6 research outputs found
THE MECHANISM OF CYTOKINE SYNERGY INDUCED BY COMBINATORIAL TLR ACTIVATION
Ph.DDOCTOR OF PHILOSOPH
Developing a framework for semi-automated rule-based modelling for neuroscience research
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
Modeling and analysis of biopathways dynamics
10.1142/S0219720012310014Journal of Bioinformatics and Computational Biology104JBCB