Computational systems biology

Group Leader

Prof. Harri Lähdesmäki
Aalto University School of Science
Department of Information and Computer Science

Contact Information

Aalto University School of Science
Department of Information and Computer Science
PO Box 15400
FI-00076 Aalto
harri.lahdesmaki [at]

Description of the Project

We use computational techniques to model and understand molecular regulatory mechanisms and their role in health and disease. We focus on developing statistical modeling and machine learning methods to understand transcriptional, post‐transcriptional and epigenetic regulatory mechanisms, protein signaling pathways, and effects of mutations on regulatory mechanisms. We also develop methods for biological sequence analysis, combining heterogeneous biological information sources and analyzing high-throughput measurement data, such as deep-sequencing and microarray measurements. Research projects are carried out in close collaboration with experimental groups, and we collaborate on molecular immunology, stem cell, cancer and type 1 diabetes systems biology research projects. Ongoing research topics include:

  • Modeling gene expression regulation by transcription factors, chromatin structure and epigenetic modifications
  • Modeling molecular dynamics; signaling pathways and gene regulatory networks
  • Alternative splicing detection
  • Molecular systems immunology
  • Statistical modeling for high-throughput data
  • Systems approaches in Type 1 diabetes and cancer research
  • Machine learning and computational statistics, with applications to molecular biology

More information:


Academy of Finland, EU FP7, EraSysBio+, Emil Aaltonen Foundation, FICS and TISE graduate schools.

Recent Publications

Erkkilä, T., Lehmusvaara, S., Ruusuvuori, P., Visakorpi, T., Shmulevich, I. and Lähdesmäki, H., Probabilistic analysis of gene expression measurements from heterogeneous tissues,
Bioinformatics, Vol. 26, No. 20, pp. 2571-2577, 2010.

Elo, L. L., Järvenpää, H., Tuomela, S., Raghav, S., Ahlfors, H., Laurila, K., Gupta, B., Lund, R. J., Tahvanainen, J., Hawkins, D., Oresic, M., Lähdesmäki, H., Rasool, O., Rao, K. V., Aittokallio, T. and Lahesmaa, R., Genome-wide Profiling of Interleukin-4 and STAT6 Transcription Factor Regulation of Human Th2 Cell Programming, Immunity, Vol. 32, No. 6, pp. 727-862, 2010.

Aho, T., Almusa, H., Matilainen, J., Larjo, A., Ruusuvuori, P., Aho, K.-L., Wilhelm, T., Lähdesmäki, H., Beyer, A., Harju, M., Chowdhury, S., Leinonen, K, Roos, C. and Yli-Harja, O., Reconstruction and validation of RefRec: a global model for the yeast molecular interaction network, PLoS ONE, 5(5):e10662, 2010.

Dai, X. and Lähdesmäki, H., Novel data fusion method and exploration of multiple information sources for transcription factor target gene prediction, EURASIP Journal on Advances in Signal Processing, Special issue on Genomic Signal Processing, Vol. 2010, Article ID 235795, 2010.

Laurila, K. and Lähdesmäki, H., A protein-protein interaction guided method for competitive transcription factor binding improves target predictions, Nucleic Acids Research, Vol. 37, No. 22, e146, 2009.

Äijö, T. and Lähdesmäki, H., Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics, Bioinformatics, Vol. 25, No. 22, pp. 2937-2944, 2009.

Laurila, K. and Lähdesmäki, H., Systematic analysis of disease-related regulatory mutation classes reveals distinct effects on transcription factor binding, In Silico Biology, Vol. 9, 0018, 2009.

Dai, X., Erkkilä, T., Yli-Harja, O. and Lähdesmäki, H., A joint mixture model for clustering genes from Gaussian and beta distributed data, BMC Bioinformatics 10:165, 2009.

Dai, X., Lähdesmäki, H. and Yli-Harja, O., A stratified beta-Gaussian mixture model for clustering genes with multiple data sources, International Journal on Advances in Life Sciences, Vol. 1, No. 1, pp. 14-25, 2009.

Nykter, M., Lähdesmäki, H., Rust, A. G., Thorsson, V. and Shmulevich, I., A data integration framework for prediction of transcription factor targets: a BCL6 case study, Annals of the New York Academy of Sciences, Vol. 1158, pp. 205-214, 2009.

Lähdesmäki, H., Rust, A. G. and Shmulevich, I., Probabilistic inference of transcription factor binding from multiple data sources, PLoS ONE, Vol. 3, No. 3, e1820, 2008.

Lähdesmäki, H. and Shmulevich, I., Learning the structure of dynamic Bayesian networks from time series and steady state measurements, Machine Learning, Vol. 71, No. 2-3, pp. 185-217, 2008.

Liu, W., Lähdesmäki, H., Dougherty, E. R. and Shmulevich, I., Inference of Boolean networks using sensitivity regularization, EURASIP Journal on Bioinformatics and Systems Biology, Vol. 2008, Article ID 780541, 12 pages, 2008.

Ahdesmäki, M., Lähdesmäki, H., Gracey, A., Shmulevich, I. and Yli-Harja O., Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data, BMC Bioinformatics, 8:233, 2007.