Metabolic networks
Metabolic Networks
It used to be called Metabolic Pathways, but even early concepts of metabolism realized that there are multiple interconnections that expose our notion of clearly delineated sequences to be a barely appropriate conceptual simplification. The problem is confounded by the fact that many reactions that would not progress at reasonable rates in the laboratory, may in fact be quite favourable given the crowded, compartmentalized environment of the cell's interior. Therefore, a crucial question of the analysis of metabolic networks is: which paths are meaningful?
Introductory reading
| Faust et al. (2011) Prediction of metabolic pathways from genome-scale metabolic networks. BioSystems 105:109-21. (pmid: 21645586) |
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[ PubMed ] [ DOI ] The analysis of a variety of data sets (transcriptome arrays, phylogenetic profiles, etc.) yields groups of functionally related genes. In order to determine their biological function, associated gene groups are often projected onto known pathways or tested for enrichment of known functions. However, these approaches are not flexible enough to deal with variations or novel pathways. During the last decade, we developed and refined an approach that predicts metabolic pathways from a global metabolic network encompassing all known reactions and their substrates/products, by extracting a subgraph connecting at best a set of seed nodes (compounds, reactions, enzymes or enzyme-coding genes). In this review, we summarize this work, while discussing the problems and pitfalls but also the advantages and applications of network-based metabolic pathway prediction. |
Contents
Exercises
| Latendresse et al. (2012) Browsing metabolic and regulatory networks with BioCyc. Methods Mol Biol 804:197-216. (pmid: 22144155) |
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[ PubMed ] [ DOI ] The BioCyc database collection at BioCyc.org integrates genome and cellular network information for more than 1,100 organisms. This method chapter describes Web-based tools for browsing metabolic and regulatory networks within BioCyc. These tools allow visualization of complete metabolic and regulatory networks, and allow the user to zoom-in on regions of the network of interest. The user can find objects of interest such as genes and metabolites within the networks, and can selectively examine the connectivity of the network. The EcoCyc database within the BioCyc collection has been extensively curated. The descriptions within EcoCyc of the Escherichia coli metabolic network and regulatory network were derived from thousands of publications. Other BioCyc databases received moderate levels of curation, or no curation at all. Those databases receiving no curation contain metabolic networks that were computationally inferred from the annotated genome sequences of each organism. |
Further reading and resources
- Principles
| Geyer (2013) Modeling metabolic processes between molecular and systems biology. Curr Opin Struct Biol 23:218-23. (pmid: 23290351) |
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[ PubMed ] [ DOI ] Currently, there are two main 'schools' to handle the complexity of a living cell, the molecular biological paradigm that for each cellular function there is a key enzyme, and the complementary systems biological view that function emerges from the connectivity of the many enzymes. Here I argue that a combined molecular-systemic modeling ansatz would combine the strengths of both concepts and allow to build models that are detailed on the local level, reflect the high connectivity of cellular metabolism, and are still manageable. An overview over recent modeling advances shows that the tools and techniques are available for a hierarchic setup of even large systems form local rules which are then suited for a systemic analysis and parameterization. |
| Arita (2012) From metabolic reactions to networks and pathways. Methods Mol Biol 804:93-106. (pmid: 22144150) |
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[ PubMed ] [ DOI ] Enzymatic reactions form a hypergraph structure and their translation into a graph structure accompanies an information loss. This chapter introduces well-known topological transformations from metabolic reactions to a graph, and discusses their advantages and disadvantages. Also discussed is the legitimacy of defining cofactors or currency metabolites, and suitable application area of each representation. |
| Zhang et al. (2011) Metabolomics, pathway regulation, and pathway discovery. J Biol Chem 286:23631-5. (pmid: 21566142) |
