ArticleBMC systems biology2017
Quantitative evaluation and reversion analysis of the attractor landscapes of an intracellular regulatory network for colorectal cancer.
Article in BMC systems biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Harnessing the analog computing power of regulatory networks with the Regulatory Network Machine.iScience · 2025Article
- CanSeer: a translational methodology for developing personalized cancer models and therapeutics.Scientific reports · 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
- Critical transition and reversion of tumorigenesis.Experimental & molecular medicine · 2023Review
- NETISCE: a network-based tool for cell fate reprogramming.NPJ systems biology and applications · 2022Article
- Modeling signaling pathways in biology with MaBoSS: From one single cell to a dynamic population of heterogeneous interacting cells.Computational and structural biotechnology journal · 2022Review
- A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks.Patterns (New York, N.Y.) · 2021Review
- Signal flow control of complex signaling networks.Scientific reports · 2019Article
- Attractor - a new turning point in drug discovery.Drug design, development and therapy · 2019Review
- Discrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer.Current opinion in systems biology · 2018Review
- ATLANTIS - Attractor Landscape Analysis Toolbox for Cell Fate Discovery and Reprogramming.Scientific reports · 2018Article
- Estimating Attractor Reachability in Asynchronous Logical Models.Frontiers in physiology · 2018Article
- Target Control in Logical Models Using the Domain of Influence of Nodes.Frontiers in physiology · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCancer reversion, converting the phenotypes of a cancer cell into those of a normal cell, has been sporadically observed throughout history. However, no systematic analysis has been attempted so far.
resultsTo investigate this from a systems biological perspective, we have constructed a logical network model of colorectal tumorigenesis by integrating key regulatory molecules and their interactions from previous experimental data. We identified molecular targets that can reverse cancerous cellular states to a normal state by systematically perturbing each molecular activity in the network and evaluating the resulting changes of the attractor landscape with respect to uncontrolled proliferation, EMT, and stemness. Intriguingly, many of the identified targets were well in accord with previous studies. We further revealed that the identified targets constitute stable network motifs that contribute to enhancing the robustness of attractors in cancerous cellular states against diverse regulatory signals.
conclusionsThe proposed framework for systems analysis is applicable to the study of tumorigenesis and reversion of other types of cancer.
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