Molecular Medicine

Systems Biology

Mission

Systems biology is a new approach to studying complex biological phenomena. It seeks to integrate high-throughput biological studies to understand how biological systems function. The ultimate goal is to develop a model of the system by studying the relationships and interactions between components of the system. A system can be defined as a disease state and genes involved in the development of that state. Systems biology will increase the power of genetic studies aimed at identification of genes causing multigenic diseases, and pave the way towards application of targeted and personalized medicine.

The aim of our group is to find new disease genes, to understand the mechanisms behind diseases and to create new methods and technologies to speed up the search for disease genes and the study of their function. Our main disease model is cancer, and the current lines of work in the laboratory aim to determine the mechanisms by which oncogenes and growth factors control cell proliferation.

Research

Systems biology is applied to biological problems relevant to human diseases by first identification and characterization of the components affecting pathological and physiological systems. Components are identified using high-throughput technologies, such as RNA interference, cDNA expression screening and chemical genomics, and by systematic integration of obtained data with existing knowledge. Subsequent computational modeling will allow prediction of the effect of mutational or pharmacological perturbations to the system.

Cancer is a multigenic disease which can be caused by mutations in > 300 genes. These mutations cause cancer cells to escape from the mechanisms normally regulating proliferation and differentiation. Despite this genetic heterogeneity, all cancers share common phenotypes (e.g. uncontrolled cell division). To understand cancer, we must identify both the genes causing cancer and the genes controlling the common phenotypes, and subsequently understand how the cancer genes control the activity of the cell cycle genes.

For this purpose, we have in collaboration with Kimmo Palin and Esko Ukkonen (UH Dept. of Computer Science) developed computational methods which can identify genes whose expression is controlled by transcription factors linked to cancer. These computational methods allow determination of expression patterns of genes from genomic sequences. We are currently in the process of developing this technology further to predict which variations of sequence (e.g. SNPs) between individuals result in changes in gene expression, and are therefore more likely to cause disease states, including cancer.

To determine which of the predicted target genes affect cell proliferation, we are combining the computational analyses with experimental results from genome-wide RNA-interference analyses identifying genes regulating cell proliferation and cell size. We are currently extending these studies to systematically analyze the genetic and biochemical interactions between the identified genes and proteins.

Research Group
Jussi Taipale, Mikael Björklund, Martin Bonke, Lin Feng, Outi Hallikas, Arttu Jolma, Teemu Kivioja, Song-Ping Li, Ritva Nurmi, Maria Sokolova, Minna Taipale, Mikko Turunen, Gonghong Wei, Jian Yan

	Mikael Björklund, Ph.D. Postdoc		Martin Bonke, Ph.D. Postdoc
	Outi Hallikas, M.Sc. Ph.D. student		Song-Ping Li, Ph.D. Postdoc
	Sini Miettinen Laboratory technician (Univ. of Helsinki)		Ritva Nurmi Laboratory technician (KTL)
	Minna Taipale, Ph.D. Postdoc

Recent Publications

Bjorklund M, Taipale M, Varjosalo M, Saharinen J, Lahdenpera J, Taipale J. Identification of pathways regulating cell size and cell-cycle progression by RNAi. Nature. 2006 Feb 23;439(7079):1009-13. PubMed
Echeverri CJ, Beachy PA, Baum B, Boutros M, Buchholz F, Chanda SK, Downward J, Ellenberg J, Fraser AG, Hacohen N, Hahn WC, Jackson AL, Kiger A, Linsley PS, Lum L, Ma Y, Mathey-Prevot B, Root DE, Sabatini DM, Taipale J, Perrimon N, Bernards R. (2006) Minimazing the risk of reporting false positives in large-scale RNAi screens. Nat Methods 3:10;777-9.
Hallikas O, Palin K, Sinjushina N, Rautiainen R, Partanen J, Ukkonen E, Taipale J. Genome-wide prediction of mammalian enhancers based on analysis of transcription-factor binding affinity. Cell. 2006 Jan 13;124(1):47-59. PubMed
Hallikas O, Taipale J. (2006) High-throughput assay for determining specificity and affinity of protein-DNA binding interactions. Nat Protoc 1:1;215-22.
Palin K, Taipale J, Ukkonen E. (2006) Locating potential enchancer elements by comparative genomics using the EEL software. Nature Protocols 1:368-74.
Varjosalo M, Li SP, Taipale J. Divergence of Hedgehog Signal Transduction Mechanism between Drosophila and Mammals. Dev Cell. 2006 Feb;10(2):177-86. PubMed
Varjosalo M, Taipale J. (2007) Hedgehog signaling. J Cell Sci 120:3-6.
Varjosalo M, Björklund M, Cheng F, Syvänen H, Kivioja T, Kilpinen S, Sun Z, Kallioniemi O, Stunnenberg HG, He WW, Ojala P, Taipale J. (2008). Application of active and kinase-deficient kinome collection for identification of kinases regulating hedgehog signaling. Cell, 133(3):537-48.
Varjosalo M, Taipale J. (2008) Hedgehog: functions and mechanisms. Genes Dev. 22:2454-72.

Additional Information

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Professor Jussi Taipale (email: firstname.lastname@ktl.fi)

Links

University of Helsinki

Biocentrum Helsinki

Regulatory Genomics