Bittig, Arne T. (2017) ML-Space: Hybrid Spatial Gillespie and Brownian Motion Simulation at Multiple Levels, and a Rule-based Description Language. PhD thesis, Institute of Computer Science, University of Rostock.
Full text not available from this repository.Abstract
Computer models and simulations of micro-biological processes facilitate structuring knowledge and making testable predictions ab out their behavior. Methods that treat cells like well-stirred systems are well established in that regard, but the spatial distribution of key actors often cannot be neglected. Spatio-temporal dynamics of cellular processes can be simulated at different levels of detail, from (deterministic) partial differential equations via the spatial Stochastic Simulation algorithm to tracking Brownian trajectories of individual particles. In this thesis, a spatial simulation algorithm for multi-level models, including dynamically hierarchically nested cellular compartments and entities is presented. The approach, called ML-Space, combines stochastic spatial algorithms in discretized space, i.e. population- and subvolume-based simulation, with individual particles moving in continuous space that have spatial extensions and can contain other particles. For a formal description of the systems to be simulated spatially, ML-Space provides a rule- based specification language. It incorporates spatial properties of the model actors via named attributes and supports concise and compact descriptions of models and allows easy adaptation of the spatial resolution, while abstracting from the concrete simulation algorithm as much as possible. ML-Space has successfully been used to model disease-relevant aspects of various cellular processes, including mitochondrial fission dynamics and actin filament formation. Additionally, existing spatial models with multi-level aspects were successfully reproduced and an abstract multi-level model was developed to explore the simulation approaches’ capabilities and performance.
| Item Type: | Thesis (PhD) |
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