refinement of GEMs for a model yeast Scheffersomyces stipitis is used as the case study to demonstrate the effectiveness of the SID framework.

The Response to 2-Aminoacrylate Differs in Escherichia coli and Salmonella enterica, despite Shared Metabolic Components.

The metabolic network of an organism includes the sum total of the biochemical reactions present. In microbes, this network has an impeccable ability to sense and respond to perturbations caused by internal or external stimuli. The metabolic potential (i.e., network structure) of an organism is often drawn from the genome sequence, based on the presence of enzymes deemed to indicate specific pathways. Escherichia coli and Salmonella enterica are members of the Enterobacteriaceae family of Gram-negative bacteria that share the majority of their metabolic components and regulatory machinery as the "core genome." In S. enterica, the ability of the enamine intermediate 2-aminoacrylate (2AA) to inactivate a number of pyridoxal 5'-phosphate (PLP)-dependent enzymes has been established in vivo In this study, 2AA metabolism and the consequences of its accumulation were investigated in E. coli The data showed that despite the conservation of all relevant enzymes, S. enterica and E. coli differed in both the generation and detrimental consequences of 2AA. In total, these findings suggest that the structure of the metabolic network surrounding the generation and response to endogenous 2AA stress differs between S. enterica and E. coliIMPORTANCE This work compared the metabolic networks surrounding the endogenous stressor 2-aminoacrylate in two closely related members of the Enterobacteriaceae The data showed that despite the conservation of all relevant enzymes in this metabolic node, the two closely related organisms diverged in their metabolic network structures. This work highlights how a set of conserved components can generate distinct network architectures and how this can impact the physiology of an organism. This work defines a model to expand our understanding of the 2-aminoacrylate stress response and the differences in metabolic structures and cellular milieus between S. enterica and E. coli.

Redesigning metabolism based on orthogonality principles

Modifications made during metabolic engineering for overproduction of chemicals have network-wide effects on cellular function due to ubiquitous metabolic interactions. These interactions, that make metabolic network structures robust and optimized for cell growth, act to constrain the capability of the cell factory. To overcome these challenges, we explore the idea of an orthogonal network structure that is designed to operate with minimal interaction between chemical production pathways and the components of the network that produce biomass. We show that this orthogonal pathway design approach has significant advantages over contemporary growth-coupled approaches using a case study on succinate production. We find that natural pathways, fundamentally linked to biomass synthesis, are less orthogonal in comparison to synthetic pathways. We suggest that the use of such orthogonal pathways can be highly amenable for dynamic control of metabolism and have other implications for metabolic engineering.

Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA.

Genome-scale metabolic network reconstructions (GENREs) are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA). We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.

Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks.

Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties.

Evolution of Centrality Measurements for the Detection of Essential Proteins in Biological Networks.

Current breakthroughs in high-throughput technologies have propelled the development of databases that systematically store knowledge of how genes, proteins, and metabolites interact. To elucidate the mechanisms of molecular interaction, such data can be represented through networks where nodes are biological entities (e.g., gene, protein, miRNA, transcription factor, and metabolites) and edges are associations/interactions between them (e.g., co-expression, signaling, regulation, and physical interaction). One approach to use such networks is to analyze their topological structure and try to relate this to biological function. Topological analysis hints at the possible behavior of a network in the regulation of biological processes or phenotypes and help in unveiling the core mechanisms. Broadly speaking, topological parameters can be used to explore: (1) collective behaviors (global properties such as diameter, small-world and scale-free properties of a network), (2) subnetwork behaviors (functional motif discovery), and (3) individual behaviors (prioritization of important nodes by centrality indices) of various network components (Ma and Gao, 2012). One of the first attempts found in the literature considered centrality related to lethality, and is known as the centrality–lethality rule proposed by Jeong et al. indicating a positive correlation between connectivity and indispensability in the yeast protein-protein interaction map (Jeong et al., 2001). Similarly, Wagner and Fell analyzed the structure of a large metabolic network of E. coli using metabolite node degree and shortest mean path length and observed small world like properties that follow power-law distributions (Wagner and Fell, 2001). In these two comprehensive studies, an old metric system (centrality index) was applied with different strategies, aiming to answer the following question: Do centrality indices predict the essential nodes in the biological networks? Remarkably, topological analyses carried out in transcriptional regulatory (TR) and metabolic networks have been a valuable guide to identify those biological components, called essential nodes, that play a major role in vital functional activities for some microorganisms (Resendis-Antonio et al., 2005, 2012). The relationship between nodes topological features, such as their degree, and their essentiality remains however debated (Coulomb et al., 2005). Prediction of essential proteins is a challenging task because it needs experimental approaches that are expensive, time-consuming, and laborious (Zhong et al., 2013; Li et al., 2014). To optimize the search of essential nodes in biological networks, a series of computational methods that include topological criteria have been proposed. In this paper, we review the cutting edge computational methods by categorizing them according to their underlying strategies to identify essential components. In each case, we discuss their predictive experimental power and identify shortcomings.

