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Shamith Samarajiwa

 Shamith profile picDr Shamith Samarajiwa 

 Biography | PubMed

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Integrative Systems Biomedicine of Carcinogenesis

- Computational & Systems Biology approaches for battling Cancer



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 carcinogenesis, and interactions between the immune system and cancer.


Research themes:


1. Cellular Regulomes, Genomic Architecture and Regulatory Logic:
Networks of p53 direct targets in Apoptosis and Senescence Samarajiwa & Kirschner et al. PLoS Genet. 2015 Mar; 11(3): e1005053/CC By 4.0

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), Translatomes (Ribo-seq)
  • Gene regulation and Epigenomics: TFs & Histone mark ChIP-seq, DNA Methylation
  • 3D Genomes: open chromatin (ATAC-seq), long-distance interactions (Hi-C and Capture 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 “omic” datasets to extract meaningful biology.
  • Analysis of functional regulatory elements, upstream regulators and downstream targets and their interactions.
  • Map the complex regulatory circuitry involved in these events for predictive modeling.


CLICK HERE to visit our map of TP53 targets in Apoptosis 

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


 2. Data science for Cancer Biomedicine:

  • Data Integration and Visualization  

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, develop and apply novel computational methods to extract, mine and understand hidden biology underlying carcinogenic phenotypes.

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.

  •  Artificial Intelligence for Precision Genomics

We’re developing AI methods and integrative software systems for personalised medicine. This includes developing computational solutions using web and data technologies, together with computational linguistics (Text Mining and NLP) approaches, semantic-web and linked-data concepts, coupled with probabilistic, Bayesian inference and machine learning based data mining methodologies.


3. Pathway Bioinformatics and Network Biology:

My group has a strong interest in computational approaches to understand and model information flow and connectivity of signalling, regulatory and metabolic pathways and networks at a systems level. We are engineering network science and graph theoretic methods to decipher pathways and networks that drive phenotypes influencing carcinogenesis.


4.  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 in 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 20;332(6032):966-70.

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.

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.

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.

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.