ReviewThe Journal of biological chemistry2009
Insights into the organization of biochemical regulatory networks using graph theory analyses.
Review in The Journal of biological chemistry, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
What it found
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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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
42 citing papers in PubMed.
- Creating an Interactive Web Interface for Networks Stored in Knowledge Graph Databases.Current protocols · 2025Article
- The Evolution of Systems Biology and Systems Medicine: From Mechanistic Models to Uncertainty Quantification.Annual review of biomedical engineering · 2025Review
- Disrupted topological organization of the default mode network in mild cognitive impairment with subsyndromal depression: A graph theoretical analysis.CNS neuroscience & therapeutics · 2024Article
- Systems biology approaches to identify driver genes and drug combinations for treating COVID-19.Scientific reports · 2024Article
- Enrichr-KG: bridging enrichment analysis across multiple libraries.Nucleic acids research · 2023Article
- Phase separation in innate immune response and inflammation-related diseases.Frontiers in immunology · 2023Review
- Gene Regulatory Identification Based on the Novel Hybrid Time-Delayed Method.Frontiers in genetics · 2022Article
- Article
- Article
- Age- and speed-dependent modulation of gaits in DSCAMJournal of neurophysiology · 2018Article
- Persistent activation of interlinked type 2 airway epithelial gene networks in sputum-derived cells from aeroallergen-sensitized symptomatic asthmatics.Scientific reports · 2018Article
- Reconstruction and Application of Protein-Protein Interaction Network.International journal of molecular sciences · 2016Review
- Speed-Dependent Modulation of the Locomotor Behavior in Adult Mice Reveals Attractor and Transitional Gaits.Frontiers in neuroscience · 2016Article
- Network reconstruction based on proteomic data and prior knowledge of protein connectivity using graph theory.PloS one · 2015Article
- Transcription factor and microRNA-regulated network motifs for cancer and signal transduction networks.BMC systems biology · 2015Article
- Weighting schemes in metabolic graphs for identifying biochemical routes.Systems and synthetic biology · 2014Article
- An approach to investigate intracellular protein network responses.Chemical research in toxicology · 2014Article
- Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders.Genes, brain, and behavior · 2014Review
- Proteomic analysis of human plasma in chronic rheumatic mitral stenosis reveals proteins involved in the complement and coagulation cascade.Clinical proteomics · 2014Article
- Molecular architecture of the chick vestibular hair bundle.Nature neuroscience · 2013Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Graph theory has been a valuable mathematical modeling tool to gain insights into the topological organization of biochemical networks. There are two types of insights that may be obtained by graph theory analyses. The first provides an overview of the global organization of biochemical networks; the second uses prior knowledge to place results from multivariate experiments, such as microarray data sets, in the context of known pathways and networks to infer regulation. Using graph analyses, biochemical networks are found to be scale-free and small-world, indicating that these networks contain hubs, which are proteins that interact with many other molecules. These hubs may interact with many different types of proteins at the same time and location or at different times and locations, resulting in diverse biological responses. Groups of components in networks are organized in recurring patterns termed network motifs such as feedback and feed-forward loops. Graph analysis revealed that negative feedback loops are less common and are present mostly in proximity to the membrane, whereas positive feedback loops are highly nested in an architecture that promotes dynamical stability. Cell signaling networks have multiple pathways from some input receptors and few from others. Such topology is reminiscent of a classification system. Signaling networks display a bow-tie structure indicative of funneling information from extracellular signals and then dispatching information from a few specific central intracellular signaling nexuses. These insights show that graph theory is a valuable tool for gaining an understanding of global regulatory features of biochemical networks.
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Registered trials
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.