Functional centrality as a predictor of shifts in metabolic flux states.

BackgroundThe flux phenotype describes the entirety of biochemical conversions in a cell, which renders it a key characteristic of metabolic function. To quantify the functional relevance of individual biochemical reactions, functional centrality has been introduced based on cooperative game theory and structural modeling. It was shown to be capable to determine metabolic control properties utilizing only structural information. Here, we demonstrate the capability of functional centrality to predict changes in the flux phenotype.ResultsWe use functional centrality to successfully predict changes of metabolic flux triggered by switches in the environment. The predictions via functional centrality improve upon predictions using control-effective fluxes, another measure aiming at capturing metabolic control using structural information.ConclusionsThe predictions of flux changes via functional centrality corroborate the capability of the measure to gain a mechanistic understanding of metabolic control from the structure of metabolic networks.

Metabolic network structure and function in bacteria goes beyond conserved enzyme components

For decades, experimental work has laid the foundation for our understanding of the linear and branched pathways that are integrated to form the metabolic networks on which life is built. Genetic and biochemical approaches applied in model organisms generate empirical data that correlate genes, gene products and their biological activities. In the post-genomic era, these results have served as the basis for the genome annotation that is routinely used to infer the metabolic capabilities of an organism and mathematically model the presumed metabolic network structure. At large, genome annotation and metabolic network reconstructions have demystified genomic content of non-culturable microorganisms and allowed researchers to explore the breadth of metabolisms in silico. Mis-annotation aside, it is unclear whether in silico reconstructions of metabolic structure from component parts accurately captures the higher levels of network organization and flux distribution. For this approach to provide accurate predictions, one must assume that the conservation of metabolic components leads to conservation of metabolic network architecture and function. This assumption has not been rigorously tested. Here we describe the implications of a recent study (MBio 5;7(1): e01840-15), which demonstrated that conservation of metabolic components was not sufficient to predict network structure and function.

Metabolic Network Constrains Gene Regulation of C4 Photosynthesis: The Case of Maize.

Engineering C3 plants to increase their efficiency of carbon fixation as well as of nitrogen and water use simultaneously may be facilitated by understanding the mechanisms that underpin the C4 syndrome. Existing experimental studies have indicated that the emergence of the C4 syndrome requires co-ordination between several levels of cellular organization, from gene regulation to metabolism, across two co-operating cell systems—mesophyll and bundle sheath cells. Yet, determining the extent to which the structure of the C4 plant metabolic network may constrain gene expression remains unclear, although it will provide an important consideration in engineering C4 photosynthesis in C3 plants. Here, we utilize flux coupling analysis with the second-generation maize metabolic models to investigate the correspondence between metabolic network structure and transcriptomic phenotypes along the maize leaf gradient. The examined scenarios with publically available data from independent experiments indicate that the transcriptomic programs of the two cell types are co-ordinated, quantitatively and qualitatively, due to the presence of coupled metabolic reactions in specific metabolic pathways. Taken together, our study demonstrates that precise quantitative coupling will have to be achieved in order to ensure a successfully engineered transition from C3 to C4 crops.

Control analysis of the impact of allosteric regulation mechanism in a Escherichia coli kinetic model: Application to serine production

Kinetic modeling is a key aspect of systems biology with biotechnological applications. However, a limitation of building kinetic models of metabolism (particularly from stoichiometric reconstructions of metabolic networks) is that they often ignore the allosteric regulators. This can cause discrepancies in the model predictions. In this paper, we derived an approximated lin-log ODE model of the Escherichia coli central carbon metabolism, with and without metabolite-enzyme regulators. Next, we analyzed the influence of incorporating this level of metabolite-enzyme interactions in the metabolic network by performing several in silico single-gene knockouts and enzyme under-/over-expression changes. Through comparing these model predictions with those generated with a reference mechanistic kinetic model for E. coli, it is shown that including of allosteric regulation affects the flux control patterns over serine production and reveals more details of the model behavior in a general sense. The present work demonstrates that the regulatory (allosteric) structure in metabolic networks plays an essential role to further improve kinetic model prediction capabilities. The incorporation of allosteric regulation interactions in building a kinetic model can lead to different hypotheses in order to suggest enzyme targets for strain design through metabolic engineering.

Resource constrained flux balance analysis predicts selective pressure on the global structure of metabolic networks.

