Modeling Mechanisms of Cell Secretion

Tsaneva-Atanasova, Krasimira; Osinga, Hinke M.; Tabak, Joël; Pedersen, Morten Gram

doi:10.1007/s10441-010-9115-8

Cited by 12 publications

(7 citation statements)

References 40 publications

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“…The Pedersen-Sherman model is currently the most comprehensive insulin secretion model which is able to explain an impressive array of experimental observations [10,11,16]. However, before it can be successfully used together with experiments to determine the molecular identities of the granule pools, a few questions need to be addressed of the model itself, namely the consideration of discrete vesicle numbers and the stochasticity of transition events.…”

Section: Discussionmentioning

confidence: 99%

A stochastic version of the Pedersen-Sherman insulin secretion model

Dwivedi

2014

SIURO

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Pedersen and Sherman have recently developed a multi-pool model of insulin vesicle secretion from pancreatic beta-cells [12]. In the PedersenSherman model, pool sizes are reported as concentrations; however, concentrations of the different pools vary by as much as seven orders of magnitude. Very low concentrations indicate there could be discrete numbers of vesicles in some of the pools, leading naturally to the questions regarding stochasticity in the signalling pathway. We therefore simulate a discrete counterpart of the deterministic model with a (hybrid) Gillespie-style stochastic simulation algorithm. The stochastic simulations are calibrated to the deterministic model in the mean. We estimate the variances in pool sizes numerically and show it closely tracks the mean, indicating a Poisson-like result. Our numerical results demonstrate that the mean behavior indicated in the Pedersen-Sherman model is evident with just one islet.

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Section: Discussionmentioning

confidence: 99%

A stochastic version of the Pedersen-Sherman insulin secretion model

Dwivedi

2014

SIURO

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“…There, it is included into a mathematical model for insulin secretion applied to IVGTT and OGTT data. Also Pedersen et al presented an updated version of the hypothesis for oral minimal models of insulin secretion in [40] and Tsaneva‐Atanasova described insulin secretion in a general context of mechanisms of cell secretion [41]. …”

Section: Mathematical Modelmentioning

confidence: 99%

Dynamics of glucose and insulin concentration connected to the β‐cell cycle: model development and analysis

Gallenberger

Castell

Hense

et al. 2012

Theor Biol Med Model

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BackgroundDiabetes mellitus is a group of metabolic diseases with increased blood glucose concentration as the main symptom. This can be caused by a relative or a total lack of insulin which is produced by the β‐cells in the pancreatic islets of Langerhans. Recent experimental results indicate the relevance of the β‐cell cycle for the development of diabetes mellitus.MethodsThis paper introduces a mathematical model that connects the dynamics of glucose and insulin concentration with the β‐cell cycle. The interplay of glucose, insulin, and β‐cell cycle is described with a system of ordinary differential equations. The model and its development will be presented as well as its mathematical analysis. The latter investigates the steady states of the model and their stability.ResultsOur model shows the connection of glucose and insulin concentrations to the β‐cell cycle. In this way the important role of glucose as regulator of the cell cycle and the capability of the β‐cell mass to adapt to metabolic demands can be presented. Simulations of the model correspond to the qualitative behavior of the glucose‐insulin regulatory system showed in biological experiments.ConclusionsThis work focusses on modeling the physiological situation of the glucose‐insulin regulatory system with a detailed consideration of the β‐cell cycle. Furthermore, the presented model allows the simulation of pathological scenarios. Modification of different parameters results in simulation of either type 1 or type 2 diabetes.

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“…Such measurements can then be utilized by mechanistic models of cell behavior which require such quantitative data sets for testing predictions. (Licko, 1973; Sunnaker et al, 2013; Tsaneva-Atanasova et al, 2010)…”

Section: Introductionmentioning

confidence: 99%

Quantifying time-varying cellular secretions with local linear models

Byers

Christodoulides

Delehanty

et al. 2017

Heliyon

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Extracellular protein concentrations and gradients initiate a wide range of cellular responses, such as cell motility, growth, proliferation and death. Understanding inter-cellular communication requires spatio-temporal knowledge of these secreted factors and their causal relationship with cell phenotype. Techniques which can detect cellular secretions in real time are becoming more common but generalizable data analysis methodologies which can quantify concentration from these measurements are still lacking. Here we introduce a probabilistic approach in which local-linear models and the law of mass action are applied to obtain time-varying secreted concentrations from affinity-based biosensor data. We first highlight the general features of this approach using simulated data which contains both static and time-varying concentration profiles. Next we apply the technique to determine concentration of secreted antibodies from 9E10 hybridoma cells as detected using nanoplasmonic biosensors. A broad range of time-dependent concentrations was observed: from steady-state secretions of 230 pM near the cell surface to large transients which reached as high as 56 nM over several minutes and then dissipated.

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Modeling Mechanisms of Cell Secretion

Cited by 12 publications

References 40 publications

A stochastic version of the Pedersen-Sherman insulin secretion model

A stochastic version of the Pedersen-Sherman insulin secretion model

Dynamics of glucose and insulin concentration connected to the β‐cell cycle: model development and analysis

Quantifying time-varying cellular secretions with local linear models

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