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Related Concept Videos

Feedback Regulation of Calcium Concentration01:27

Feedback Regulation of Calcium Concentration

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Calcium is an essential signaling molecule required for various cellular functions. Calcium pumps and ion channels on cell and organellar membranes, such as those on the endoplasmic reticulum (ER), regulate calcium concentrations inside the cell. They remain closed, keeping the cytosolic calcium levels low at a resting state.
Various transmembrane receptors, such as G protein-coupled receptors (GPCRs), elicit a response to extracellular signals by increasing cytosolic calcium. Activated GPCRs...
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Calmodulin (CaM) is a calcium-binding protein in eukaryotes that controls various calcium-regulated cellular processes. It has four calcium-binding sites that bind calcium to form the calcium-calmodulin ( Ca2+-CaM) complex. GPCR stimulation increases the calcium levels in the cells that bind to CaM and induces a conformational change.
The Ca2+-CaM complex does not have enzymatic activity by itself. Instead, the complex binds downstream target proteins, including membrane proteins or enzymes,...
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Related Experiment Video

Updated: Jan 5, 2026

Imaging Calcium Dynamics in Subpopulations of Mouse Pancreatic Islet Cells
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A Statistical View on Calcium Oscillations.

Jake Powell1, Martin Falcke2,3, Alexander Skupin4,5

  • 1Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK.

Advances in Experimental Medicine and Biology
|October 25, 2019
PubMed
Summary
This summary is machine-generated.

Statistical models analyze stochastic calcium oscillations, offering insights into cellular mechanisms. Bayesian methods infer parameters from single-cell spike data, revealing subcellular processes driving these dynamic events.

Keywords:
Bayesian inferenceCalcium spikesHeterogeneous cell populationsIntensity functions

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Area of Science:

  • Cellular Biology
  • Biophysics
  • Systems Biology

Background:

  • Intracellular calcium concentration exhibits transient fluctuations (calcium oscillations) across various cell types and conditions.
  • These whole-cell calcium oscillations are increasingly recognized as stochastic, presenting significant modeling challenges.
  • Existing models often struggle to link whole-cell spike timing to underlying subcellular processes.

Purpose of the Study:

  • To review and highlight advanced statistical approaches for modeling stochastic calcium oscillations.
  • To demonstrate the application of intensity functions for analyzing non-stationary calcium spike sequences.
  • To illustrate how Bayesian inference can extract mechanistic information from single-cell calcium spike data.

Main Methods:

  • Utilizing statistical models that describe the timing of whole-cell calcium spikes.
  • Employing intensity functions to analyze non-stationary calcium spike sequences, such as those during store depletion or time-dependent stimulation.
  • Applying Bayesian concepts for parameter inference from single-cell calcium spike data.

Main Results:

  • Statistical models can effectively analyze non-stationary calcium spike sequences using intensity functions.
  • Bayesian inference allows for the estimation of key statistical model parameters from single-cell data.
  • Whole-cell statistical models provide valuable insights into subcellular mechanisms driving calcium oscillations.
  • The interspike interval distribution in HEK293 cells under constant stimulation follows a Gamma distribution.

Conclusions:

  • Statistical and Bayesian approaches offer powerful tools for dissecting the complexity of stochastic calcium oscillations.
  • These methods bridge the gap between whole-cell oscillatory behavior and underlying subcellular mechanisms.
  • Understanding calcium oscillations through statistical modeling has implications for various biological processes and diseases.