ArticleScientific reports2018
Topological estimation of signal flow in complex signaling networks.
Article in Scientific reports, 2018. 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.
- Identifying omic biomarkers for chronic inflammatory diseases associated with periodontitis using percolation on multi-disease gene co-expression networks.Communications medicine · 2026Article
- Control of Cellular Differentiation Trajectories for Cancer Reversion.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations.Cell reports methods · 2024Article
- Article
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
- Cell-Type-Specific Gene Regulatory Networks of Pro-Inflammatory and Pro-Resolving Lipid Mediator Biosynthesis in the Immune System.International journal of molecular sciences · 2023Article
- NETISCE: a network-based tool for cell fate reprogramming.NPJ systems biology and applications · 2022Article
- Identifying molecular targets for reverse aging using integrated network analysis of transcriptomic and epigenomic changes during aging.Scientific reports · 2021Article
- Signal flow control of complex signaling networks.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In a cell, any information about extra- or intra-cellular changes is transferred and processed through a signaling network and dysregulation of signal flow often leads to disease such as cancer. So, understanding of signal flow in the signaling network is critical to identify drug targets. Owing to the development of high-throughput measurement technologies, the structure of a signaling network is becoming more available, but detailed kinetic parameter information about molecular interactions is still very limited. A question then arises as to whether we can estimate the signal flow based only on the structure information of a signaling network. To answer this question, we develop a novel algorithm that can estimate the signal flow using only the topological information and apply it to predict the direction of activity change in various signaling networks. Interestingly, we find that the average accuracy of the estimation algorithm is about 60-80% even though we only use the topological information. We also find that this predictive power gets collapsed if we randomly alter the network topology, showing the importance of network topology. Our study provides a basis for utilizing the topological information of signaling networks in precision medicine or drug target discovery.
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Registered trials
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