Defining complex rule-based models in space and over time
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Date
26/11/2015Author
Wilson-Kanamori, John Roger
Kanamori, John
Metadata
Abstract
Computational biology seeks to understand complex spatio-temporal phenomena across multiple
levels of structural and functional organisation. However, questions raised in this context
are difficult to answer without modelling methodologies that are intuitive and approachable for
non-expert users. Stochastic rule-based modelling languages such as Kappa have been the focus
of recent attention in developing complex biological models that are nevertheless concise,
comprehensible, and easily extensible. We look at further developing Kappa, in terms of how
we might define complex models in both the spatial and the temporal axes.
In defining complex models in space, we address the assumption that the reaction mixture
of a Kappa model is homogeneous and well-mixed. We propose evolutions of the current iteration
of Spatial Kappa to streamline the process of defining spatial structures for different
modelling purposes. We also verify the existing implementation against established results in
diffusion and narrow escape, thus laying the foundations for querying a wider range of spatial
systems with greater confidence in the accuracy of the results.
In defining complex models over time, we draw attention to how non-modelling specialists
might define, verify, and analyse rules throughout a rigorous model development process. We
propose structured visual methodologies for developing and maintaining knowledge base data
structures, incorporating the information needed to construct a Kappa rule-based model. We
further extend these methodologies to deal with biological systems defined by the activity of
synthetic genetic parts, with the hope of providing tractable operations that allow multiple users
to contribute to their development over time according to their area of expertise.
Throughout the thesis we pursue the aim of bridging the divide between information sources
such as literature and bioinformatics databases and the abstracting decisions inherent in a
model. We consider methodologies for automating the construction of spatial models, providing
traceable links from source to model element, and updating a model via an iterative
and collaborative development process. By providing frameworks for modellers from multiple
domains of expertise to work with the language, we reduce the entry barrier and open the field
to further questions and new research.