ArticleScientific reports2013
APG: an Active Protein-Gene network model to quantify regulatory signals in complex biological systems.
Article in Scientific reports, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 22 citations in OpenAlex.
- Mechanism-Centric Approaches for Biomarker Detection and Precision Therapeutics in Cancer.Frontiers in genetics · 2021Review
- An integrative method to decode regulatory logics in gene transcription.Nature communications · 2017Article
- BGRMI: A method for inferring gene regulatory networks from time-course gene expression data and its application in breast cancer research.Scientific reports · 2016Article
- Unravelling personalized dysfunctional gene network of complex diseases based on differential network model.Journal of translational medicine · 2015Article
- PTHGRN: unraveling post-translational hierarchical gene regulatory networks using PPI, ChIP-seq and gene expression data.Nucleic acids research · 2014Article
- Quantitative iTRAQ LC-MS/MS Proteomics Reveals Transcription Factor Crosstalk and Regulatory Networks in Hypopharyngeal Squamous Cell Carcinoma.Journal of Cancer · 2014Article
- Drug repositioning framework by incorporating functional information.IET systems biology · 2013Article
- Detecting early-warning signals of type 1 diabetes and its leading biomolecular networks by dynamical network biomarkers.BMC medical genomics · 2013Article
- Investigation of anti-cancer mechanisms by comparative analysis of naked mole rat and rat.BMC systems biology · 2013Article
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synergistic interactions among transcription factors (TFs) and their cofactors collectively determine gene expression in complex biological systems. In this work, we develop a novel graphical model, called Active Protein-Gene (APG) network model, to quantify regulatory signals of transcription in complex biomolecular networks through integrating both TF upstream-regulation and downstream-regulation high-throughput data. Firstly, we theoretically and computationally demonstrate the effectiveness of APG by comparing with the traditional strategy based only on TF downstream-regulation information. We then apply this model to study spontaneous type 2 diabetic Goto-Kakizaki (GK) and Wistar control rats. Our biological experiments validate the theoretical results. In particular, SP1 is found to be a hidden TF with changed regulatory activity, and the loss of SP1 activity contributes to the increased glucose production during diabetes development. APG model provides theoretical basis to quantitatively elucidate transcriptional regulation by modelling TF combinatorial interactions and exploiting multilevel high-throughput information.
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