The connectivity structure, giant strong component
and centrality of metabolic networks

Ma, Hongwu; Zeng, An‐Ping

doi:10.1093/bioinformatics/btg177

Cited by 344 publications

(318 citation statements)

References 29 publications

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“…3) through long chains of reactions. The bowtie structure of metabolism was independently derived from computational analyses [7]. The biologically natural modular decomposition of components is organised by the global structure into a 'knot' (of carriers and precursors) and non-'knot' parts of the 'bowtie'.…”

Section: Basic Features Of Metabolic Networkmentioning

confidence: 99%

Highly optimised global organisation of metabolic networks

Tanaka¹,

Csete

Doyle

2005

IEE Proc. Syst. Biol.

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High-level, mathematically precise descriptions of the global organisation of complex metabolic networks are necessary for understanding the global structure of metabolic networks, the interpretation and integration of large amounts of biologic data (sequences, various -omics) and ultimately for rational design of therapies for disease processes. Metabolic networks are highly organised to execute their function efficiently while tolerating wide variation in their environment. These networks are constrained by physical requirements (e.g. conservation of energy, redox and small moieties) but are also remarkably robust and evolvable. The authors use well-known features of the stoichiometry of bacterial metabolic networks to demonstrate how network architecture facilitates such capabilities, and to develop a minimal abstract metabolism which incorporates the known features of the stoichiometry and respects the constraints on enzymes and reactions. This model shows that the essential functionality and constraints drive the tradeoffs between robustness and fragility, as well as the large-scale structure and organisation of the whole network, particularly high variability. The authors emphasise how domainspecific constraints and tradeoffs imposed by the environment are important factors in shaping stoichiometry. Importantly, the consequence of these highly organised tradeoffs and tolerances is an architecture that has a highly structured modularity that is self-dissimilar and scale-rich. IntroductionMetabolic networks, which have been extensively studied for decades, are emblematic of how evolution has sculpted biologic systems for optimal function. In addition to unambiguous functional descriptions of core metabolism, this conserved network has been recently described in detail in terms of its stoichiometry (mass and energy balance). A higher level, mathematically defined description of the global organisation of complex metabolic networks is critical for a deep understanding of metabolism, from the interpretation of huge amounts of biologic data (sequences, various -omics) to design of therapies for disease processes. The stakes are high for obtaining the big picture right: biologic data plugged into a distorted model or interpreted in the context of a flawed universal law propagates misinterpretations. In flux analyses [1], stoichiometry is considered as a constraint, and fluxes are optimised to satisfy a global objective, typically growth. Previous studies, however, have not directly addressed whether the stoichiometry itself is highly optimal or organised in any sense and contributes to the origins and purpose of complexity in biological networks. Yet biochemistry textbooks describe metabolism as having evolved to be 'highly integrated' with the appearance of a 'coherent design' [2]. Here we explore both important 'design' (with no implication of a 'designer') features of metabolism and the sense in which stoichiometry itself has highly organised and optimised tolerances and tradeoffs (HOT) [3] for fun...

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Section: Basic Features Of Metabolic Networkmentioning

confidence: 99%

Highly optimised global organisation of metabolic networks

Tanaka¹,

Csete

Doyle

2005

IEE Proc. Syst. Biol.

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“…Although some centrality measures have been analysed for biological networks [27,28] a systematic study of the relationships between centralities and protein indispensability in PINs has not been reported. It has been argued that "the biological consequences of these topological properties are not clear-in fact, there might not be any, as both small-world and scale-free behavior can be explained by well-known evolutionary events" [5].…”

Section: Introductionmentioning

confidence: 99%

Virtual identification of essential proteins within the protein interaction network of yeast

2006

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Topological analysis of large scale protein-protein interaction networks (PINs) is important for understanding the organisational and functional principles of individual proteins. The number of interactions that a protein has in a PIN has been observed to be correlated with its indispensability. Essential proteins generally have more interactions than the non-essential ones. We show here that the lethality associated with removal of a protein from the yeast proteome correlates with different centrality measures of the nodes in the PIN, such as the closeness of a protein to many other proteins, or the number of pairs of proteins which need a specific protein as an intermediary in their communications, or the participation of a protein in different protein clusters in the PIN. These measures are significantly better than random selection in identifying essential proteins in a PIN. Centrality measures based on graph spectral properties of the network, in particular the subgraph centrality, show the best performance in identifying essential proteins in the yeast PIN. Subgraph centrality gives important structural information about the role of individual proteins and permits the selection of possible targets for rational drug discovery through the identification of essential proteins in the PIN.

