Low degree metabolites explain essential reactions and enhance modularity in biological networks

Samal, Areejit; Singh, Shalini; Giri, Varun; Krishna, Sandeep; Raghuram, Nandula; Jain, Shaili

doi:10.1186/1471-2105-7-118

Cited by 59 publications

(52 citation statements)

References 42 publications

(62 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Weighted network analysis has been used in several studies in past for identification of influential nodes and quantification of their interactive strengths. Some of them include the following: examining the flux movement in transportation related network, for instance the air traffic networks and Internet networks [15, 16]; analysis of the rate of turnover of molecules through a metabolic pathway in cellular systems [17,18, 19, 20]; study of the statistical properties of trading activity network existent in stock exchanges in developing economies [21,22,23]; investigation into the degree distribution, node distribution and weight distribution of the world investment networks[24] and lately for analysis of the dynamics of stock market networks [25,26,27]. …”

Section: Introductionmentioning

confidence: 99%

Investigating the Influence Relationship Models for Stocks in Indian Equity Market: A Weighted Network Modelling Study

2016

View full text Add to dashboard Cite

The socio-economic systems today possess high levels of both interconnectedness and interdependencies, and such system-level relationships behave very dynamically. In such situations, it is all around perceived that influence is a perplexing power that has an overseeing part in affecting the dynamics and behaviours of involved ones. As a result of the force & direction of influence, the transformative change of one entity has a cogent aftereffect on the other entities in the system. The current study employs directed weighted networks for investigating the influential relationship patterns existent in a typical equity market as an outcome of inter-stock interactions happening at the market level, the sectorial level and the industrial level. The study dataset is derived from 335 constituent stocks of ‘Standard & Poor Bombay Stock Exchange 500 index’ and study period is 1st June 2005 to 30th June 2015. The study identifies the set of most dynamically influential stocks & their respective temporal pattern at three hierarchical levels: the complete equity market, different sectors, and constituting industry segments of those sectors. A detailed influence relationship analysis is performed for the sectorial level network of the construction sector, and it was found that stocks belonging to the cement industry possessed high influence within this sector. Also, the detailed network analysis of construction sector revealed that it follows scale-free characteristics and power law distribution. In the industry specific influence relationship analysis for cement industry, methods based on threshold filtering and minimum spanning tree were employed to derive a set of sub-graphs having temporally stable high-correlation structure over this ten years period.

show abstract

Section: Introductionmentioning

confidence: 99%

Investigating the Influence Relationship Models for Stocks in Indian Equity Market: A Weighted Network Modelling Study

2016

View full text Add to dashboard Cite

show abstract

“…UPUC metabolites have been introduced by Samal et al [19]. They were described as metabolites that are consumed and produced by only a single reaction and, thus, exhibit the lowest possible degree in a bipartite network representation of the metabolic system (metabolites and reactions are both represented as inter- but not intra-connected node sets).…”

Section: Introductionmentioning

confidence: 99%

“…A UPUC cluster may then be defined as a reaction subset that connects a set of UPUC metabolites. Besides the high essentiality of these UPUC reactions, which is one of the key issues in [19], they comprise also some other quite interesting features, e.g., proportionally fixed steady-state fluxes and significant correspondence with gene-regulatory modules [19]. We would like to point out that the UPUC category, as defined above, has not been used in the original study of Samal et al [19], but rather a set consisting of reactions that are either associated with UP or UC metabolites.…”

Section: Introductionmentioning

confidence: 99%

A Topological Characterization of Medium-Dependent Essential Metabolic Reactions

2012

View full text Add to dashboard Cite

Metabolism has frequently been analyzed from a network perspective. A major question is how network properties correlate with biological features like growth rates, flux patterns and enzyme essentiality. Using methods from graph theory as well as established topological categories of metabolic systems, we analyze the essentiality of metabolic reactions depending on the growth medium and identify the topological footprint of these reactions. We find that the typical topological context of a medium-dependent essential reaction is systematically different from that of a globally essential reaction. In particular, we observe systematic differences in the distribution of medium-dependent essential reactions across three-node subgraphs (the network motif signature of medium-dependent essential reactions) compared to globally essential or globally redundant reactions. In this way, we provide evidence that the analysis of metabolic systems on the few-node subgraph scale is meaningful for explaining dynamic patterns. This topological characterization of medium-dependent essentiality provides a better understanding of the interplay between reaction deletions and environmental conditions.

show abstract

“…It was shown [53][54][55] that metabolism is not an exception. We will now use the insights into modules and their effect on Z-scores described in the electronic supplementary material, §S1.1 to show and explain the effects of modules on the TSP of graphs in general.…”

Section: Modularitymentioning

confidence: 99%

Artefacts in statistical analyses of network motifs: general framework and application to metabolic networks

Beber

Fretter

Jain

et al. 2012

J. R. Soc. Interface.

View full text Add to dashboard Cite

Few-node subgraphs are the smallest collective units in a network that can be investigated. They are beyond the scale of individual nodes but more local than, for example, communities. When statistically over-or under-represented, they are called network motifs. Network motifs have been interpreted as building blocks that shape the dynamic behaviour of networks. It is this promise of potentially explaining emergent properties of complex systems with relatively simple structures that led to an interest in network motifs in an ever-growing number of studies and across disciplines. Here, we discuss artefacts in the analysis of network motifs arising from discrepancies between the network under investigation and the pool of random graphs serving as a null model. Our aim was to provide a clear and accessible catalogue of such incongruities and their effect on the motif signature. As a case study, we explore the metabolic network of Escherichia coli and show that only by excluding ever more artefacts from the motif signature a strong and plausible correlation with the essentiality profile of metabolic reactions emerges.

show abstract

Low degree metabolites explain essential reactions and enhance modularity in biological networks

Cited by 59 publications

References 42 publications

Investigating the Influence Relationship Models for Stocks in Indian Equity Market: A Weighted Network Modelling Study

Investigating the Influence Relationship Models for Stocks in Indian Equity Market: A Weighted Network Modelling Study

A Topological Characterization of Medium-Dependent Essential Metabolic Reactions

Artefacts in statistical analyses of network motifs: general framework and application to metabolic networks

Contact Info

Product

Resources

About