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Olli Yli-Harja

Tampere University of Technology Department of Signal Processing


During the years 1988-1992 Olli Yli-Harja was a research scientist at the Technical Research Centre of Finland, in 1993-1996 he was a lecturer in computer science in Helsinki University of Technology, and in 1997-1998 an Assistant Professor in University of Helsinki. In 1998 he was a research fellow at East Anglia University, Norwich, UK. In 1998-2001 he was a Senior researcher at the Institute of Signal Processing in Tampere University of Technology. In 2005, he was a visiting scientist in the University of Texas M. D. Anderson Cancer Center, Houston. Currently he is a Professor in the Department of Signal Processing in Tampere University of Technology, Finland and leads the Computational Systems Biology research group.

Areas of Research:

Dr. Yli-Harja's research interests focus on three areas in computational systems biology:

Development of computational tools and software for systems biology utilizing advanced methods of signal processing and statistics

This includes development and validation of software tools for systems biology with focus on image and data analysis, quantitative simulation of modern biological measurement techniques. We also develop methods for statistical inference, large scale data integration, visualization, and data mining to help interpretation of biological data. This work facilitates the use of large scale data in applied and basic research in biosciences.

D. Charlebois, A. S. Ribeiro, A. Lehmussola, J. Lloyd-Price, O. Yli-Harja, and S. A. Kauffman. Effects of microarray noise on inference efficiency of a stochastic model of gene networks. WSEAS Transactions in Biology, 2008.

A. Lehmussola, P. Ruusuvuori, J. Selinummi, T. Rajala, and O.Yli-Harja, "Synthetic images of high-throughput microscopy for validation of image analysis methods," Proceedings of the IEEE, 96(8): 1348-1360, 2008.

T. Aho, O.-P. Smolander, J. Niemi, and O. Yli-Harja. RMBNToolbox: random models for biochemical networks. BMC Systems Biology, 1:22, 2007.

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

A. Lehmussola, P. Ruusuvuori, and O. Yli-Harja. Evaluating the performance of microarray segmentation algorithms. Bioinformatics, 22(23):2910-2917, Dec 2006.

M. Nykter, T. Aho, M. Ahdesmäki, P. Ruusuvuori, A. Lehmussola, and O. Yli-Harja. Simulation of microarray data with realistic characteristics. BMC Bioinformatics, 7:349, Jun 2006.

H. Lähdesmäki, S. Hautaniemi, I. Shmulevich, and O. Yli-Harja. Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks. Signal Processing, 86(4):814-834, Apr 2006.

Development of computational tools and software for systems biology utilizing advanced methods of signal processing and statistics
Cross-disciplinary applied research together with several partners in biosciences

We have a range of applications in cancer research, stem cell research, microbe-based biotechnology, yeast metabolics, transcriptional regulation, and signaling networks as well as electro-physiological modeling within mammalian cells. We aim at clinical applications related to, for example, understanding the cellular level signaling in Alzheimer's disease, and utilization clinical information using natural language processing. We also develop experimental facilities for electro-physiological measurements of mammalian neural networks, and cell image processing in an integrated microfluidic platform.

A. Saarinen, M.-L. Linne, and O. Yli-Harja. Stochastic differential equation model for cerebellar granule cell excitability. PLoS Computational Biology, 4(2):e1000004, Feb 2008.

Nikhil, A. Visa, O. Yli-Harja, C.-Y. Lin, and J. A. Puhakka. Application of the clustering hybrid regression approach to model xylose-based fermentative hydrogen production. Energy and Fuels, 22(1):128-133, 2008.

A. Saarinen, M.-L. Linne, and O. Yli-Harja. Modeling single neuron behavior using stochastic differential equations. Neurocomputing, 69(10-12):1091-1096, May 2006.

M. Nykter, K. K. Hunt, R. E. Pollock, A. K. El-Naggar, E. Taylor, I. Shmulevich, O. Yli-Harja, and W. Zhang. Unsupervised analysis uncovers changes in histopathologic diagnosis in supervised genomic studies. Technology in Cancer Research and Treatment, 5(2):177-182, Apr 2006.

