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EPSRC Reference: EP/C010620/1
Title: Stochastic Modelling and Statistical Inference of Gene Regulatory Pathways: Integrating Multiple Sources of Data
Principal Investigator: Wit, Professor E
Other Investigators:
Kolch, Professor W Girolami, Professor M
Researcher Co-Investigators:
Project Partners:
Department: Statistics
Organisation: University of Glasgow
Scheme: Standard Research (Pre-FEC)
Starts: 01 September 2005 Ends: 31 August 2008 Value (£): 217,312
EPSRC Research Topic Classifications:
Bioinformatics Genomics
Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:  
Summary on Grant Application Form
Genes carry the information about the normal functioning of any organism, in particular human beings. Surprisingly, it was recently discovered that there are fewer human genes than originally expected. The sheer complexity and diversity within and among human beings is therefore mainly the result of the way that these genes interact with one another. Whereas the scientific community has been able to identify most genes, the way that these genes interact with each other is still largely a mystery. The ultimate goal of the pharmaceutical industry and many life scientists is to understand organisms at a molecular organizational level. It is generally believed that all single gene diseases have been identified. Most disease types, however, including the most costly and deadly to society, such as cancer and heart disease, are multi-gene complexes, often with a strong environmental component. Consequently, detailed information about the operation of certain genes and proteins, as well as information about regulatory pathways and gene ontology, combined with further information available from the scientific literature ought to be combined in a logical way, when analyzing or modelling such gene complexes. The statistical paradigm for gene network reconstruction is perfectly suited for that task as it is used to dealing with uncertain information and to combining it for reaching conclusions with carefully constructed confidence bounds.Many of the current approaches to gene network reconstruction have several shortcomings: (i) they employ a single data source, typically mRNA levels from microarray experiments, to infer the network structure. This lack of data results in low sensitivity and specificity of the inferences, which means that the conclusions are typically of no interest to practicing life scientists; (ii) network inference is typically ad hoc and not explicitly based on any statistical model, let alone on a proper likelihood method; (iii) furthermore, there has been very little objective validation of the resulting networks i.e. most networks have neither been validated biologically nor have the inference methods been validated statistically. The approach that we are proposing attempts to overcome all three aforementioned caveats: (i) rather than basing our inference merely on a single data source, we propose to combine the available information from different sources in a statistically principled manner; (ii) we propose to base any inference on an explicit statistical model, which incorporates prior biological knowledge and allows for imprecision and other forms of uncertainty; (iii) by setting up a biologically validated stochastic model, which describes the genetic pathway studied by Prof Kolch, we can achieve both an objective validation of any gene network reconstruction method developed and in depth assessment of the novel biological insights which the inferred structures provide.Therefore, this project will provide appropriate novel statistical methods that will be required to model and reconstruct gene regulation networks. These methods will assist in underpinning the next important phase of post-genomic research that attempts to understand the complexity of higher order organisms due to genome-wide gene connectivity and interaction.
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