Dataset Information

Hierarchic stochastic modelling applied to intracellular Ca(2+) signals.

ABSTRACT: Important biological processes like cell signalling and gene expression have noisy components and are very complex at the same time. Mathematical analysis of such systems has often been limited to the study of isolated subsystems, or approximations are used that are difficult to justify. Here we extend a recently published method (Thurley and Falcke, PNAS 2011) which is formulated in observable system configurations instead of molecular transitions. This reduces the number of system states by several orders of magnitude and avoids fitting of kinetic parameters. The method is applied to Ca(2+) signalling. Ca(2+) is a ubiquitous second messenger transmitting information by stochastic sequences of concentration spikes, which arise by coupling of subcellular Ca(2+) release events (puffs). We derive analytical expressions for a mechanistic Ca(2+) model, based on recent data from live cell imaging, and calculate Ca(2+) spike statistics in dependence on cellular parameters like stimulus strength or number of Ca(2+) channels. The new approach substantiates a generic Ca(2+) model, which is a very convenient way to simulate Ca(2+) spike sequences with correct spiking statistics.

SUBMITTER: Moenke G

PROVIDER: S-EPMC3531454 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Hierarchic stochastic modelling applied to intracellular Ca(2+) signals.

Moenke Gregor G Falcke Martin M Thurley Keven K

PloS one 20121227 12

Important biological processes like cell signalling and gene expression have noisy components and are very complex at the same time. Mathematical analysis of such systems has often been limited to the study of isolated subsystems, or approximations are used that are difficult to justify. Here we extend a recently published method (Thurley and Falcke, PNAS 2011) which is formulated in observable system configurations instead of molecular transitions. This reduces the number of system states by se ...[more]

PMID: 23300536

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Project description:Pancreatic β-cells release insulin upon a rise in blood glucose. The precise mechanisms of stimulus-secretion coupling, and its failure in Diabetes Mellitus Type 2, remain to be elucidated. The consensus model, as well as a class of currently prescribed anti-diabetic drugs, are based around the observation that glucose-evoked ATP production in β-cells leads to closure of cell membrane ATP-gated potassium (KATP) channels, plasma membrane depolarisation, Ca2+ influx, and finally the exocytosis of insulin granules. However, it has been demonstrated by the inactivation of this pathway using genetic and pharmacological means that closure of the KATP channel alone may not be sufficient to explain all β-cell responses to glucose elevation. We have previously proposed that NAADP-evoked Ca2+ release is an important step in stimulus-secretion coupling in pancreatic β-cells. Here we show using total internal reflection fluorescence (TIRF) microscopy that glucose as well as the Ca2+ mobilising messenger nicotinic acid adenine dinucleotide phosphate (NAADP), known to operate in β-cells, lead to highly localised elementary intracellular Ca2+ signals. These were found to be obscured by measurements of global Ca2+ signals and the action of powerful SERCA-based sequestration mechanisms at the endoplasmic reticulum (ER). Building on our previous work demonstrating that NAADP-evoked Ca2+ release is an important step in stimulus-secretion coupling in pancreatic β-cells, we provide here the first demonstration of elementary Ca2+ signals in response to NAADP, whose occurrence was previously suspected. Optical quantal analysis of these events reveals a unitary event amplitude equivalent to that of known elementary Ca2+ signalling events, inositol trisphosphate (IP3) receptor mediated blips, and ryanodine receptor mediated quarks. We propose that a mechanism based on these highly localised intracellular Ca2+ signalling events mediated by NAADP may initially operate in β-cells when they respond to elevations in blood glucose.

Project description:BackgroundInfectious diseases such as SARS and H1N1 can significantly impact people's lives and cause severe social and economic damages. Recent outbreaks have stressed the urgency of effective research on the dynamics of infectious disease spread. However, it is difficult to predict when and where outbreaks may emerge and how infectious diseases spread because many factors affect their transmission, and some of them may be unknown.MethodsOne feasible means to promptly detect an outbreak and track the progress of disease spread is to implement surveillance systems in regional or national health and medical centres. The accumulated surveillance data, including temporal, spatial, clinical, and demographic information can provide valuable information that can be exploited to better understand and model the dynamics of infectious disease spread. The aim of this work is to develop and empirically evaluate a stochastic model that allows the investigation of transmission patterns of infectious diseases in heterogeneous populations.ResultsWe test the proposed model on simulation data and apply it to the surveillance data from the 2009 H1N1 pandemic in Hong Kong. In the simulation experiment, our model achieves high accuracy in parameter estimation (less than 10.0 % mean absolute percentage error). In terms of the forward prediction of case incidence, the mean absolute percentage errors are 17.3 % for the simulation experiment and 20.0 % for the experiment on the real surveillance data.ConclusionWe propose a stochastic model to study the dynamics of infectious disease spread in heterogeneous populations from temporal-spatial surveillance data. The proposed model is evaluated using both simulated data and the real data from the 2009 H1N1 epidemic in Hong Kong and achieves acceptable prediction accuracy. We believe that our model can provide valuable insights for public health authorities to predict the effect of disease spread and analyse its underlying factors and to guide new control efforts.

Dataset Information

Hierarchic stochastic modelling applied to intracellular Ca(2+) signals.

Publications

Hierarchic stochastic modelling applied to intracellular Ca(2+) signals.

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