ArticleJournal of experimental & clinical cancer research : CR2022
The world's first digital cell twin in cancer electrophysiology: a digital revolution in cancer research?
Article in Journal of experimental & clinical cancer research : CR, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The trial behind it
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Who cites it
12 citing papers in PubMed, 21 citations in OpenAlex.
- Exploring the scope and applications of digital twin technologies in dentistry: a scoping review.Evidence-based dentistry · 2026Article
- Cancer: A bioelectric disease?Clinical and translational medicine · 2026Review
- Targeting ion channel dysregulation in tumors: emerging therapeutic opportunities.Trends in pharmacological sciences · 2026Review
- A Machine Learning-Driven Electrophysiological Platform for Real-Time Tumor-Neural Interaction Analysis and Modulation.Nature communications · 2026Article
- From images to physics-based computational models to digital twins: a framework for personalized cancer therapies.Frontiers in radiology · 2026Article
- Computational modeling and simulation in oncology.Clinical and translational medicine · 2025Article
- Advances and critical aspects in cancer treatment development using digital twins.Briefings in bioinformatics · 2025Review
- From virtual to reality: innovative practices of digital twins in tumor therapy.Journal of translational medicine · 2025Review
- G protein-coupled receptor digital twins for precision and personalized medicine.Computational and structural biotechnology journal · 2025Review
- Transforming precision medicine: The potential of the clinical artificial intelligent single-cell framework.Clinical and translational medicine · 2025Article
- The Functional Interaction of KATP and BK Channels with Aquaporin-4 in the U87 Glioblastoma Cell.Biomedicines · 2024Article
- The bioelectric mechanisms of local calcium dynamics in cancer cell proliferation: an extension of the A549Frontiers in molecular biosciences · 2024Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe introduction of functional in-silico models, in addition to in-vivo tumor models, opens up new and unlimited possibilities in cancer research and drug development. The world's first digital twin of the A549 cell's electrophysiology in the human lung adenocarcinoma, unveiled in 2021, enables the investigation and evaluation of new research hypotheses about modulating the function of ion channels in the cell membrane, which are important for better understanding cancer development and progression, as well as for developing new drugs and predicting treatments. MAIN BODY: The developed A549 in-silico model allows virtual simulations of the cell's rhythmic oscillation of the membrane potential, which can trigger the transition between cell cycle phases. It is able to predict the promotion or interruption of cell cycle progression provoked by targeted activation and inactivation of ion channels, resulting in abnormal hyper- or depolarization of the membrane potential, a potential key signal for the known cancer hallmarks. For example, model simulations of blockade of transient receptor potential cation channels (TRPC6), which are highly expressed during S-G2/M transition, result in a strong hyperpolarization of the cell's membrane potential that can suppress or bypass the depolarization required for the S-G2/M transition, allowing for possible cell cycle arrest and inhibition of mitosis. All simulated research hypotheses could be verified by experimental studies. SHORT
conclusionFunctional, non-phenomenological digital twins, ranging from single cells to cell-cell interactions to 3D tissue models, open new avenues for modern cancer research through "dry lab" approaches that optimally complement established in-vivo and in-vitro methods.
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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.