Gene Function Hypotheses for the Campylobacter jejuni Glycome Generated by a Logic-Based Approach |
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Authors: | Michael JE Sternberg Alireza Tamaddoni-Nezhad Victor I Lesk Emily Kay Paul G Hitchen Adrian Cootes Lieke B van Alphen Marc P Lamoureux Harold C Jarrell Christopher J Rawlings Evelyn C Soo Christine M Szymanski Anne Dell Brendan W Wren Stephen H Muggleton |
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Institution: | 1. Centre for Integrative Systems Biology, Imperial College London, London SW7 2AZ, UK;2. Division of Molecular Biosciences, Department of Life Science, Imperial College London, London SW7 2AZ, UK;3. Department of Computing, Imperial College London, London SW7 2AZ, UK;4. Department of Pathogen Molecular Biology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK;5. Alberta Glycomics Centre, Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada T6G 2E9;6. Institute for Biological Sciences, National Research Council, Ottawa, Ontario, Canada K1A 0R6;7. Department of Computational and Systems Biology, Rothamsted Research, Harpenden AL5 2JQ, UK;8. Institute for Marine Biosciences, National Research Council, Halifax, Nova Scotia, Canada B3H 3Z1 |
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Abstract: | Increasingly, experimental data on biological systems are obtained from several sources and computational approaches are required to integrate this information and derive models for the function of the system. Here, we demonstrate the power of a logic-based machine learning approach to propose hypotheses for gene function integrating information from two diverse experimental approaches. Specifically, we use inductive logic programming that automatically proposes hypotheses explaining the empirical data with respect to logically encoded background knowledge. We study the capsular polysaccharide biosynthetic pathway of the major human gastrointestinal pathogen Campylobacter jejuni. We consider several key steps in the formation of capsular polysaccharide consisting of 15 genes of which 8 have assigned function, and we explore the extent to which functions can be hypothesised for the remaining 7. Two sources of experimental data provide the information for learning—the results of knockout experiments on the genes involved in capsule formation and the absence/presence of capsule genes in a multitude of strains of different serotypes. The machine learning uses the pathway structure as background knowledge. We propose assignments of specific genes to five previously unassigned reaction steps. For four of these steps, there was an unambiguous optimal assignment of gene to reaction, and to the fifth, there were three candidate genes. Several of these assignments were consistent with additional experimental results. We therefore show that the logic-based methodology provides a robust strategy to integrate results from different experimental approaches and propose hypotheses for the behaviour of a biological system. |
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