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Computational medicine

Computational medicine

The Unit is a home for interdisciplinary (theory, experiments, computation) research where we develop, validate, and apply quantitative approaches on fundamental problems of living systems with application to the clinic and theory.

The Unit is ranked as outstanding (ERA 2012), production rate averaging 20 publications annually the last 5-10 years & founding of two BioIT companies. Extensive teaching (bioinformatics, network biology, systems medicine) – PhD courses, local/national, and international workshops & several international summer schools between 2002 and 2016 (now closed).

RESEARCH: On the theory side we develop new algorithms and machine intelligence approaches for essentially extracting the fundamental equations encoding causality directly from data (ERC consolidator, VR-NT). Fundamental work on algorithmic information theory (VR-NT Foass) and non-linear dynamical systems modelling aims at better tools for understanding and dealing, i.e. reprogramming and modelling, complex networks.  On the applied technological side we believe that evolved solutions in nature can provide insights on how to design new powerful intelligent hybrid computational systems.

To ground this work we perform molecular analysis of cells during health and disease, including a large number of clinical collaborations. Current major medical applications include Multiple Sclerosis with the CMM MS team (AZ-SciLife grant, Hjärnfonden, ALF, & H2020), Ageing & Frailty (FP7),  and Cancer Stem Cell biology with Gondor, Olsson et al (KAW 2018-2022) at KI.

In our work we mainly perform single cell genomics of cells, utilizing  cell culture experiments, (primary cells, clinical samples). Techniques include RNAseq, CyTOF, ATACseq, proteomics, and PCR. This data generation, in effect reading contextual parts of cells, is coupled with technologies for writing (perturbing) such as shRNAi, CRISPR/Cas9, in conjunction with live cell imaging for precision phenotyping. To make sense of massive parallell data we develop and use bioinformatics analysis, data-fusion techniques, mathematical modelling, theory, construction of data-bases, and design of software (R-scripts, GitHub, web-platforms).

Please visit the compmed website.


Group leader

Jesper Tegnér


Job title





Bioinformatics, Computer simulations, Genomics, Immunology


Cancer, Multiple sclerosis


Aging, Gene expression