Event

Causal Analysis of Biomedical Data – Prof. Marieke Kuijjer

This is a hybrid event. Join in person at the LCSB, or tune in remotely via Webex.

Modelling gene regulatory network rewiring in cancer

The development of high-throughput omics techniques has advanced cancer research. However, while large-scale data have contributed to our understanding of cancer development, heterogeneity, and progression, for most cancer types, the clinical utility of omics profiling is still limited. This is in part because clinical samples are most often profiled as “bulk” tissues, containing a mixture of cell types, rendering it difficult to extract signals derived from cancer cells versus immune cells and other cells in the tumor microenvironment. Moreover, clinical analysis of omics data is generally focused on protein-coding genes alone, ignoring the non-coding genome. However, this part of our genome contains regulatory regions that drive and influence which genes are expressed, and to what extent, and thus can be important in driving and sustaining cancer. In this talk, I will showcase how my group’s research, instead, tries to embrace the complexity of cancer genomes and cancer data by modeling and analyzing patient- and cell type-specific, genome-wide regulatory networks. I will present various integrative approaches to model such networks, as well as methodologies and examples to get insights into how network rewiring may drive cancer progression and heterogeneity.


About the speaker

Marieke Kuijjer is Group Leader of the Computational Biology and Systems Medicine group at the Center for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, Norway. She received her PhD from Leiden University Medical Center and postdoctoral training at the Dana-Farber Cancer Institute and Harvard Chan School of Public Health. Marieke’s research interests include the development of network-based methods to integrate and analyse cancer data.

Marieke Kuijjer Profile

The Causal Analysis of Biomedical Data Lecture Series is supported by the Luxembourg National Research Fund (FNR) RESCOM Program.

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