BackgroundA universal feature of metabolic networks is their hourglass or bow-tie structure on cellular level. This architecture reflects the conversion of multiple input nutrients into multiple biomass components via a small set of precursor metabolites. However, it is yet unclear to what extent this structural feature is the result of natural selection.ResultsWe extend flux balance analysis to account for limited cellular resources. Using this model, optimal structure of metabolic networks can be calculated for different environmental conditions. We observe a significant structural reshaping of metabolic networks for a toy-network and E. coli core metabolism if we increase the share of invested resources for switching between different nutrient conditions. Here, hub nodes emerge and the optimal network structure becomes bow-tie-like as a consequence of limited cellular resource constraint. We confirm this theoretical finding by comparing the reconstructed metabolic networks of bacterial species with respect to their lifestyle.ConclusionsWe show that bow-tie structure can give a system-level fitness advantage to organisms that live in highly competitive and fluctuating environments. Here, limitation of cellular resources can lead to an efficiency-flexibility tradeoff where it pays off for the organism to shorten catabolic pathways if they are frequently activated and deactivated. As a consequence, generalists that shuttle between diverse environmental conditions should have a more predominant bow-tie structure than specialists that visit just a few isomorphic habitats during their life cycle.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0232-5) contains supplementary material, which is available to authorized users.

Phylogeny of metabolic networks: a spectral graph theoretical approach.

Many methods have been developed for finding the commonalities between different organisms in order to study their phylogeny. The structure of metabolic networks also reveals valuable insights into metabolic capacity of species as well as into the habitats where they have evolved. We constructed metabolic networks of 79 fully sequenced organisms and compared their architectures. We used spectral density of normalized Laplacian matrix for comparing the structure of networks. The eigenvalues of this matrix reflect not only the global architecture of a network but also the local topologies that are produced by different graph evolutionary processes like motif duplication or joining. A divergence measure on spectral densities is used to quantify the distances between various metabolic networks, and a split network is constructed to analyse the phylogeny from these distances. In our analysis, we focused on the species that belong to different classes, but appear more related to each other in the phylogeny. We tried to explore whether they have evolved under similar environmental conditions or have similar life histories. With this focus, we have obtained interesting insights into the phylogenetic commonality between different organisms.

Induction of the Sugar-Phosphate Stress Response Allows Saccharomyces cerevisiae 2-Methyl-4-Amino-5-Hydroxymethylpyrimidine Phosphate Synthase To Function in Salmonella enterica.

Thiamine pyrophosphate is a required cofactor for all forms of life. The pyrimidine moiety of thiamine, 2-methyl-4-amino-5-hydroxymethylpyrimidine phosphate (HMP-P), is synthesized by different mechanisms in bacteria and plants compared to fungi. In this study, Salmonella enterica was used as a host to probe requirements for activity of the yeast HMP-P synthase, Thi5p. Thi5p synthesizes HMP-P from histidine and pyridoxal-5-phosphate and was reported to use a backbone histidine as the substrate, which would mean that it was a single-turnover enzyme. Heterologous expression of Thi5p did not complement an S. enterica HMP-P auxotroph during growth with glucose as the sole carbon source. Genetic analyses described here showed that Thi5p was activated in S. enterica by alleles of sgrR that induced the sugar-phosphate stress response. Deletion of ptsG (encodes enzyme IICB [EIICB] of the phosphotransferase system [PTS]) also allowed function of Thi5p and required sgrR but not sgrS. This result suggested that the role of sgrS in activation of Thi5p was to decrease PtsG activity. In total, the data herein supported the hypothesis that one mechanism to activate Thi5p in S. enterica grown on minimal medium containing glucose (minimal glucose medium) required decreased PtsG activity and an unidentified gene regulated by SgrR. This work describes a metabolic link between the sugar-phosphate stress response and the yeast thiamine biosynthetic enzyme Thi5p when heterologously expressed in Salmonella enterica during growth on minimal glucose medium. Suppressor analysis (i) identified a mutant class of the regulator SgrR that activate sugar-phosphate stress response constitutively and (ii) determined that Thi5p is conditionally active in S. enterica. These results emphasized the power of genetic systems in model organisms to uncover enzyme function and underlying metabolic network structure.

MetabNet: An R Package for Metabolic Association Analysis of High-Resolution Metabolomics Data.

Liquid-chromatography high-resolution mass spectrometry provides capability to measure >40,000 ions derived from metabolites in biologic samples. This presents challenges to confirm identities of known chemicals and delineate potential metabolic pathway associations of unidentified chemicals. We provide an R package for metabolic network analysis, MetabNet, to perform targeted metabolome-wide association study of specific metabolites to facilitate detection of their related metabolic pathways and network structures.

Microbial diversity and metabolic networks in acid mine drainage habitats.

