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I am currently hosting Dr Deirdre Gribbin as a Leverhulme Artist in Residence in my lab to compose a piece of music called "Hearing Your Genes Evolve", inspired by human genetic variation.


The wealth of genome-scale data now available for sequences, structures and interactions provides an unprecedented opportunity to systematically investigate principles of gene and protein interactions. We are particularly interested in the evolution and dynamics of regulatory and physical interaction networks. To address these questions, we combine computational and mathematical approaches with both genome-wide and individual gene/protein experiments.

Our lab focuses on two main areas: (1) transcription factors and the regulation of gene expression, and (2) physical protein-protein interactions and protein complexes.

Transcriptional regulatory networks and genetic switches

It is becoming increasingly clear that not only differences in genes, but also differences in their spatio-temporal expression patterns determine the physiology of an organism – its development, differentiation and behaviour. A key step in the regulation of gene expression is the control of transcription. Transcription factors regulate this process by decoding DNA elements and binding to DNA in a sequence-specific manner. We have developed a prediction pipeline that identifies repertoires of transcription factors in genomes, available in two databases at transcriptionfactor.org and flytf.org.


Research Illustration


In addition, we aim to elucidate transcriptional regulatory networks orchestrating T helper cell differentiation and plasticity. Using the T helper cell system, we want to answer questions such as: What is the hierarchy and kinetics of molecular events that contribute to changes in gene expression? Are the kinetics of these interactions graded or switch-like?


Evolution and assembly of protein complexes

We are also interested in the principles that govern the folding and assembly of protein complexes. We use the informative power of genomic and proteomic data, together with the three-dimensional structures from the Protein DataBank, to filter out the critical changes in sequence and structure that switch protein complex formation from the large number of functionally neutral changes. The 3DComplex.org database is a research tool for our work in this area. We test critical ancestral mutations predicted by our in silico phylogeny-based methods to change protein complexes using biophysical and biochemical techniques in the lab.



Research Illustration