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“…By contrast, the currently available maps of transcriptional networks do not have significant strongly connected components, suggesting a unidirectional regulation mode with relatively little transcriptional crosstalk (Balá zsi et al, 2005). The currently available metabolic and signal transduction networks are more connected, with 50 to 60% of the nodes forming the largest strongly connected component (Ma and Zeng, 2003;Ma'ayan et al, 2005). This intriguing range of interconnectivity from relatively unidirectional transcriptional regulatory maps to strongly connected protein interaction maps is affected by several factors.…”

Section: Network Analysismentioning

confidence: 92%

“…The currently available metabolic and signal transduction networks are more connected, with 50 to 60% of the nodes forming the largest strongly connected component (Ma and Zeng, 2003;Ma'ayan et al, 2005). This intriguing range of interconnectivity from relatively unidirectional transcriptional regulatory maps to strongly connected protein interaction maps is affected by several factors.…”

Section: Current Perspective Essaymentioning

confidence: 99%

Network Inference, Analysis, and Modeling in Systems Biology

2007

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Cells use signaling and regulatory pathways connecting numerous constituents, such as DNA, RNA, proteins, and small molecules, to coordinate multiple functions, allowing them to adapt to changing environments. High-throughput experimental methods enable the measurement of expression levels for thousands of genes and the determination of thousands of protein-protein or protein-DNA interactions. It is increasingly recognized that theoretical methods, such as statistical inference, graph analysis, and dynamic modeling, are needed to make sense of this abundance of information. This perspective argues that theoretical methods and models are most useful if they lead to novel biological predictions and reviews biological predictions arising from three systems biology topics: graph inference (i.e., reconstructing the network of interactions among a set of biological entities), graph analysis (i.e., mining the information content of the network), and dynamic network modeling (i.e., connecting the interaction network to the dynamic behavior of the system). The methods and principles discussed in this perspective are generally applicable, and the examples were selected from plant biology wherever possible. INTRODUCTIONTo understand the function of a cell or of higher units of biological organization, often it is beneficial to conceptualize them as systems of interacting elements. For such a systems-level description (which represents the main goal of systems biology), one needs to know (1) the identity of the components that constitute the biological system; (2) the dynamic behavior of these components (i.e., how their abundance or activity changes over time in various conditions); and (3) the interactions among these components (Kitano, 2002). Ultimately, this information can be combined into a model that is not only consistent with current knowledge but provides new insights and predictions, such as the behavior of the system in conditions that were previously unexplored.The origins of systems biology can be traced back to systems theory, a line of inquiry based on the assumptions that all phenomena can be viewed as a web of relationships among elements, and all systems can be handled by a common set of methods (von Bertalanffy, 1968;Weinberg, 1975;Bogdanov, 1980;Heinrich and Schuster, 1996;Francois, 1999;Voit, 2000). Early attempts at systems-level understanding of biology suffered from inadequate data on which to base the theories and models; however, the recent advent of high-throughput technologies brought an abundance of data on system elements and interactions, leading to a revival of systems biology.In some cases, the organization of the network of interactions underlying a biological system is straightforward (e.g., a linear chain of interactions), while in other cases a more formal representation, offered by mathematical graph theory (Bollobá s, 1979), is required. The simplest possible graph representation reduces the system's elements to graph nodes (also called vertices) and reduces their pairwise relationsh...

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The connectivity structure, giant strong component and centrality of metabolic networks

Abstract: http://genome.gbf.de/bioinformatics/

Cited by 344 publications

References 29 publications

Highly optimised global organisation of metabolic networks

Highly optimised global organisation of metabolic networks

Virtual identification of essential proteins within the protein interaction network of yeast

Network Inference, Analysis, and Modeling in Systems Biology

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