Development of a graph-theoretic approach to predict protein function by integrating large scale heterogeneous data

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The list of organisms with completed genome sequence is continuously growing and this has led to the identification of thousands of genes whose function is still unknown. These genes could potentially be involved in important biological cell functions and could represent important targets for diagnostic and pharmacogenomics studies and be of industrial and agronomical importance. A major undertaking for biology is therefore that of identifying the function of these uncharacterized genes on a genomic scale. The challenge for bioinformatics is then to devise algorithmic methods that, given a gene, can predict a hypothesis for its function that can then be validated by wet-lab assays. Luckily, new experimental techniques have become available, producing data which offer clues about protein function and can therefore be employed for function prediction, e.g. protein interaction data, gene expression data. Some experimental and computational data have a natural representation as networks (e.g. protein interaction data), others are inherently 'one-dimensional' (e.g. sequence patterns). Three facts have recently become clear: while each data type contains important information that can help in determining the function of a protein, no single data type by itself suffices; large-scale functional inference greatly improves by integrating evidence from different sources; for those data types which can be represented as networks, the best results are obtained by algorithms that take advantage of the networks' topologies. So far, methods that make functional inferences on networks are very limited in the type of data they can integrate, while methods that can integrate a greater variety of data do not take advantage of the networks' topologies. I intend to investigate a general method that can integrate essentially any data type currently available taking into account its intrinsic structure: it takes advantage of the graph topology for network data, and it can integrate this evidence together with one-dimensional information. I shall develop graph-theoretical methods that use the diffusion of information over graphs to generate functional evidence from network data. This evidence is then combined with other one-dimensional information using machine learning techniques. The strength of the methodology lies in its ability to use diverse sets of noisy data, and to combine them to obtain sound statistical inferences; the weak signals contained in each dataset is enhanced by integrating the data. The methodology will be first developed on Yeast, and I shall then transfer this approach to higher organisms such as C. elegans, D. melanogaster, A. thaliana, and H. sapiens. For all these organisms the performance of the algorithms will then be evaluated 'in silico' by means of test sets; that is I shall verify the accuracy of the methods at predicting the function for genes whose annotation is known. The approach will then be tested 'in vivo' on a sub-network of genes that form signalling pathways (MAPK signalling) and function to transmit information from receptors to gene expression. MAPK pathway components are highly diversified in the model plant, Arabidopsis thaliana, with 123 components. For many of these we do not know how they connect up and what their biological functions are. These will be predicted by the algorithms and then functionally tested by silencing their expression using RNA interference and in mutant lines. I shall also design and implement stand-alone and web-based software tools incorporating the algorithms developed. The applications will enable the biologist to easily apply the algorithms through a user-friendly interface; to visualize the relevant biological networks thus making the inference process transparent and providing an explanation for the functional annotation predicted by the system. A web tool will also be created. All these tools will be made freely available to the scientific community.