Paul Thomas

Professor of Preventive Medicine and Quantitative and Computational Biology
Email Office NRT 2502 Office Phone 323-442-7799
  • Summary Statement of Research Interests

    For more information, see the Thomas group website

    Evolution of genes and gene functions. The evolutionary histories of genes can be reconstructed from DNA and protein sequence data. The functions of genes are usually inherited during evolution, but a key driver of organism evolution is the modification or change of gene function. We are interested in studying how this functional evolution occurs and how it can be computationally modeled. An important application of this work is in understanding the functions of human genes, by using experimental data gained in many different “model organisms,” such as the mouse, the fruit fly and the bacterium E. coli.

    Computational representation of biological knowledge. Scientists working for hundreds of years, and particularly since the advent of modern molecular biology techniques over 30 years ago, have amassed a great deal of knowledge of how biological systems work—too much to be completely known by any one person. The Gene Ontology Consortium aims to represent this knowledge in terms of a structured model, enabling computers to help in the analysis and interpretation of new biological data. We are developing extensions to the Gene Ontology to better represent how genes encode biological function at the molecular level and in the context of the cell, the organism and even its environment.

    Analysis of genomics data in the context of prior biological knowledge. It is now possible to perform “genomics” experiments in which, for example, variations in all 20,000 human genes are determined for thousands of different individuals, and compared between those affected by a particular disease, and those unaffected. We are exploring how ontologies can be used in an informed fashion, to help interpret the results of genome-wide association studies (GWAS) in humans.

    Prediction of functional genetic variation using evolutionary sequence reconstruction. Most genetic variants in humans have no discernable effect at all, yet some variants can greatly affect, for example, the risk of developing a particular disease. We are interested in computational reconstructions of the changes genes have undergone during evolution, to help predict which human genetic variants are most likely to confer disease risk.

    Project-specific websites