ArticlePLoS computational biology2015
Cell fate reprogramming by control of intracellular network dynamics.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 92 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
92 citing papers in PubMed.
- Control-guided refinement of partially specified Boolean networks: applications to RTK signaling.Bioinformatics (Oxford, England) · 2026Article
- Succession-diagram-based Markov chains reveal the attractor landscape of asynchronous Boolean networks.NPJ systems biology and applications · 2026Article
- Controllability of the gene regulatory network in zebrafish embryogenesis.Scientific reports · 2025Article
- Node and edge control strategy identification via trap spaces in Boolean networks.BMC bioinformatics · 2025Article
- Cellular patterns in Arabidopsis root epidermis emerge from gene regulatory network and diffusion dynamical feedback.NPJ systems biology and applications · 2025Article
- Reverse control of biological networks to restore phenotype landscapes.Science advances · 2025Article
- Mapping the attractor landscape of Boolean networks with biobalm.Bioinformatics (Oxford, England) · 2025Article
- AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks.eLife · 2025Article
- Control of Cellular Differentiation Trajectories for Cancer Reversion.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- General relationship of local topologies, global dynamics, and bifurcation in cellular networks.NPJ systems biology and applications · 2024Article
- Generating synthetic signaling networks for in silico modeling studies.Journal of theoretical biology · 2024Article
- Irreversibility in bacterial regulatory networks.Science advances · 2024Article
- Canalizing kernel for cell fate determination.Briefings in bioinformatics · 2024Article
- Cancer mutationscape: revealing the link between modular restructuring and intervention efficacy among mutations.NPJ systems biology and applications · 2024Article
- Pancreatic cancer mutationscape: revealing the link between modular restructuring and intervention efficacy amidst common mutations.bioRxiv : the preprint server for biology · 2024Article
- Decoding the principle of cell-fate determination for its reverse control.NPJ systems biology and applications · 2024Article
- A network-based modeling framework reveals the core signal transduction network underlying high carbon dioxide-induced stomatal closure in guard cells.PLoS biology · 2024Article
- Article
- Cell reprogramming design by transfer learning of functional transcriptional networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
32 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell's fate, such as disease therapeutics and stem cell reprogramming. Here we develop a novel network control framework that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our approach drives any initial state to the target state with 100% effectiveness and needs to be applied only transiently for the network to reach and stay in the desired state. We illustrate our method's potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of helper T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments.
Indexed as
Identifiers
What OpenQuestion holds
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.