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Shamith Samarajiwa

 Shamith profile picDr Shamith Samarajiwa 

 Biography | PubMed


Integrative Systems Biomedicine of Carcinogenesis

- Computational biology approaches for deciphering tumorigenesis and early carcinogenesis


My lab develops multi-disciplinary data science, data engineering and computational biology solutions to understand multi-scale biological systems involved in immunity, inflammation and cancer. We are particularly interested in the complex systems underlying tumorigenesis and early carcinogenesis, and interactions between the immune system and cancer.


Research themes:


1. Cellular Regulomes, Genomic Architecture and Regulatory Logic:

We study genomic regulatory logic in both normal cells and in cancer driven perturbations using high-throughput biomedical technologies:

  • Transcriptomics: (Microarrays, mRNA-seq), non-coding (ncRNA-seq), single cell (scRNA-seq), Nascent (Ribo-seq)
  • Gene regulation and Epigenomics: TFs & Histone mark ChIP-seq, DNA Methylation
  • Chromatin dynamics: open chromatin (ATAC-seq), long-distance interactions (Hi-C)
  • Cancer genomes: Exome and whole genome sequencing, Regulatory SNPs
  • Proteomics

This involves computational integration of different data layers to understand joint influences of interacting biological processes to comprehend how distinct regulatory layers contribute to complex phenotypes.

We develop computational and statistical methods for:

  • Integration of interrelated “omic” datasets.
  • Identification and analysis of functional regulatory elements, upstream regulators and down stream targets and their interactions.
  • Methods to understand and map the complex regulatory circuitry involved in these events.

These methods will be applied to reverse engineering of cellular regulomes that modulate specific phenotypes in carcinogenesis.


2. Data science approaches for deciphering cancer associated systems:

We utilize data-science approaches for biomedical knowledge integration and mining culminating in data driven computational modelling to de-convolute biological systems. We integrate both data and technologies, and apply novel computational methods to extract, mine and understand pertinent biology underlying phenotypes.

This includes developing computational solutions using web and data warehouse technologies, together with computational linguistics approaches, semantic-web and linked-data concepts, coupled with probabilistic, Bayesian inference and machine learning based data mining methodologies.

In order to explore and explain novel biomedical knowledge, we design bespoke data visualization solutions using technologies such as Processing, Shiny, Flex, JavaScript and HTML5.


3. Pathway Bioinformatics and Network Biology:

My group has a strong interest in computational approaches to understand and model information flow and connctivity of signalling, regulatory and metabolic pathways and networks at a systems level. We hope to decipher pathways and networks driving phenotypes influencing carcinogenesis.


4. Cancer Systems Immunology:

Together with our experimental collaborators, we use integrative and systems modelling approaches to understand the complex relationships between immune responses, especially innate and inflammatory components and early carcinogenesis.



Click to contact Dr. Shamith Samarajiwa by email.                     


Google Scholar Citations:


Selected Publications:

Phenotype specific analyses reveal distinct regulatory mechanism for chronically activated p53. Kirschner K*, Samarajiwa SA*, Cairns JM, Menon S, Pérez-Mancera PA, Tomimatsu K, Bermejo-Rodriguez C, Ito Y, Chandra T, Narita M, Lyons SK, Lynch AG, Kimura H, Ohbayashi T, Tavaré S, Narita M. PLoS Genet. 2015 Mar

Spatial coupling of mTOR and autophagy augments secretory phenotypes. 
Narita M*, Young AR*, Arakawa S, Samarajiwa SA, Nakashima T, Yoshida S, Hong S, 
Berry LS, Reichelt S, Ferreira M, Tavaré S, Inoki K, Shimizu S, Narita M. Science. 2011 May

INTERFEROME: the database of interferon regulated genes. 
Samarajiwa SA*, Forster S, Auchettl K, Hertzog PJ. Nucleic Acids Res. 2009 Jan;37:D852-7.

Soluble IFN receptor potentiates in vivo type I IFN signaling and exacerbates TLR4-mediated septic shock. Samarajiwa SA, Mangan NE, Hardy MP, Najdovska M, Dubach D, Braniff SJ, Owczarek CM, Hertzog PJ. J Immunol. 2014 May 1;192(9):4425-35. doi: 10.4049/jimmunol.1302388.

Silencing of Irf7 pathways in breast cancer cells promotes bone metastasis through immune escape. Bidwell BN, Slaney CY, Withana NP, Forster S, Cao Y, Loi S, Andrews D, Mikeska T, Mangan NE, Samarajiwa SA, de Weerd NA, Gould J, Argani P, Möller A, Smyth MJ, Anderson RL, Hertzog PJ, Parker BS. Nat Med. 2012 Aug;18(8):1224-31. doi: 10.1038/nm.2830.

Independence of repressive histone marks and chromatin compaction during senescent heterochromatic layer formation. Chandra T, Kirschner K, Thuret JY, Pope BD, Ryba T, Newman S, Ahmed K, Samarajiwa SA, Salama R, Carroll T, Stark R, Janky R, Narita M, Xue L, Chicas A, Nũnez S, Janknecht R, Hayashi-Takanaka Y, Wilson MD, Marshall A, Odom DT, Babu MM, Bazett-Jones DP, Tavaré S, Edwards PA, Lowe SW, Kimura H, Gilbert DM, Narita M. Mol Cell. 2012 Jul 27;47(2):203-14. doi: 10.1016/j.molcel.2012.06.010.

The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, Speed D, Lynch AG, Samarajiwa S, Yuan Y, Gräf S, Ha G, Haffari G, Bashashati A, Russell R, McKinney S; METABRIC Group, Langerød A, Green A, Provenzano E, Wishart G, Pinder S, Watson P, Markowetz F, Murphy L, Ellis I, Purushotham A, Børresen-Dale AL, Brenton JD, Tavaré S, Caldas C, Aparicio S. Nature. 2012 Apr 18;486(7403):346-52. doi: 10.1038/nature10983.