ArticleCell systems2020
Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks.
Article in Cell systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 36 citations in OpenAlex.
- Single-cell stimulus-response gene expression trajectories reveal the stimulus specificities of dynamic responses by single macrophages.Molecular cell · 2024Article
- Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways.International journal of molecular sciences · 2024Review
- The ability to sense the environment is heterogeneously distributed in cell populations.eLife · 2024Article
- Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data.PLoS computational biology · 2023Article
- Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway.Methods in molecular biology (Clifton, N.J.) · 2023Review
- Epidermal growth factor receptor cascade prioritizes the maximization of signal transduction.Scientific reports · 2022Article
- Analysis of cellular kinetic models suggest that physiologically based model parameters may be inherently, practically unidentifiable.Journal of pharmacokinetics and pharmacodynamics · 2022Article
- Quantifying information of intracellular signaling: progress with machine learning.Reports on progress in physics. Physical Society (Great Britain) · 2022Review
- Data-based stochastic modeling reveals sources of activity bursts in single-cell TGF-β signaling.PLoS computational biology · 2022Article
- Identifying cell-to-cell variability in internalization using flow cytometry.Journal of the Royal Society, Interface · 2022Article
- Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: The rate-limiting step number.Science advances · 2022Article
- Review of applications and challenges of quantitative systems pharmacology modeling and machine learning for heart failure.Journal of pharmacokinetics and pharmacodynamics · 2022Review
- SiGMoiD: A super-statistical generative model for binary data.PLoS computational biology · 2021Article
- Entropic Dynamics on Gibbs Statistical Manifolds.Entropy (Basel, Switzerland) · 2021Article
- Concepts and Applications of Information Theory to Immuno-Oncology.Trends in cancer · 2021Review
- Inferring a network from dynamical signals at its nodes.PLoS computational biology · 2020Article
- The Maximum Caliber Variational Principle for Nonequilibria.Annual review of physical chemistry · 2020Review
- Estimation methods for heterogeneous cell population models in systems biology.Journal of the Royal Society, Interface · 2018Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Predictive models of signaling networks are essential for understanding cell population heterogeneity and designing rational interventions in disease. However, using computational models to predict heterogeneity of signaling dynamics is often challenging because of the extensive variability of biochemical parameters across cell populations. Here, we describe a maximum entropy-based framework for inference of heterogeneity in dynamics of signaling networks (MERIDIAN). MERIDIAN estimates the joint probability distribution over signaling network parameters that is consistent with experimentally measured cell-to-cell variability of biochemical species. We apply the developed approach to investigate the response heterogeneity in the EGFR/Akt signaling network. Our analysis demonstrates that a significant fraction of cells exhibits high phosphorylated Akt (pAkt) levels hours after EGF stimulation. Our findings also suggest that cells with high EGFR levels predominantly contribute to the subpopulation of cells with high pAkt activity. We also discuss how MERIDIAN can be extended to accommodate various experimental measurements.
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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.