text: Dr Heba Sailem — Institute of Biomedical Engineering
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  My research is focused on developing machine-learning approaches to better characterise gene functions at a multiple systems level. In particular, I am interested in developing automated knowledge discovery approaches for high throughput imaging datasets of cells and tissues. To achieve that, I combine methods from Computer Vision, Bioinformatics and Systems Biology fields. I work with Prof Jens Rittscher and Prof Andrew Zisserman at the Department of Engineering supported by a Sir Henry Wellcome Research Fellowship.
  Before joining Oxford University, I did my PhD at the Institute of Cancer Research in London. During my PhD I developed methods for integration of phenotypic data with gene expression [1], modelling of the relationship between signalling and cell context [2], modelling the dynamics of cell morphogenesis [3, 4]. In these studies, I discovered new links between cell shape and breast cancer progression and identified signalling networks that might play a role in tumour invasiveness.
  I also devised PhenoPlot, one of the first tools that is specifically designed for visualising phenotypic data [5]. This method facilitates the interpretation of high dimensional data by generating pictorial representations of cells based on hundreds to thousands of measurements.
  1. Sailem, H. and Bakal, C. (2017) Identification of clinically predictive metagenes that encode components of a network coupling cell shape to transcription by image-omics. Genome Research.
  2. Sero, J.*, Sailem, H.*, Ardy, R., Almuttaqi, H., Zhang, T., & Bakal, C. (2015) Cell Shape and context regulate the nuclear translocation of NF-kappaB in breast epithelial and tumor Cells. Molecular Systems Biology.
  3. Sailem, H., Bousgouni, V., Cooper, S., & Bakal, C. (2014). Cross-talk between Rho and Rac GTPases drives deterministic exploration of cellular shape space and morphological heterogeneity. Open Biology.
  4. Yin, Z., Sadok, A., Sailem, H., McCarthy, A., Xia, X., Li, F., … Bakal, C. (2013). A screen for morphological complexity identifies regulators of switch-like transitions between discrete cell shapes. Nature Cell Biology.
  5. Sailem, H., Sero, J., & Bakal, C. (2015) Visualizing cellular imaging data using PhenoPlot. Nature Communications. (Highlighted in Nature Methods)