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[ PubMed ] [ DOI ] Metabolomics is a data-based research strategy, the aims of which are to identify biomarker pictures of metabolic systems and metabolic perturbations and to formulate hypotheses to be tested. It involves the assay by mass spectrometry or NMR of many metabolites present in the biological system investigated. In this minireview, we outline studies in which metabolomics led to useful biomarkers of metabolic processes. We also illustrate how the discovery potential of metabolomics is enhanced by associating it with stable isotopic techniques. |
| Palsson (2009) Metabolic systems biology. FEBS Lett 583:3900-4. (pmid: 19769971) |
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[ PubMed ] [ DOI ] The first full genome sequences were established in the mid-1990s. Shortly thereafter, genome-scale metabolic network reconstructions appeared. Since that time, we have witnessed an exponential growth in their number and uses. Here I discuss, from a personal point of view, four topics: (1) the placement of metabolic systems biology in the context of broader scientific developments, (2) its foundational concepts, (3) some of its current uses, and (4) some of the expected future developments in the field. |
- Network definition
| Faust & van Helden (2012) Predicting metabolic pathways by sub-network extraction. Methods Mol Biol 804:107-30. (pmid: 22144151) |
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[ PubMed ] [ DOI ] Various methods result in groups of functionally related genes obtained from genomes (operons, regulons, syntheny groups, and phylogenetic profiles), transcriptomes (co-expression groups) and proteomes (modules of interacting proteins). When such groups contain two or more enzyme-coding genes, graph analysis methods can be applied to extract a metabolic pathway that interconnects them. We describe here the way to use the Pathway extraction tool available on the NeAT Web server ( http://rsat.ulb.ac.be/neat/ ) to piece together the metabolic pathway from a group of associated, enzyme-coding genes. The tool identifies the reactions that can be catalyzed by the products of the query genes (seed reactions), and applies sub-graph extraction algorithms to extract from a metabolic network a sub-network that connects the seed reactions. This sub-network represents the predicted metabolic pathway. We describe here the pathway prediction process in a step-by-step way, give hints about the main parametric choices, and illustrate how this tool can be used to extract metabolic pathways from bacterial genomes, on the basis of two study cases: the isoleucine-valine operon in Escherichia coli and a predicted operon in Cupriavidus (Ralstonia) metallidurans. |
| Haggart et al. (2011) Whole-genome metabolic network reconstruction and constraint-based modeling. Meth Enzymol 500:411-33. (pmid: 21943909) |
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[ PubMed ] [ DOI ] With the advent of modern high-throughput genomics, there is a significant need for genome-scale analysis techniques that can assist in complex systems analysis. Metabolic genome-scale network reconstructions (GENREs) paired with constraint-based modeling are an efficient method to integrate genomics, transcriptomics, and proteomics to conduct organism-specific analysis. This text explains key steps in the GENRE construction process and several methods of constraint-based modeling that can help elucidate basic life processes and development of disease treatment, bioenergy solutions, and industrial bioproduction applications. |
| Croes et al. (2005) Metabolic PathFinding: inferring relevant pathways in biochemical networks. Nucleic Acids Res 33:W326-30. (pmid: 15980483) |
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[ PubMed ] [ DOI ] Our knowledge of metabolism can be represented as a network comprising several thousands of nodes (compounds and reactions). Several groups applied graph theory to analyse the topological properties of this network and to infer metabolic pathways by path finding. This is, however, not straightforward, with a major problem caused by traversing irrelevant shortcuts through highly connected nodes, which correspond to pool metabolites and co-factors (e.g. H2O, NADP and H+). In this study, we present a web server implementing two simple approaches, which circumvent this problem, thereby improving the relevance of the inferred pathways. In the simplest approach, the shortest path is computed, while filtering out the selection of highly connected compounds. In the second approach, the shortest path is computed on the weighted metabolic graph where each compound is assigned a weight equal to its connectivity in the network. This approach significantly increases the accuracy of the inferred pathways, enabling the correct inference of relatively long pathways (e.g. with as many as eight intermediate reactions). Available options include the calculation of the k-shortest paths between two specified seed nodes (either compounds or reactions). Multiple requests can be submitted in a queue. Results are returned by email, in textual as well as graphical formats (available in http://www.scmbb.ulb.ac.be/pathfinding/). |
