ReviewBMC systems biology2013
Topology of molecular interaction networks.
Review in BMC systems biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
51 citing papers in PubMed.
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- Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics identify cancer-specific mechanisms of stress adaptation.Nature communications · 2023Article
- Reviewing Atrial Fibrillation Pathophysiology from a Network Medicine Perspective: The Relevance of Structural Remodeling, Inflammation, and the Immune System.Life (Basel, Switzerland) · 2023Review
- Protein-protein interaction (PPI) network analysis reveals important hub proteins and sub-network modules for root development in rice (Oryza sativa).Journal, genetic engineering & biotechnology · 2023Article
- A novel approach to topological network analysis for the identification of metrics and signatures in non-small cell lung cancer.Scientific reports · 2023Article
- Quantitative systems pharmacology of the eye: Tools and data for ocular QSP.CPT: pharmacometrics & systems pharmacology · 2023Review
- A Data Science Approach for the Identification of Molecular Signatures of Aggressive Cancers.Cancers · 2022Article
- A Comprehensive Non-targeted Analysis Study of the Prenatal Exposome.Environmental science & technology · 2021Article
- Testing biological network motif significance with exponential random graph models.Applied network science · 2021Article
- Identifying miRNA-mRNA Networks Associated With COPD Phenotypes.Frontiers in genetics · 2021Article
- The omnistat: A flexible continuous-culture system for prolonged experimental evolution.Methods in ecology and evolution · 2020Article
- Identifying Protein-metabolite Networks Associated with COPD Phenotypes.Metabolites · 2020Article
- Modeling Basins of Attraction for Breast Cancer Using Hopfield Networks.Frontiers in genetics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks.Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs.Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes.Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.