The University of Southampton
University of Southampton Institutional Repository

Probabilistic inference in models of systems biology

Probabilistic inference in models of systems biology
Probabilistic inference in models of systems biology
In Systems Biology, it is usual to use a set of ordinary differential equations to characterize biological function at a system level. The parameters in these equations generally reflect the reaction or decay rates of a molecular species, while states characterize the concentration values of species of interest, e.g. mRNA, proteins and metabolites. Often parameter values are estimated from in vitro experiments which may not be true reflections of the in vivo environments. With internal states, some may not be accessible for experimental measurement. Hence there is interest in estimating parameter values and states from noisy or incomplete observations taken at inputs/outputs of a system. This thesis explores several probabilistic inference approaches to do this.

The study starts from a thorough investigation of the effectivenesses of the most commonly used one-pass inference methods, from which the non-parametric particle filtering approach is shown to be the most powerful method in the sequential category. After this study, the family of Approximate Bayesian Computation (ABC) methods, also known as likelihood-free batch approach, is reviewed chronologically and its advantages and deficiencies are summarized via a statistical toy example and two biological models. Additionally, a novel ABC method coupled with the sensitivity analysis technique has been developed and demonstrated on three periodic and one transient biological models. This approach has the potential to solve problem in high dimension by selectively allocating computational budget. In order to assess the capability of the proposed method in real-world problems, we have modeled the polymer pathway and conducted quantitative analysis via the proposed inference approach.
Liu, Xin
82424b16-94ac-4de7-972f-b384d98cba3f
Liu, Xin
82424b16-94ac-4de7-972f-b384d98cba3f
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Liu, Xin (2014) Probabilistic inference in models of systems biology. University of Southampton, Physical Sciences and Engineering, Doctoral Thesis, 227pp.

Record type: Thesis (Doctoral)

Abstract

In Systems Biology, it is usual to use a set of ordinary differential equations to characterize biological function at a system level. The parameters in these equations generally reflect the reaction or decay rates of a molecular species, while states characterize the concentration values of species of interest, e.g. mRNA, proteins and metabolites. Often parameter values are estimated from in vitro experiments which may not be true reflections of the in vivo environments. With internal states, some may not be accessible for experimental measurement. Hence there is interest in estimating parameter values and states from noisy or incomplete observations taken at inputs/outputs of a system. This thesis explores several probabilistic inference approaches to do this.

The study starts from a thorough investigation of the effectivenesses of the most commonly used one-pass inference methods, from which the non-parametric particle filtering approach is shown to be the most powerful method in the sequential category. After this study, the family of Approximate Bayesian Computation (ABC) methods, also known as likelihood-free batch approach, is reviewed chronologically and its advantages and deficiencies are summarized via a statistical toy example and two biological models. Additionally, a novel ABC method coupled with the sensitivity analysis technique has been developed and demonstrated on three periodic and one transient biological models. This approach has the potential to solve problem in high dimension by selectively allocating computational budget. In order to assess the capability of the proposed method in real-world problems, we have modeled the polymer pathway and conducted quantitative analysis via the proposed inference approach.

PDF
__soton.ac.uk_ude_personalfiles_users_jo1d13_mydesktop_Liu.pdf - Other
Download (12MB)

More information

Published date: December 2014
Organisations: University of Southampton, Vision, Learning and Control

Identifiers

Local EPrints ID: 374334
URI: http://eprints.soton.ac.uk/id/eprint/374334
PURE UUID: fa5bcc90-9a14-4858-98d2-687b86e11f6a

Catalogue record

Date deposited: 17 Feb 2015 09:33
Last modified: 17 Jul 2017 21:27

Export record

Contributors

Author: Xin Liu
Thesis advisor: Mahesan Niranjan

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×