Genetic interactions and network medicine

Research overview

The FIMM-EMBL research group focuses on developing and applying integrated computational-experimental approaches to address biomedical questions, such as how genes function as interaction networks to carry out and regulate cellular processes, how perturbations in these networks contribute to complex traits, such as human diseases, and where in the disease network should one target to inhibit disease phenotypes, such as tumor growth.

Research strategy

We are developing systems biology modeling approaches to revealing molecular mechanisms behind disease processes and system-level responses to genetic and chemical perturbations. A particular emphasis is on understanding how genetic mutations or other perturbations that have no discernible individual effect on the disease phenotypes may result in strong synergistic effects leading to disease when combined. While such epistatic genetic interactions are prevalent and known to be involved in many disease processes, such as in the development of cancers and cardiovascular diseases, they have remained extremely difficult to identify on a global scale because of the vast number of potential interaction partners and non-linear genotype-phenotype relationships.

The group has expertise in network-centric modeling frameworks for predicting and analyzing genetic interactions in the context of large-scale experiments, including ‘reverse-genetic’ approaches, in which the function of genes are systematically perturbed using e.g. high-throughput RNAi or compound screening; and ‘forward-genetic’ approaches, such as genome-wide association or next-generation sequencing studies, where naturally occurring mutations pinpoint the loci that are associated with the trait of interest. An integrative analysis and visualization of the physical and genetic relationships has the potential to identify key players and their interaction partners in disease networks, as well as to suggest selective targets for personalized therapies.

Research projects

Modeling and prediction of synthetic lethal interactions in cancers

We are applying a top-down modeling approach to predict synthetic lethal partners of individual cancer-causing mutations, optimal targeting of which holds a great promise for being a highly specific and selective means to kill the cancer cells without severe side-effects to normal cells.

Epistatic interactions among genetic variants in cardiometabolic traits
We are developing and exploiting computationally efficient statistical and machine learning strategies for mining interactions among panels of genetic variants, environmental factors, and underlying biological pathways, which are most predictive of the increased disease risk.

Selected publications

Heiskanen MA, Aittokallio T. Mining high-throughput screens for cancer drug targets: lessons from yeast chemical-genomic profiling and synthetic lethality. Focus article, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery (in press).
Tuikkala J, Vähämaa H, Salmela P, Nevalainen OS, Aittokallio T. A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization. BioData Mining (in press).
Corander J, Aittokallio T, Ripatti S, Samuel K. The rocky road to personalized medicine: computational and statistical challenges. Commentary article, Personalized Medicine 2012; 9: 109–114.
Elo LL, Kallio A, Laajala TD, Hawkins RD, Korpelainen E, Aittokallio T. Optimized detection of transcription factor-binding sites in ChIP-seq experiments. Nucleic Acids Research 2012; 40: e1.
Lindén RO, Eronen VP, Aittokallio T. Quantitative maps of genetic interactions in yeast: comparative evaluation and integrative analysis. BMC Systems Biology 2011; 5: 45.
Lietzén N, Öhman T, Rintahaka J, Julkunen I, Aittokallio T, Matikainen S, Nyman TA. Quantitative subcellular proteome and secretome profiling of influenza A virus-infected human primary macrophages. PLoS Pathogens 2011; 7: e1001340.
Okser S, Lehtimäki T, Elo LL, Mononen N, Peltonen N, Kähönen M, Juonala M, Fan YM, Hernesniemi JA, Laitinen T, Lyytikäinen LP, Rontu R, Eklund C, Hutri-Kähönen N, Taittonen L, Hurme M, Viikari JS, Raitakari OT, Aittokallio T. Genetic variants and their interactions in the prediction of increased pre-clinical carotid atherosclerosis: the cardiovascular risk in young Finns study. PLoS Genetics 2010; 6: e1001146.

Group members

Agnieszka Szwajda	PhD student	agnieszka.szwajda@helsinki.fi
Bhagwan Yadav	PhD student	bhagwan.yadav@helsinki.fi
Jing Tang	Senior researcher	jing.tang@helsinki.fi
Petteri Hintsanen	Postdoctoral researcher	petteri.hintsanen@helsinki.fi
Tero Aittokallio	Group leader	tero.aittokallio@helsinki.fi

The research is done in collaboration with the Turku Biomathematics Research Group.

About Tero Aittokallio

Tero Aittokallio received his PhD in Applied Mathematics from the University of Turku in 2001, under supervision of Prof. Mats Gyllenberg. He then went to a post-doctoral training (2006-2007) in the Systems Biology Lab at the Institut Pasteur, with Dr. Benno Schwikowski, where he focused on network biology applications using high-throughput experimental assays and network analysis tools such as Cytoscape. Dr. Aittokallio then launched his independent career as a principal investigator in the Turku Biomathematics Research Group in 2007, and received a five-year appointment as an Academy Research Fellow. A particular emphasis has been on modeling non-linear genotype-phenotype relationships and genetic (or epistatic) interactions in model organisms such as yeast.