We use advanced bioinformatic and computational methods to investigate similarities and differences among genomes and the proteins they encode. Our goal is to make rigorous quantitative inferences, at both global and fine scales, about how genomes, gene and protein families, regulatory networks and cellular functions have evolved and diversified. To deal with very large quantities of data, we use advanced data management methods, implement high-throughput computational workflows, and develop new algorithms, approaches and software.

Genomes have diversified, both structurally and functionally, from shared ancestral states. We develop methods and employ analytical pipelines to reconstruct the paths of descent (phylogenomics) and to study processes of change through time (evolutionary genomics). We have characterised pathways of lateral genetic transfer where genetic information moves across, not within, genealogical lineages, and have developed statistically based approaches to discovery of genetically recombined regions and recombination breakpoints. We are now applying these approaches to understand genome diversification and the evolution of pathogenicity in bacteria.

 

The major direction of our research is the inference, comparison and analysis of biomolecular networks in mammalian cells in normal development and disease. We are developing scalable approaches that let us interrogate diverse data types including molecular sequences (single-nucleotide polymorphisms and copy-number variation), protein and RNA structures, metabolic and signalling pathways, regulatory and moelcular interaction networks, gene expression profiles, subcellular localisation, cellular function, orthology maps and phylogenetic profiles.

The Visible Cell® e-project

The mammalian cell is a complex, highly structured entity. In normal development, gene products are trafficked to specific subcellular structures, where they form interaction networks from which cellular function emerges. In collaboration with colleagues within IMB and the ARC Centre of Excellence in Bioinformatics, we are developing the Visible Cell® project, a computer-based environment for generation and testing of biological hypotheses linking the genome with cellular structure and function (phenome).

The term e-research (or e-science) is increasingly applied to large interdisciplinary projects that, like the Visible Cell®, involve automated data capture, knowledge engineering, information integration, high-performance computational modelling across multiple spatial and temporal scales, large-scale data management and remote collaboration. E-research concepts and technologies being applied within the Visible Cell® project include a formalised software development environment, markup languages for biological data and computational models, data Grid technology (Storage Resource Broker) and Web Services.

BioMANTA: toward a Semantic Interactome

The Modelling and Analysis of Biological Network Activity (BioMANTA) project aims to apply Semantic Web technologies to the discovery and modelling of complex molecular interactions in the mammalian cell. The project links researchers at Pfizer Research Technology Centre (Cambridge, Massachusetts) and IMB.

Semantic Web technologies form the basis of the “Deep Web” vision of Tim Berners-Lee and the W3C (World Wide Web Consortium). The current WWW makes an unprecedented amount of information available to human search and retrieval, but represents only a tiny iceberg-tip compared with the vast quantity of information – estimated to be 100- to 1000-fold greater – that is electronically accessible but not human-readable. The goal of W3C is to develop approaches and protocols to access, mine and make (human) sense of this “Deep Web”. A key part of W3C is directed at developing Semantic Web technologies for flexible data integration, automated inferencing and advanced machine learning methods over very large disparate data (http://www.w3.org/2001/sw/).

Semantic Web technologies may hold tremendous promise in the drug discovery and development process including target identification, biomarker discovery, mechanistic modelling of toxicity, and large-scale ‘omics data analysis. Relevant data sets are gigantic (beyond the ability of individual humans to reason-over) and diverse, but contain rich information on the complex networks of biological molecules (enzymes, regulators, small RNAs) that transduce genomic information into biological function. Human reasoning over such vast data is further compromised by the complexity of the underlying biology, and the possible absence or undependability of data.

Already a few key protein databases (notably the unified protein resource UniProt) have been converted to the format (RDF) being developed by W3C to realise a Semantic Web. The BioMANTA project is aimed at converting public and selected private (Pfizer, IMB) protein-protein interaction and biological pathway data to RDF, and applying early-generation inferencing tools to ask complex questions about biological function, e.g. relationships between the activation of kinase pathways and the development of inflammation or hepato- or cardiotoxicity. The project also provides for the quantitative mathematical modelling of inferred pathways, and the experimental validation of hypothesis using high-throughput array-based technologies.


Research projects

• About IMB
• Research
• Research Group Leaders
• News & Events
• Commercialisation
• Student Opportunities
• Ångstrom Art Collection
• Facilities and Programs
• Community Information
• Employment Opportunities
• Contact
• Sitemap