Advanced Stochastic Computation for Inference from Tree, Graph and Network Models

As a result of recent experimental advances, large amounts of biological data are becoming available for humans and other organisms.
Such data pose inference problems well beyond the capabilities of standard statistical tools. Experimental advances must be accompanied by the development of suitable biostatistical and bioinformatic tools that will make efficient use of the complex data to improve our understanding of the genetic forces shaping the evolution of genome organization.

Molecular evolution and comparative genomics are no longer fields where collecting data is the main obstacle to progress. Many probabilistic models proposed in biology try to capture the evolutionary mechanisms and reflect the data generating mechanism. Whilst these models still have limitations, there have been substantial improvements in recent years.
We contend that progress is mainly limited by a lack of adequate computational tools for extracting information from existing data and performing inference for complex models. A major bottleneck in the application of probabilistic models to biology is that their calibration is computationally expensive and in many instances not possible using modern techniques. Thus, researchers often prefer to use simple summary statistics to characterize the underlying biological process; this approach is obviously unsatisfactory. E.g., topological summary statistics capture basic characteristics of binary interaction networks but are affected by different types of bias so great, that caution must be taken when drawing conclusions. A big challenge for systems biology nowadays consists in developing statistical and bioinformatics tools within the rigorous framework of probabilistic modelling that will allow for a better and more comprehensive understanding of cellular functions.

In the last few decades a wealth of research has been performed on model-based inference for molecular data accompanied by an explosion of research in developing computationally efficient methods to facilitate it. Broadly speaking, there are three main approaches to statistical inference in molecular biology: (i) importance sampling (IS) for likelihood evaluation, (ii) Markov chain Monte Carlo (MCMC) methods (iii) Approximate Bayesian Computation (ABC) . In this proposal we concentrate on a combination of advanced IS (or more generally Sequential Monte Carlo (SMC)) and MCMC methods focussed upon Markov models in genetics and bioinformatics. In particular, building upon these techniques, we aim to develop a general framework for approximate inference which is theoretically sound, computationally feasable and still be able to accurately reflect the complexity of the underlying stochastic model. The main life science application on which we will concentrate are: genealogical trees,
protein networks, phylogenetic trees.

The project will develop new methods for inference from post-genomic data. Its benefits will be in the greater interpretability of statistical models derived from such data, generating new biological insights and hypothesis from existing and future projects. The methods will be equally applicable throughout post-genomic science and thus many groups of scientists throughout academia, government, biomedical community and industry will gain from the new techniques. A comprehensive and multi-faceted approach to feedback and engagement with the public and relevant researchers will be employed by the team, in collaboration with UCL Media Relations, which has regular contact with local and national media. The results of our research will be widely disseminated to the public (e.g. via UCL central press office). All aspects of the work will be published in peer-review journals and disseminated via presentations at appropriate conferences and seminars. We will also target publications relating to biology generally in the relevant industrial and more popular press and we will use our previous experience in providing briefings to the press on complex scientific issues.
The non-academic beneficiaries of the project will range across many groups:
(i) Scientists from government agencies, non-government organisations (e.g.~charities) and industries will benefit from an improved ability to analyse data from post-genomic investigations, enabling them to fulfill their research duties.
(ii) Scientists from the chemical, pharmaceutical and food industries, who invest in new technologies and employ `omic' methods in their daily research, will have improved data analysis tools available. This will enhance the competitiveness of these industries in developing new products such as drugs.
(iii) Clinical practitioners will benefit in the long run from a better understanding of biological mechanisms underlying disease, which has profound health and wealth implications. UCL has committed to developing and supporting the highest quality of interdisciplinary and translational research.
(iv) The general public is becoming more aware of post-genomic technologies/projects (e.g.~the human genome project) and this research will make an excellent example of how the new technologies can be combined with advanced computing methods to generate new biological insights.

A great opportunity for dissemination is offered by graduate courses such as the MSc at UCL and NUS. We will create a project web-site to disseminate our methodological development and a suite of statistical and visualization tools accessible to the widest possible audience. Extensive documentation will be provided for the web resources produced, including a user manual and integrated help pages. A mailing list will be established (allowing researchers to register interest in the project) and this will be used to provide updates on data availability during the lifetime of the project, as well as changes/improvements. Monitoring of the number of hits will be undertaken and reported and will give us a measure of success of these outreach activities. A workshop will be held at the end of the project in UCL and it will focus on exchanging knowledge between project participants and the wider scientific community. Any potential commercial exploitation of the outcomes will be examined in collaboration with UCL Consultants Ltd., who can give expert advice on, for example, patent applications, software licensing, material transfer and other legal matters.