ArticleiScience2020
Diverse Cell Stimulation Kinetics Identify Predictive Signal Transduction Models.
Article in iScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
8 citing papers in PubMed, 9 citations in OpenAlex.
- Article
- Sequential design of single-cell experiments to identify discrete stochastic models for gene expression.Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control · 2024Article
- AI-powered simulation-based inference of a genuinely spatial-stochastic gene regulation model of early mouse embryogenesis.PLoS computational biology · 2024Article
- Modelling patient drug exposure profiles in vitro to narrow the valley of death.Nature reviews bioengineering · 2024Article
- Phenotypic consequences of logarithmic signaling in MAPK stress response.bioRxiv : the preprint server for biology · 2023Article
- Processing stimulus dynamics by the NF-κB network in single cells.Experimental & molecular medicine · 2023Review
- Rate thresholds in cell signaling have functional and phenotypic consequences in non-linear time-dependent environments.Frontiers in cell and developmental biology · 2023Review
- Building predictive signaling models by perturbing yeast cells with time-varying stimulations resulting in distinct signaling responses.STAR protocols · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Computationally understanding the molecular mechanisms that give rise to cell signaling responses upon different environmental, chemical, and genetic perturbations is a long-standing challenge that requires models that fit and predict quantitative responses for new biological conditions. Overcoming this challenge depends not only on good models and detailed experimental data but also on the rigorous integration of both. We propose a quantitative framework to perturb and model generic signaling networks using multiple and diverse changing environments (hereafter "kinetic stimulations") resulting in distinct pathway activation dynamics. We demonstrate that utilizing multiple diverse kinetic stimulations better constrains model parameters and enables predictions of signaling dynamics that would be impossible using traditional dose-response or individual kinetic stimulations. To demonstrate our approach, we use experimentally identified models to predict signaling dynamics in normal, mutated, and drug-treated conditions upon multitudes of kinetic stimulations and quantify which proteins and reaction rates are most sensitive to which extracellular stimulations.
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.