DrShamith Samarajiwa

I am a computational biologist and genomicist. I moved to Imperial as an Associate Professor in 2022 and head the Computational Biology and Biomedical AI group and lead the Computational Biomedicine MSc programme. Previously, I was a research group leader at the MRC Cancer Unit, University of Cambridge (2015-2022), a research associate at the CRUK Cambridge Institute, University of Cambridge (2008-2014) and the foundation bioinformatics fellow (2006-2008) at the Monash Institute of Medical Research (now Hudson Institute) in Australia.

The Computational Biology & Biomedical AI lab studies gene and epigenomic (and translatome + metabolome related) regulatory processes that contribute to both normal physiology and disease pathology. This includes the regulation of host defence mechanisms (related to antiviral, inflammatory, and anti-tumour responses), chromatin state changes underlying cellular senescence and ageing, and regulatory mechanisms underlying immune & inflammatory diseases, metabolic diseases and cancer. 

The lab integratively studies gene (transcriptome, translatome and metabolome), epigenome and chromatin architecture regulation affecting disease processes and phenotypes at the systems level. We utilise a mixture of Computational & Systems Biology, multi-omics and Data Science (especially leading edge Artificial Intelligence methods) to integrate, data-mine and deconvolute large, complex and heterogeneous biomedical datasets (extracted from data repositories or generated by us, and our numerous experimental and clinical collaborators) with the aim of uncovering the fundamental rules and complex regulatory logic underlying gene and (epi)genome regulation of disease processes.

There are a number of key focus areas of the lab:

​To gain a mechanistic understanding of how phenotypes are actualised by studying information flow of signaling, metabolic and regulatory networks and interactomes associated with these processes. We do this by developing integrative approaches (Knowledge Graphs, statistical methods etc) and other computational methods to identifying Upstream Regulators of Gene Signatures, Transcription Factor Direct Targets, Enhancer-Promoter Interactions (EPIs) and associated Chromatin Architectural Changes. We are also interested in the associated changes in the Metabalome and Lipodome.

* Immunity: We use the Interferon (IFN) system as a model, utilizing our bespoke IFN stimulated gene (ISG) and pathways resources combined with sophisticated computational algorithms and methods that are being developed by us.

* Ageing: We are also interested in regulatory processes underlying cellular senescence and ageing. We develop integrative approaches of epigenomic and chromatin regulation and the discovery of senolytic and senomorphic biomarkers.​ The Senesceome is our knwledge graph driven senescence resource.

* Cancer: Another key aspect of our work is in understanding regulatory processes underlying carcinogenesis and metastasis. Cancer is a collection of over 200 different diseases (most with multiple subtypes) characterized by abnormal cell growth, deregulated proliferation, and tissue invasion. Its evolutionary nature, the resultant heterogeneity within and between tumours, together with complex interactions with the microenvironment, lead to difficulty in treating or finding a cure. Perturbation in cellular information flow due to mutations, genetic aberrations, ploidy, metabolic reprogramming and cellular pathway and network deregulation, result in complex phenotypes that promote carcinogenesis. We are particularly focused on developing AI approaches for early detection and diagnosis of cancer.