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Multiscale modelling of cellular calcium signalling

Multiscale modelling of cellular calcium signalling
Multiscale modelling of cellular calcium signalling
The ability to design effective medicines that treat disease requires a detailed knowledge of the associated natural processes. These natural processes operate on a range of complex spatio-temporal scales. Computational modelling allows us to investigate such processes, however these often provide detail only at a single spatiotemporal scale. Multiscale modelling aims to combine or link these scales represented by computational models, such that information is passed between different types of simulation. The aim of this investigation is to develop a multiscale model of cellular calcium signalling through extension of existing models. The pancreatic acinar cell was chosen for this treatment, owing to the polarised structure, range of signalling events, and disease states that are characteristic of this cell type.

Published models, that use networks of Ordinary Differential Equations (ODE), were implemented to gain understanding of the complex dynamics exhibited by cellular systems. ODE-based biological network models reduce the complexity of a system by making assumptions about the spatio-temporal properties. A model of the calcium dynamics of the pancreatic acinar cell was coupled to a model of the mitochondria, and the resulting collection of ODEs was extended to hypothetically simulate the onset of acute pancreatitis; a disease state whose mechanism is not yet fully understood. The entire model was then reformulated into one that uses the Finite Element Method (FEM) to solve the diffusion equation of species around the system, as well as to distribute spatially the contributing factors to cellular calcium signalling, such as associated calcium pumps. This provided a more physical representation of the cell, compared to the ODE model.

However, some of the results found in the original published material relating to model behaviour could not be reproduced fully in the FEM implementation. The issues encountered during this study highlight the challenges faced when modelling complex systems that have incomplete data, and instead rely on heavily fitted parameters. Despite this, the results demonstrate behaviour consistent with many experimental observations, due to the sophistication of FEM over ODE models. These include insight into the distribution of mitochondria, calcium tunnelling in the endoplasmic reticulum (ER), and mitochondrial calcium accumulation as a mechanism of acute pancreatitis onset. These findings represent the unique paths that may be followed while constructing a multiscale model, and a platform from which further research may continue.
Mason, Daniel John
07d13e44-8e75-4b51-9d77-708b117eb1aa
Mason, Daniel John
07d13e44-8e75-4b51-9d77-708b117eb1aa
Essex, Jonathan
1f409cfe-6ba4-42e2-a0ab-a931826314b5

Mason, Daniel John (2012) Multiscale modelling of cellular calcium signalling. University of Southampton, Chemistry, Doctoral Thesis, 252pp.

Record type: Thesis (Doctoral)

Abstract

The ability to design effective medicines that treat disease requires a detailed knowledge of the associated natural processes. These natural processes operate on a range of complex spatio-temporal scales. Computational modelling allows us to investigate such processes, however these often provide detail only at a single spatiotemporal scale. Multiscale modelling aims to combine or link these scales represented by computational models, such that information is passed between different types of simulation. The aim of this investigation is to develop a multiscale model of cellular calcium signalling through extension of existing models. The pancreatic acinar cell was chosen for this treatment, owing to the polarised structure, range of signalling events, and disease states that are characteristic of this cell type.

Published models, that use networks of Ordinary Differential Equations (ODE), were implemented to gain understanding of the complex dynamics exhibited by cellular systems. ODE-based biological network models reduce the complexity of a system by making assumptions about the spatio-temporal properties. A model of the calcium dynamics of the pancreatic acinar cell was coupled to a model of the mitochondria, and the resulting collection of ODEs was extended to hypothetically simulate the onset of acute pancreatitis; a disease state whose mechanism is not yet fully understood. The entire model was then reformulated into one that uses the Finite Element Method (FEM) to solve the diffusion equation of species around the system, as well as to distribute spatially the contributing factors to cellular calcium signalling, such as associated calcium pumps. This provided a more physical representation of the cell, compared to the ODE model.

However, some of the results found in the original published material relating to model behaviour could not be reproduced fully in the FEM implementation. The issues encountered during this study highlight the challenges faced when modelling complex systems that have incomplete data, and instead rely on heavily fitted parameters. Despite this, the results demonstrate behaviour consistent with many experimental observations, due to the sophistication of FEM over ODE models. These include insight into the distribution of mitochondria, calcium tunnelling in the endoplasmic reticulum (ER), and mitochondrial calcium accumulation as a mechanism of acute pancreatitis onset. These findings represent the unique paths that may be followed while constructing a multiscale model, and a platform from which further research may continue.

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Published date: 30 September 2012
Organisations: University of Southampton, Chemistry

Identifiers

Local EPrints ID: 350651
URI: https://eprints.soton.ac.uk/id/eprint/350651
PURE UUID: 676c9ac8-a143-4ea0-bc3d-591f0ea7991f
ORCID for Jonathan Essex: ORCID iD orcid.org/0000-0003-2639-2746

Catalogue record

Date deposited: 09 Apr 2013 11:32
Last modified: 28 Nov 2018 01:35

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Contributors

Author: Daniel John Mason
Thesis advisor: Jonathan Essex ORCID iD

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