Computational systems biology

Developing predictive models for precision medicine

At IBM Research – Zurich, we develop novel approaches to analyze different molecular levels of high-throughput data. From single-cell to cell population-averaged data (proteomics, transcriptomics), we aim to integrate multiple layers of genome-scale information. This, in combination with clinical information and prior knowledge through literature mining, enables us to understand molecular mechanisms and explore applications to personalised medicine.

Our main research projects include, but not are limited to, studying cell-to-cell heterogeneity, integrative multi-omics analysis, dynamic network inference and robust biomarker discovery, most of which are applied in the case of cancer.

We gratefully acknowledge our numerous collaborations with university hospitals, research institutes and universities that work alongside our team in many of our projects.

Cell heterogeneity

Tumor hetero­geneity

Identifying the sources of cell hetero­geneity is crucial to develop­ing effective disease man­age­ment strat­egies.


Multimodal data integration

Developing a predictive computa­tional tech­nology to exploit and inte­grate multiple molecular and clinical data.


Signaling network recon­struc­tion

Reconstructing the wiring dia­gram of cell sig­nal­ing net­works by sta­tistical means.


Biomarker dis­covery

Constructing accurate models to pre­dict macro­scopic molecular para­meters.


Molecular finger­prints of cancer

Developing a novel computational frame­work to con­struct a pheno­type–geno­type associa­tion net­work for prostate cancer.

Ask the expert


Maria Rodriguez Martinez

María Rodríguez Martínez

IBM Research scientist

We gratefully acknowledge generous funding from SNF and EU logos