ArticleProceedings of the National Academy of Sciences of the United States of America2012
Predicting rates of cell state change caused by stochastic fluctuations using a data-driven landscape model.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed.
- Learning stochastic dynamics and cell-fate landscapes from single-cell snapshots via optimal transport.Science advances · 2026Article
- Learning stochastic processes with intrinsic noise from cross-sectional biological data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Colony context and size-dependent compensation mechanisms give rise to variations in nuclear growth trajectories.Cell systems · 2025Article
- Modeling relaxation experiments with a mechanistic model of gene expression.BMC bioinformatics · 2024Article
- Landscape quantifies the intermediate state and transition dynamics in ecological networks.PLoS computational biology · 2024Article
- Inferring structural and dynamical properties of gene networks from data with deep learning.NAR genomics and bioinformatics · 2022Article
- Optimal transport analysis reveals trajectories in steady-state systems.PLoS computational biology · 2021Article
- Memory shapes microbial populations.PLoS computational biology · 2021Article
- Information Thermodynamics and Reducibility of Large Gene Networks.Entropy (Basel, Switzerland) · 2021Article
- Partitioning stable and unstable expression level variation in cell populations: A theoretical framework and its application to the T cell receptor.PLoS computational biology · 2020Article
- Article
- Probing pluripotency gene regulatory networks with quantitative live cell imaging.Computational and structural biotechnology journal · 2020Review
- Modeling of Drug Diffusion Based on Concentration Profiles in Healthy and Damaged Human Skin.Biophysical journal · 2019Article
- Morphological State Transition Dynamics in EGF-Induced Epithelial to Mesenchymal Transition.Journal of clinical medicine · 2019Article
- Phenotypic heterogeneity and evolution of melanoma cells associated with targeted therapy resistance.PLoS computational biology · 2019Article
- Opportunities and Challenges in Implementation of Multiparameter Single Cell Analysis Platforms for Clinical Translation.Clinical and translational science · 2018Review
- Single-cell analysis resolves the cell state transition and signaling dynamics associated with melanoma drug-induced resistance.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Cellular automaton models for time-correlated random walks: derivation and analysis.Scientific reports · 2017Article
- Data-based modeling of drug penetration relates human skin barrier function to the interplay of diffusivity and free-energy profiles.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Microchip-based single-cell functional proteomics for biomedical applications.Lab on a chip · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We develop a potential landscape approach to quantitatively describe experimental data from a fibroblast cell line that exhibits a wide range of GFP expression levels under the control of the promoter for tenascin-C. Time-lapse live-cell microscopy provides data about short-term fluctuations in promoter activity, and flow cytometry measurements provide data about the long-term kinetics, because isolated subpopulations of cells relax from a relatively narrow distribution of GFP expression back to the original broad distribution of responses. The landscape is obtained from the steady state distribution of GFP expression and connected to a potential-like function using a stochastic differential equation description (Langevin/Fokker-Planck). The range of cell states is constrained by a force that is proportional to the gradient of the potential, and biochemical noise causes movement of cells within the landscape. Analyzing the mean square displacement of GFP intensity changes in live cells indicates that these fluctuations are described by a single diffusion constant in log GFP space. This finding allows application of the Kramers' model to calculate rates of switching between two attractor states and enables an accurate simulation of the dynamics of relaxation back to the steady state with no adjustable parameters. With this approach, it is possible to use the steady state distribution of phenotypes and a quantitative description of the short-term fluctuations in individual cells to accurately predict the rates at which different phenotypes will arise from an isolated subpopulation of cells.
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.