Computational Systems Genomics Lab


Key Laboratory for Computational Biology of CAS

CAS-MPG Partner Institute for Computational Biology

Shanghai Institute for Biological Sciences, Chinese Academy of Sciences


We develop and apply advanced statistical methodology to enable a more meaningful interpretation of large scale multi-dimensional cancer-omic data. Specifically, we are applying tools from network physics, graph theory, Bayesian statistics, signal processing and machine learning to help address some of the outstanding challenges in the cancer genomics and epigenomics fields. We are particularly interested in using the language of networks and statistical mechanics to help tame the complexity of multi-dimensional medical genomic data, and to unravel the systems biology principles underlying complex genetic diseases like cancer.


Latest Publication

Teschendorff AE, Enver T. Single-cell entropy for accurate estimation of differentiation potency from a cell's transcriptome. Nat Commun. 2017 Jun 1;8:15599. doi: 10.1038/ncomms15599. PubMed PMID: 28569836..

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Selected Publications

Role of DNA methylation and epigenetic silencing of HAND2 in endometrial cancer development

HAND2 methylation is a common and crucial molecular alteration in endometrial cancer that could potentially be employed as a biomarker for early detection of endometrial cancer and as a predictor of treatment response. The true clinical utility of HAND2 DNA methylation, however, requires further validation in prospective studies.[PDF]

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Cellular network entropy as the energy potential in Waddington's differentiation landscape

In disease, network entropy predicts a higher level of cellular plasticity in cancer stem cell populations compared to ordinary cancer cells. Importantly, network entropy also allows identification of key differentiation pathways. In summary, network entropy provides a quantitative measure of a cell’s undifferentiated state, defining its elevation in Waddington’s landscape.[PDF]

Distinctive topology of age-associated epigenetic drift in the human interactome

We show that these genes synergize topologically with disease and longevity genes, forming unexpectedly large network communities. Thus, these results point toward a potentially distinct mechanistic and biological role of DNA methylation in dictating the complex aging and disease phenotypes.[PDF]

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A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control

we present a novel functional supervised algorithm, called Functional Epigenetic Modules (FEM), for the integrative analysis of Infinium 450k DNA methylation and matched or unmatched gene expression data. The algorithm identifies gene modules of coordinated differential methylation and differential expression in the context of a human interactome.[PDF]

A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data

We propose a novel model-based intra-array normalization strategy for 450 k data, called BMIQ (Beta MIxture Quantile dilation), to adjust the beta-values of type2 design probes into a statistical distribution characteristic of type1 probes.[PDF]

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Epigenetic variability in cells of normal cytology is associated with the risk of future morphological transformation

We demonstrate that the risk of neoplastic transformation can be predicted from DNA methylation profiles in the morphologically normal cell of origin of an epithelial cancer. Having profiled only 0.1% of CpGs in the human genome, studies of wider coverage are likely to yield improved predictive and diagnostic models with the accuracy needed for clinical application.[PDF]

Research Focus

Cancer System-Omics and Network Physics

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Exploring novel tools and concepts from graph theory and network physics to help us elucidate the system-omic principles underlying the cancer phenotype.

Epigenomics of Ageing, Stem Cells and Cancer

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Involved in a large number of data-driven projects concerning the epigenomics of ageing and cancer, in close collaboration with biologists and clinicians at UCL, London, and elsewhere.

Statistical analysis of large scale omic data

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Our aim is to develop novel statistical methods to help address these challenges and thus enable a more meaningful interpretation of such data.

Molecular taxonomy of cancer and personalized medicine

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Currently, we are exploring a perturbation signature approach in the context of large multi-dimensional cancer genomic data and matched drug sensitivity screens, with two goals in mind.

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