3 research outputs found
Parameter inference for stochastic biological models
PhD ThesisParameter inference is the field concerned with estimating reliable
model parameters from data. In recent years there has been a trend
in the biology community toward single cell technologies such as fluorescent flow cytometry, transcriptomics and mass cytometry: providing a rich array of stochastic time series and temporal distribution
data for analysis. Deterministically, there are a wide range of parameter inference and global optimisation techniques available. However,
these do not always scale well to non-deterministic (i.e., stochastic)
settings — whereby the temporal evolution of the system can be described by a chemical master equation for which the solution is nearly
always intractable, and the dynamic behaviour of a system is hard to
predict. For systems biology, the inference of stochastic parameters
remains a bottleneck for accurate model simulation.
This thesis is concerned with the parameter inference problem for
stochastic chemical reaction networks. Stochastic chemical reaction
networks are most frequently modelled as a continuous time discretestate Markov chain using Gillespie’s stochastic simulation algorithm.
Firstly, I present a new parameter inference algorithm, SPICE, that
combines Gillespie’s algorithm with the cross-entropy method. The
cross-entropy method is a novel approach for global optimisation inspired from the field of rare-event probability estimation. I then
present recent advances in utilising the generalised method of moments for inference, and seek to provide these approaches with a direct stochastic simulation based correction. Subsequently, I present a
novel use of a recent multi-level tau-leaping approach for simulating
population moments efficiently, and use this to provide a simulation
based correction to the generalised method of moments. I also propose a new method for moment closures based on the use of Padé
approximants.
The presented algorithms are evaluated on a number of challenging
case studies, including bistable systems — e.g., the Schlögl System
and the Genetic Toggle Switch — and real experimental data. Experimental results are presented using each of the given algorithms. We
also consider ‘realistic’ data — i.e., datasets missing model species,
multiple datasets originating from experiment repetitions, and datasets
containing arbitrary units (e.g., fluorescence values). The developed
approaches are found to be viable alternatives to existing state-ofthe-art methods, and in certain cases are able to outperform other
methods in terms of either speed, or accuracyNewcastle/Liverpool/Durham BBSRC
Doctoral Training Partnership for financial suppor