T. Jaatinen, H. Hemmoranta, S. Hautaniemi, J. Niemi, D. Nicorici, J. Laine, O. Yli-Harja, and J. Partanen. Global gene expression profile of human cord blood-derived CD133+ cells. Stem Cells, 24(3):631-641, Mar 2006.

A. Niemistö, V. Dunmire, O. Yli-Harja, W. Zhang, and I. Shmulevich. Analysis of angiogenesis using in vitro experiments and stochastic growth models. Physical Review E, 72, 062902, Dec 2005.

J. Selinummi, J. Seppälä, O. Yli-Harja, and J. A. Puhakka. Software for quantification of labeled bacteria from digital microscope images by automated image analysis. BioTechniques, 39(6):859-863, Dec 2005.

A. Pettinen, T. Aho, O.-P. Smolander, T. Manninen, A. Saarinen, K.-L. Taattola, O. Yli-Harja, and M.-L. Linne. Simulation tools for biochemical networks: evaluation of performance and usability. Bioinformatics, 21(3):357-363, Sep 2005.

H. Lähdesmäki, I. Shmulevich, V. Dunmire, O. Yli-Harja, and W. Zhang. In silico microdissection of microarray data from heterogeneous cell populations. BMC Bioinformatics, 6:54, 2005.

H. Lähdesmäki, X. Hao, B. Sun, L. Hu, O. Yli-Harja, I. Shmulevich, and W. Zhang. Distinguishing key biological pathways between primary breast cancers and their lymph node metastases by gene function-based clustering analysis. International Journal of Oncology, 24(6):1589-1596, Jun 2004.

Cross-disciplinary applied research together with several partners in biosciences:
Basic research in theoretical biology

This includes mathematical modeling of emergent properties of complex biological systems and investigation of information processing and structure-dynamics-relationship within them. We also develop quantitative simulation methods of biological processes using advanced mathematical models.

M. Nykter, N. D. Price, M. Aldana, S. A. Ramsey, S. A. Kauffman, L. Hood, O. Yli-Harja, and I. Shmulevich. Gene expression dynamics in the macrophage exhibit criticality. Proceedings of the National Academy of Sciences USA, 105(6):1897-1900, 2008.

M. Nykter, N. D. Price, A. Larjo, T. Aho, S. A. Kauffman, O. Yli-Harja, and I. Shmulevich. Critical networks exhibit maximal information diversity in structuredynamics relationships. Physical Review Letters, 100, 058702, 2008.

P. Rämö, S. Kauffman, J. Kesseli, and O. Yli-Harja. Measures for information propagation in Boolean networks. Physica D, 227(1):100-104, 2007.

J. Kesseli, P. Rämö, and O. Yli-Harja. Iterated maps for annealed Boolean networks. Physical Review E, 74, 046104, May 2006.

P. Rämö, J. Kesseli, and O. Yli-Harja. Perturbation avalanches and criticality in gene regulatory networks. Journal of Theoretical Biology, 242:164-170, May 2006.

P. Rämö, J. Kesseli, and O. Yli-Harja. Stability of functions in Boolean models of gene regulatory networks. Chaos, 15, 034101, 2005.

Detailed information in CSB-group research pages


We are building software tools for systems biology and aim at developing systematic methodology for their validation with regard not only to programming errors, but also usability and accuracy in the intended biological context. We have developed several useful tools that help solving problems in interpretation of biological measurements, image processing, and network modeling. These tools are freely available on our web-pages.

Detailed information in CSB-group software tools pages

Key collaborations within ISB:

Shmulevich Lab - Software tools for systems biology, network models for theoretical biology

Aitchison Lab - Quantitative proteomic characterization of transcriptional regulatory complexes that control peroxisomal gene expression, image analysis for genome-wide analysis of signaling networks regulating peroxisome biogenesis in yeast

Dudley Lab - Image analysis for studies of p-body formation in budding yeast