Professor Michael J. E. Sternberg is the Director of the Centre for Integrative Systems Biology and Bioinformatics (CISBIO) and holds the Chair of Structural Bioinformatics.
CISBIO brings together scientists from a wide range of different fields to develop innovative interdisciplinary approaches to understanding biological problems. A key component of the interdisciplinary strategy is a repeated cycle of experimentation and modelling.
Professor Sternberg has been involved in schools outreach programmes funded by the Royal Society and Rolls Royce.
Professor Sternberg entered Bioinformatics via his D.Phil in Biophysics (Oxford). He obtained a first degree in Physics (Cambridge) and a Masters in Computing (Imperial College). He has worked at Oxford, Birkbeck College, Cancer Research UK and joined Imperial College in 2001.
The main research interests of his group are:
- Prediction of protein structure and function
- Prediction of macromolecular docking and interactions
- Prediction of the effect of genetic variants particularlly those associate with disease
- Network modelling for Systems Biology
- Logic-based drug design
Professor Sternberg's group web page is located athttp://www.sbg.bio.ic.ac.uk and includes links to programs and resources available for use by the community.
Full details of his research interests can be found at http://www.sbg.bio.ic.ac.uk/research.html.
Reynolds CR, Islam SA, Sternberg MJE, 2018, EzMol: A Web Server Wizard for the Rapid Visualization and Image Production of Protein and Nucleic Acid Structures., J Mol Biol, Vol:430, Pages:2244-2248
Sternberg MJE, Yosef N, 2018, Computation Resources for Molecular Biology: Special Issue 2018., J Mol Biol, Vol:430, Pages:2181-2183
Cornish AJ, David A, Sternberg MJE, 2018, PhenoRank: reducing study bias in gene prioritization through simulation, Bioinformatics, Vol:34, ISSN:1367-4803, Pages:2087-2095
et al., 2018, Properties of human genes guided by their enrichment in rare and common variants, Human Mutation, Vol:39, ISSN:1059-7794, Pages:365-370
et al., 2017, ePlant: Visualizing and Exploring Multiple Levels of Data for Hypothesis Generation in Plant Biology, Plant Cell, Vol:29, ISSN:1040-4651, Pages:1806-1821