ArticlePatterns (New York, N.Y.)2024
Density physics-informed neural networks reveal sources of cell heterogeneity in signal transduction.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Decoding cellular population dynamics through mechanistic modelling and statistical data analysis.NPJ systems biology and applications · 2026Review
- Physics-Informed Machine Learning in Biomedical Science and Engineering.Annual review of biomedical engineering · 2026Review
- Architecting Functional Polymers: Advances in Modular Synthesis, Responsive Design, and Multifaceted Applications.Polymers · 2026Review
- Article
- A history-dependent approach for accurate initial condition estimation in epidemic models.PLoS computational biology · 2025Article
- From PINNs to PIKANs: recent advances in physics-informed machine learning.Machine learning for computational science and engineering · 2025Article
- Estimating the distribution of parameters in differential equations with repeated cross-sectional data.PLoS computational biology · 2024Article
- Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics.Nature communications · 2024Article
- Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways.International journal of molecular sciences · 2024Review
- Advanced methods for gene network identification and noise decomposition from single-cell data.Nature communications · 2024Article
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Authors and funding
5 authors.
Funding
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Abstract
The transduction time between signal initiation and final response provides valuable information on the underlying signaling pathway, including its speed and precision. Furthermore, multi-modality in a transduction-time distribution indicates that the response is regulated by multiple pathways with different transduction speeds. Here, we developed a method called density physics-informed neural networks (Density-PINNs) to infer the transduction-time distribution from measurable final stress response time traces. We applied Density-PINNs to single-cell gene expression data from sixteen promoters regulated by unknown pathways in response to antibiotic stresses. We found that promoters with slower signaling initiation and transduction exhibit larger cell-to-cell heterogeneity in response intensity. However, this heterogeneity was greatly reduced when the response was regulated by slow and fast pathways together. This suggests a strategy for identifying effective signaling pathways for consistent cellular responses to disease treatments. Density-PINNs can also be applied to understand other time delay systems, including infectious diseases.
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Registered trials
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