ReviewPhysical biology2022
Reconstructing data-driven governing equations for cell phenotypic transitions: integration of data science and systems biology.
Review in Physical biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Cell Population Dynamics Informed by Cell-Cycle Regulation: A Deterministic Modeling Toolkit.Computational and structural biotechnology journal · 2026Review
- Deciphering cell-fate trajectories using spatiotemporal single-cell transcriptomic data.NPJ systems biology and applications · 2025Review
- Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-Seq Data Analysis.Entropy (Basel, Switzerland) · 2025Review
- Transiently increased coordination in gene regulation during cell phenotypic transitions.PRX life · 2024Article
- Tipping points in epithelial-mesenchymal lineages from single-cell transcriptomics data.Biophysical journal · 2024Article
- Recovering biomolecular network dynamics from single-cell omics data requires three time points.NPJ systems biology and applications · 2024Article
- Biologically informed NeuralODEs for genome-wide regulatory dynamics.Genome biology · 2024Article
- Data- and theory-driven approaches for understanding paths of epithelial-mesenchymal transition.Genesis (New York, N.Y. : 2000) · 2024Review
- Reconstructing growth and dynamic trajectories from single-cell transcriptomics data.Nature machine intelligence · 2024Article
- scTOP: physics-inspired order parameters for cellular identification and visualization.Development (Cambridge, England) · 2023Article
- Biologically informed NeuralODEs for genome-wide regulatory dynamics.Research square · 2023Article
- scTOP: physics-inspired order parameters for cellular identification and visualization.bioRxiv : the preprint server for biology · 2023Article
- spliceJAC: transition genes and state-specific gene regulation from single-cell transcriptome data.Molecular systems biology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Cells with the same genome can exist in different phenotypes and can change between distinct phenotypes when subject to specific stimuli and microenvironments. Some examples include cell differentiation during development, reprogramming for induced pluripotent stem cells and transdifferentiation, cancer metastasis and fibrosis progression. The regulation and dynamics of cell phenotypic conversion is a fundamental problem in biology, and has a long history of being studied within the formalism of dynamical systems. A main challenge for mechanism-driven modeling studies is acquiring sufficient amount of quantitative information for constraining model parameters. Advances in quantitative experimental approaches, especially high throughput single-cell techniques, have accelerated the emergence of a new direction for reconstructing the governing dynamical equations of a cellular system from quantitative single-cell data, beyond the dominant statistical approaches. Here I review a selected number of recent studies using live- and fixed-cell data and provide my perspective on future development.
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.