Developing a framework for semi-automated rule-based modelling for neuroscience research
Wysocka, Emilia Malgorzata
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Dynamic modelling has significantly improved our understanding of the complex molecular mechanisms underpinning neurobiological processes. The detailed mechanistic insights these models offer depend on the availability of a diverse range of experimental observations. Despite the huge increase in biomolecular data generation from novel high-throughput technologies and extensive research in bioinformatics and dynamical modelling, efficient creation of accurate dynamical models remains highly challenging. To study this problem, three perspectives are considered: comparison of modelling methods, prioritisation of results and analysis of primary data sets. Firstly, I compare two models of the DARPP-32 signalling network: a classically defined model with ordinary differential equations (ODE) and its equivalent, defined using a novel rule-based (RB) paradigm. The RB model recapitulates the results of the ODE model, but offers a more expressive and flexible syntax that can efficiently handle the “combinatorial complexity” commonly found in signalling networks, and allows ready access to fine-grain details of the emerging system. RB modelling is particularly well suited to encoding protein-centred features such as domain information and post-translational modification sites. Secondly, I propose a new pipeline for prioritisation of molecular species that arise during model simulation using a recently developed algorithm based on multivariate mutual information (CorEx) coupled with global sensitivity analysis (GSA) using the RKappa package. To efficiently evaluate the importance of parameters, Hilber-Schmidt Independence Criterion (HSIC)-based indices are aggregated into a weighted network that allows compact analysis of the model across conditions. Finally, I describe an approach for the development of disease-specific dynamical models using genes known to be associated with Attention Deficit Hyperactivity Disorder (ADHD) as an exemplar. Candidate disease genes are mapped to a selection of datasets that are potentially relevant to the modelling process (e.g. interactions between proteins and domains, protein-domain and kinase-substrates mappings) and these are jointly analysed using network clustering and pathway enrichment analyses to evaluate their coverage and utility in developing rule-based models.