Phylogenetic analysis of modularity in protein interaction networks

Erten, Sinan; Li, Xin; Bebek, Gürkan; Li, Jing; Koyutürk, Mehmet

doi:10.1186/1471-2105-10-333

Cited by 33 publications

(17 citation statements)

References 34 publications

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“…Living systems are extraordinarily robust across multiple temporal and spatial scales—from molecules (Barabási and Oltvai ; Yook et al ; Hintze and Adami 2010) to communities and ecosystems (Pimm ). Such systems, when represented as biological networks, are frequently modular (Han et al ; Erten et al ; Arabidopsis Interactome Mapping Consortium ), and they have small‐world architectures (Watts and Strogatz ; Zhang and Zhang ) with recurrent dynamical network motifs (Shen‐Orr et al ; Wuchty et al ). These features are implicated in robustness and evolvability of living systems (Jeong et al ).…”

Section: Network Concepts Metrics References Interpretation/definimentioning

confidence: 99%

Dissection of Evolutionary Networks to Assess Their Role in the Evolution of Robustness, Function, and Diversification

Hearn

2013

Evolution

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Biological systems are remarkably robust in the face of environmental, mutational, and developmental perturbations. Analyses of molecular networks reveal recurrent features, such as modularity, that have been implicated in robustness and evolvability. Multiple theoretical models account for these features, yet few empirical tests of these models exist. Here I develop a set of broadly applicable methodologies to enable expanded empirical evaluation of model predictions. The methodologies focus on the inference and analysis of networks that depict evolutionary correlations among characters. I apply these methodologies to analyze an evolutionary network at a larger scale of organization among 42 stem anatomical and morphological characters of 52 species in the genus Adenia (Passifloraceae). I evaluate a model predicting that modular evolutionary networks will evolve in response to environmental change. The evolutionary network of Adenia is modular and "small-world," and the three diagnosed modules correspond roughly to functions of transport, storage, and mechanical support. The phylogenetically informed analyses suggest that the storage module is more impacted by environmental change than expected by chance. These results corroborate the hypothesis that modularity reduces the impact of environmental change, but this result requires further empirical evaluation that can be aided by the proposed methods in additional study systems.

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Section: Network Concepts Metrics References Interpretation/definimentioning

confidence: 99%

Dissection of Evolutionary Networks to Assess Their Role in the Evolution of Robustness, Function, and Diversification

Hearn

2013

Evolution

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“…In previous works on phylogenetic analysis of protein networks and complexes evolutionary information was usually used as a mean for evaluating the preservation of orthology information in functional modules [ 2 , 5 - 7 ]. Here, however, we incorporate evolutionary information beforehand and perform a comparative differential analysis for detecting evolutionary signal at complex, functional level.…”

Section: Introductionmentioning

confidence: 99%

A methodology for detecting the orthology signal in a PPI network at a functional complex level

et al. 2012

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BackgroundStable evolutionary signal has been observed in a yeast protein-protein interaction (PPI) network. These finding suggests more connected regions of a PPI network to be potential mediators of evolutionary information. Because more connected regions of PPI networks contain functional complexes, we are motivated to exploit the orthology relation for identifying complexes that can be clearly attributed to such evolutionary signal.ResultsWe proposed a computational methodology for detecting the orthology signal present in a PPI network at a functional complex level. Specifically, we examined highly functionally coherent putative protein complexes as detected by a clustering technique in the complete yeast PPI network, in the yeast sub-network which spans only ortholog proteins as determined by a given second organism, and in yeast sub-networks induced by a set of proteins randomly selected. We proposed a filtering technique for extracting orthology-driven clusters with unique functionalities, that is, neither enriched by clusters identified using the complete yeast PPI network nor identified using random sampling. Moreover, we extracted functional categories that can be clearly attributed to the presence of evolutionary signal as described by these clusters.ConclusionsApplication of the proposed methodology to the yeast PPI network indicated that evolutionary information at a functional complex level can be retrieved from the structure of the network. In particular, we detected protein complexes whose functionality could be uniquely attributed to the evolutionary signal. Moreover, we identified functions that are over-represented in these complexes due the evolutionary signal.

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“…Understanding the dynamic "evolution" of clinical trials from a systems perspective is similar to a phylogenetic analysis of ecosystems and may be useful in understanding the emergence, persistence, diversification and modularity of clinical research, particularly given the "noise" we have described in this type of data [28]. Many models have been proposed to describe dynamics in different types of network systems.…”

Section: Discussionmentioning

confidence: 99%

Dynamic, Multi-Level Network Models of Clinical Trials

Sorani¹,

Manley²,

Hemphill³

et al. 2010

Biocomputing 2011

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While networks models have often been applied to complex biological systems, they are increasingly being implemented to investigate clinical questions. Clinical trials have been studied extensively by traditional statistical methods but never, to our knowledge, using networks. We obtained data for 6,847 clinical trials from five "Nervous System Diseases" (NSD) and five "Behaviors and Mental Disorders" (BMD) from the clinicaltrials.gov registry. We constructed networks of diseases and interventions for visualization and analysis using Cytoscape software. To standardize nomenclature and enable multi-level annotation, we used MeSH and UMLS terms. We then constructed separate BMD and NSD networks to study dynamics over time. To assess how topology features related to clinical significance, we constructed a sub-network of Multiple Sclerosis and Alzheimer's trials and identified which trials had been published in high-profile medical journals. We found that the BMD network has evolved into a large, decentralized topology and does not distinctly reflect the five diseases by which it was defined, while the NSD network does, though other diseases and sub-phenotypes have emerged as areas of research. We also found that high-profile trials have distinctive network characteristics. Future work is needed to address mathematical questions such as scale-dependence of network features, clinical questions such as trial design optimization, and methodological questions such as data quality improvement.

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Phylogenetic analysis of modularity in protein interaction networks

Cited by 33 publications

References 34 publications

Dissection of Evolutionary Networks to Assess Their Role in the Evolution of Robustness, Function, and Diversification

Dissection of Evolutionary Networks to Assess Their Role in the Evolution of Robustness, Function, and Diversification

A methodology for detecting the orthology signal in a PPI network at a functional complex level

Dynamic, Multi-Level Network Models of Clinical Trials

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