- Applications
| Papp et al. (2011) Use of genome-scale metabolic models in evolutionary systems biology. Methods Mol Biol 759:483-97. (pmid: 21863504) |
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[ PubMed ] [ DOI ] One of the major aims of the nascent field of evolutionary systems biology is to test evolutionary hypotheses that are not only realistic from a population genetic point of view but also detailed in terms of molecular biology mechanisms. By providing a mapping between genotype and phenotype for hundreds of genes, genome-scale systems biology models of metabolic networks have already provided valuable insights into the evolution of metabolic gene contents and phenotypes of yeast and other microbial species. Here we review the recent use of these computational models to predict the fitness effect of mutations, genetic interactions, evolutionary outcomes, and to decipher the mechanisms of mutational robustness. While these studies have demonstrated that even simplified models of biochemical reaction networks can be highly informative for evolutionary analyses, they have also revealed the weakness of this modeling framework to quantitatively predict mutational effects, a challenge that needs to be addressed for future progress in evolutionary systems biology. |
| Teusink et al. (2010) Comparative systems biology: from bacteria to man. Wiley Interdiscip Rev Syst Biol Med 2:518-532. (pmid: 20836045) |
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[ PubMed ] [ DOI ] Comparative analyses, as carried out by comparative genomics and bioinformatics, have proven extremely powerful to obtain insight into the identity of specific genes that underlie differences and similarities across species. The central concept developed in this chapter is that important aspects of the functional differences between organisms derive not only from the differences in genetic components (which underlies comparative genomics) but also from dynamic, molecular (physical) interactions. Approaches that aim at identifying such network-based rather than component-based homologies between species we shall call Comparative Systems Biology. It will be illustrated by a number of examples from metabolic networks from prokaryotes, via yeast, to man. The potential for species comparisons, at the genome-scale using classical approaches and at the more detailed level of dynamic molecular networks will be illustrated. In our opinion, comparative systems biology, as a marriage between bioinformatics and systems biology, will offer new insights into the nature of organisms for the benefit of medicine, biotechnology, and drug design. As dynamic modeling is becoming more mainstream in cell biology, the potential of comparative systems biology will become more evident. |
| Terzer et al. (2009) Genome-scale metabolic networks. Wiley Interdiscip Rev Syst Biol Med 1:285-297. (pmid: 20835998) |
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[ PubMed ] [ DOI ] 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. |
| Domingues et al. (2010) Optimization strategies for metabolic networks. BMC Syst Biol 4:113. (pmid: 20707903) |
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[ PubMed ] [ DOI ] BACKGROUND: The increasing availability of models and data for metabolic networks poses new challenges in what concerns optimization for biological systems. Due to the high level of complexity and uncertainty associated to these networks the suggested models often lack detail and liability, required to determine the proper optimization strategies. A possible approach to overcome this limitation is the combination of both kinetic and stoichiometric models. In this paper three control optimization methods, with different levels of complexity and assuming various degrees of process information, are presented and their results compared using a prototype network. RESULTS: The results obtained show that Bi-Level optimization lead to a good approximation of the optimum attainable with the full information on the original network. Furthermore, using Pontryagin's Maximum Principle it is shown that the optimal control for the network in question, can only assume values on the extremes of the interval of its possible values. CONCLUSIONS: It is shown that, for a class of networks in which the product that favors cell growth competes with the desired product yield, the optimal control that explores this trade-off assumes only extreme values. The proposed Bi-Level optimization led to a good approximation of the original network, allowing to overcome the limitation on the available information, often present in metabolic network models. Although the prototype network considered, it is stressed that the results obtained concern methods, and provide guidelines that are valid in a wider context. |
