ArticleMolecular systems biology2016
Distinct cellular states determine calcium signaling response.
Article in Molecular systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- Dynamic heterogeneity in an E. coli stress response regulon mediates gene activation and antimicrobial peptide tolerance.Cell reports · 2026Article
- Rules of life at the interface of calcium signaling and mechanobiology.APL bioengineering · 2025Review
- Deciphering the history of ERK activity from fixed-cell immunofluorescence measurements.Nature communications · 2025Article
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- Inertial effect of cell state velocity on the quiescence-proliferation fate decision.NPJ systems biology and applications · 2024Article
- Principles of Computation by Competitive Protein Dimerization Networks.bioRxiv : the preprint server for biology · 2024Article
- Stimulus-response signaling dynamics characterize macrophage polarization states.Cell systems · 2024Article
- Reverse engineering morphogenesis through Bayesian optimization of physics-based models.NPJ systems biology and applications · 2024Article
- CHRFAM7A diversifies human immune adaption through CaEBioMedicine · 2024Article
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- Characterizing heterogeneous single-cell dose responses computationally and experimentally using threshold inhibition surfaces and dose-titration assays.NPJ systems biology and applications · 2024Article
- Characterization of two distinct immortalized endothelial cell lines, EA.hy926 and HMEC-1, for in vitro studies: exploring the impact of calcium electroporation, CaCell communication and signaling : CCS · 2024Article
- The ability to sense the environment is heterogeneously distributed in cell populations.eLife · 2024Article
- Integrating inverse reinforcement learning into data-driven mechanistic computational models: a novel paradigm to decode cancer cell heterogeneity.Frontiers in systems biology · 2024Article
- The SIRT3-ATAD3A axis regulates MAM dynamics and mitochondrial calcium homeostasis in cardiac hypertrophy.International journal of biological sciences · 2024Article
- The multiple activations in budding yeast S-phase checkpoint are Poisson processes.PNAS nexus · 2023Article
- Generative modeling of single-cell gene expression for dose-dependent chemical perturbations.Patterns (New York, N.Y.) · 2023Article
- Single-cell CaJournal of the Royal Society, Interface · 2023Article
- A mathematical model of calcium signals around laser-induced epithelial wounds.Molecular biology of the cell · 2023Article
- Oscillatory ERK Signaling and Morphology Determine Heterogeneity of Breast Cancer Cell Chemotaxis via MEK-ERK and p38-MAPK Signaling Pathways.Bioengineering (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
The heterogeneity in mammalian cells signaling response is largely a result of pre-existing cell-to-cell variability. It is unknown whether cell-to-cell variability rises from biochemical stochastic fluctuations or distinct cellular states. Here, we utilize calcium response to adenosine trisphosphate as a model for investigating the structure of heterogeneity within a population of cells and analyze whether distinct cellular response states coexist. We use a functional definition of cellular state that is based on a mechanistic dynamical systems model of calcium signaling. Using Bayesian parameter inference, we obtain high confidence parameter value distributions for several hundred cells, each fitted individually. Clustering the inferred parameter distributions revealed three major distinct cellular states within the population. The existence of distinct cellular states raises the possibility that the observed variability in response is a result of structured heterogeneity between cells. The inferred parameter distribution predicts, and experiments confirm that variability in IP3R response explains the majority of calcium heterogeneity. Our work shows how mechanistic models and single-cell parameter fitting can uncover hidden population structure and demonstrate the need for parameter inference at the single-cell level.
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