Acid mine drainage (AMD) emplacements are low-complexity natural systems. Low-pH conditions appear to be the main factor underlying the limited diversity of the microbial populations thriving in these environments, although temperature, ionic composition, total organic carbon, and dissolved oxygen are also considered to significantly influence their microbial life. This natural reduction in diversity driven by extreme conditions was reflected in several studies on the microbial populations inhabiting the various micro-environments present in such ecosystems. Early studies based on the physiology of the autochthonous microbiota and the growing success of omics-based methodologies have enabled a better understanding of microbial ecology and function in low-pH mine outflows; however, complementary omics-derived data should be included to completely describe their microbial ecology. Furthermore, recent updates on the distribution of eukaryotes and archaea recovered through sterile filtering (herein referred to as filterable fraction) in these environments demand their inclusion in the microbial characterization of AMD systems. In this review, we present a complete overview of the bacterial, archaeal (including filterable fraction), and eukaryotic diversity in these ecosystems, and include a thorough depiction of the metabolism and element cycling in AMD habitats. We also review different metabolic network structures at the organismal level, which is necessary to disentangle the role of each member of the AMD communities described thus far.

Differential metabolic and coexpression networks of plant metabolism.

Recent analyses have demonstrated that plant metabolic networks do not differ in their structural properties and that genes involved in basic metabolic processes show smaller coexpression than genes involved in specialized metabolism. By contrast, our analysis reveals differences in the structure of plant metabolic networks and patterns of coexpression for genes in (non)specialized metabolism. Here we caution that conclusions concerning the organization of plant metabolism based on network-driven analyses strongly depend on the computational approaches used.

Dynamic Modeling and Metabolic Flux Analysis for Optimized Production of Rhamnolipids

A comprehensive metabolic network based on the fundamental pathways representing the central metabolism of rhamnolipid by Pseudomonas aeruginosa is proposed and a dynamic model compatible with the underlying metabolic network is developed involving the macro-reactions derived from the elementary flux modes of the reaction network. The experimentally validated mathematical model is then coupled with a global optimization technique called differential evolution (DE) to optimize the medium composition as well as the extracellular and intracellular fluxes of the metabolic network. The analysis of the results shows the usefulness of the integrated approach involving the development of a dynamic model based on the metabolic network structure and model-based optimization of the medium composition and metabolic fluxes by an efficient evolutionary optimization technique to enhance the productivity of rhamnolipid.

Stable isotope-labeling studies in metabolomics: new insights into structure and dynamics of metabolic networks.

The rapid emergence of metabolomics has enabled system-wide measurements of metabolites in various organisms. However, advances in the mechanistic understanding of metabolic networks remain limited, as most metabolomics studies cannot routinely provide accurate metabolite identification, absolute quantification and flux measurement. Stable isotope labeling offers opportunities to overcome these limitations. Here we describe some current approaches to stable isotope-labeled metabolomics and provide examples of the significant impact that these studies have had on our understanding of cellular metabolism. Furthermore, we discuss recently developed software solutions for the analysis of stable isotope-labeled metabolomics data and propose the bioinformatics solutions that will pave the way for the broader application and optimal interpretation of system-scale labeling studies in metabolomics.

Sparsity as Cellular Objective to Infer Directed Metabolic Networks from Steady-State Metabolome Data: A Theoretical Analysis

Since metabolome data are derived from the underlying metabolic network, reverse engineering of such data to recover the network topology is of wide interest. Lyapunov equation puts a constraint to the link between data and network by coupling the covariance of data with the strength of interactions (Jacobian matrix). This equation, when expressed as a linear set of equations at steady state, constitutes a basis to infer the network structure given the covariance matrix of data. The sparse structure of metabolic networks points to reactions which are active based on minimal enzyme production, hinting at sparsity as a cellular objective. Therefore, for a given covariance matrix, we solved Lyapunov equation to calculate Jacobian matrix by a simultaneous use of minimization of Euclidean norm of residuals and maximization of sparsity (the number of zeros in Jacobian matrix) as objective functions to infer directed small-scale networks from three kingdoms of life (bacteria, fungi, mammalian). The inference performance of the approach was found to be promising, with zero False Positive Rate, and almost one True positive Rate. The effect of missing data on results was additionally analyzed, revealing superiority over similarity-based approaches which infer undirected networks. Our findings suggest that the covariance of metabolome data implies an underlying network with sparsest pattern. The theoretical analysis forms a framework for further investigation of sparsity-based inference of metabolic networks from real metabolome data.

Modeling Complex Metabolic Reactions, Ecological Systems, and Financial and Legal Networks with MIANN Models Based on Markov-Wiener Node Descriptors

The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order k(th) (W(k)). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the W(k)(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated W(k)(i) values were used as inputs for different ANNs in order to discriminate correct node connectivity patterns from incorrect random patterns. The MIANN models obtained present good values of Sensitivity/Specificity (%): MRNs (78/78), IWDBNs (90/88), and SFLN (86/84). These preliminary results are very promising from the point of view of a first exploratory study and suggest that the use of these models could be extended to the high-throughput re-evaluation of connectivity in known complex networks (collation).