Extracting the hierarchical organization of complex systems

Sales‐Pardo, Marta; Guimerà, Roger; Moreira, André A.; Amaral, Luı́s A. Nunes

doi:10.1073/pnas.0703740104

Cited by 486 publications

(371 citation statements)

References 51 publications

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“…To do this, we used a heuristic optimization method to identify clusters of species (or links) that appear in the same motif positions more often than one would expect by chance (Guimerà, Sales‐Pardo & Amaral 2007; Sales‐Pardo et al . 2007; Stouffer et al . 2012; Appendix S3, Supporting information).…”

Section: Methodsmentioning

confidence: 99%

Concomitant predation on parasites is highly variable but constrains the ways in which parasites contribute to food web structure

Cirtwill

Stouffer

2015

Journal of Animal Ecology

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Summary Previous analyses of empirical food webs (the networks of who eats whom in a community) have revealed that parasites exert a strong influence over observed food web structure and alter many network properties such as connectance and degree distributions. It remains unclear, however, whether these community‐level effects are fully explained by differences in the ways that parasites and free‐living species interact within a food web.To rigorously quantify the interrelationship between food web structure, the types of species in a web and the distinct types of feeding links between them, we introduce a shared methodology to quantify the structural roles of both species and feeding links. Roles are quantified based on the frequencies with which a species (or link) appears in different food web motifs – the building blocks of networks.We hypothesized that different types of species (e.g. top predators, basal resources, parasites) and different types of links between species (e.g. classic predation, parasitism, concomitant predation on parasites along with their hosts) will show characteristic differences in their food web roles.We found that parasites do indeed have unique structural roles in food webs. Moreover, we demonstrate that different types of feeding links (e.g. parasitism, predation or concomitant predation) are distributed differently in a food web context. More than any other interaction type, concomitant predation appears to constrain the roles of parasites. In contrast, concomitant predation links themselves have more variable roles than any other type of interaction.Together, our results provide a novel perspective on how both species and feeding link composition shape the structure of an ecological community and vice versa.

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Section: Methodsmentioning

confidence: 99%

Concomitant predation on parasites is highly variable but constrains the ways in which parasites contribute to food web structure

Cirtwill

Stouffer

2015

Journal of Animal Ecology

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“…Applications of the E. coli GEM range from pragmatic to theoretical studies, and can be classified into five general categories ( Fig. 3): 1) metabolic engineering [20][21][22][23][24][25][26][27][28][29][30] ; 2) biological discovery [31][32][33][34][35][36][37] ; 3) assessment of phenotypic behavior 19, ; 4) biological network analysis [64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79] ; and 5) studies of bacterial evolution [80][81][82] . The in silico methods used to probe the E. coli GEM in each study are summarized in Fig.…”

Section: Ask Not What You Can Do For a Reconstruction But What A Recmentioning

confidence: 99%

“…coli is generally viewed as having the most complete characterization of any model organism 98,99 . Due to the incorporation of thousands of metabolic interactions with relatively high reliability (e.g., 92% of the genes included in the latest reconstruction of E. coli 19 have experimentally determined annotated functions 99 , Table 1), validated genomescale reconstructions of E. coli have become popular resources for the analysis of various network properties [64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79] . The methods designed to analyze the underlying network structure of E. coli metabolism, some characterizing its interplay with regulation, have been developed to determine a number of physiological features.…”

Section: Systems Biology: Analysis Of Network Propertiesmentioning

confidence: 99%

“…The methods designed to analyze the underlying network structure of E. coli metabolism, some characterizing its interplay with regulation, have been developed to determine a number of physiological features. These features include the most probable active pathways and utilized metabolites under all possible growth conditions 67,69,73,75 , the existence of alternate optimal solutions and their physiological significance 65 , conserved intracellular pools of metabolites 68 , coupled reaction activities 66 and their relationship to gene co-expression 77 , metabolite coupling 71 , metabolite utilization 72 , the organization of metabolic networks 64,76 , strategies for E. coli to incorporate metabolic redundancy 78 , and the dominant functional states of the network across various environments 70,74,79 . These findings are both driven by biased approaches utilizing FBA and biomass objective function optimization and by unbiased approaches such as graph-based analyses (see Fig.…”

Section: Systems Biology: Analysis Of Network Propertiesmentioning

confidence: 99%

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The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

2008

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The number and scope of methods developed to interrogate and use metabolic network reconstructions has significantly expanded since the first review of the use of constraint-based analysis in Nature Biotechnology some 14 years ago. In particular, the Escherichia coli metabolic network reconstruction has reached the genome-scale and has been broadly adapted. Specifically, it has been used to address a broad spectrum of basic and practical applications, falling into five main categories: 1) metabolic engineering, 2) model-directed discovery, 3) interpretations of phenotypic screens, 4) analysis of network properties, and 5) studies of evolutionary processes. With these accomplishments in hand, the field is expected to move forward and seek to further, i) broaden the scope and content of network reconstructions, ii) develop new and novel in silico analysis tools, and iii) expand in adaptation to uses of proximal and distal causation in biology. Taken together, these efforts will solidify a mechanistic genotype-phenotype relationship for microbial metabolism.The availability of reconstructed metabolic networks for microorganisms has increased rapidly in recent years, and a growing number of research groups are reconstructing metabolic networks for organisms of interest 1 . A network reconstruction represents a highly curated set of primary biological information for a particular organism and thus can be considered a biochemically, genetically and genomically structured (BiGG) data base 1, 2 . A curated BiGG data base (de facto a knowledge base) can be converted into a mathematical format (i.e., an in silico model), and used to computationally assess phenotypic properties using a variety of computational methods 2,3 . Genome-scale reconstructions are thus, a key step in quantifying the genotype-phenotype relationship and can be used to 'bring genomes to life' 4 . The purpose of this review is to summarize and classify applications utilizing the E. coli reconstruction to answer a broad spectrum of biological questions. These studies provide both an up to date review of the applications of constraint-based analysis and a guide to similar applications for the growing number of organisms for which genome-scale reconstructions are becoming available. The Key Steps in the Formulation of Genome-scale Metabolic Network ModelsThe four key steps in the formulation and use of genome-scale models are illustrated in Fig. 1. Foundational to the process is the generation of global, or genome-scale, omics data. Omics data, along with legacy information (i.e., the 'bibliome') and small-scale detailed experiments, can be used to define the interactions amongst the biological components that are used to reconstruct organism-specific networks 1 . Network reconstruction is also an iterative, on-going process that continually integrates data in a formal fashion as it becomes available 5 . As a result, a current and well curated genome-scale network reconstruction is a common denominator for those studying systems biology of an or...

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“…The present availability of extensive data is allowing the construction of genome scale metabolic networks for an increasing number of species, generally through a careful human driven curation process (Feist et al, 2007;Heinemann et al, 2005;Herrgård et al, 2008;Ma et al, 2007). The topological properties of metabolic networks have been investigated in great details, revealing scale free, modular and hierarchical properties (Jeong et al, 2000;Ravasz et al, 2002;Sales Pardo et al, 2007).…”

Section: Introductionmentioning

confidence: 99%