Computational Systems Medicine
A prime challenge in the development of personalized health care strategies is to better understand how genetic alterations or chemical perturbations that individually have only a modest contribution to disease risk or treatment response may lead to strong synergistic effects on disease progression, treatment efficacy or toxicity when combined. While such epistatic or synthetic lethal interactions play a role in many diseases, including cardiometabolic diseases and cancer treatment, their systematic identification has remained difficult because of complex networks underlying genotype-phenotype relationships.
Our research group has expertise in network-centric and machine learning-based approaches to modeling and predicting complex relationships between genetic dependencies and medical phenotypes such as susceptibility to diseases and responses to treatments. We are using both 'reverse-genetic' approaches, including RNAi, CRISPR and drug screening, as well as 'forward-genetic' approaches, such as GWAS and next-generation sequencing. We believe that combining functional and genetic profiling will provide a more comprehensive network view of the mechanisms behind disease processes and enable accurate predictions of system-level phenotypic responses to genetic and chemical perturbations.
In collaboration with the Wennerberg group at FIMM, we are making use of polypharmacological effects of drugs to inhibit cancer survival pathways and sub-clones in terms of multi-target synergistic and synthetic lethal interactions. In the Individualized Systems Medicine project at FIMM/HUCH, we have implemented mathematical models to identify target addictions and other druggable vulnerabilities in individual cancer patients. With Samuel Kaski and Juho Rousu at Aalto/HIIT, we are developing machine learning models to predict drug targets and treatment responses. With the Ripatti group at FIMM, we are utilizing the Finnish health registries to study the effects of common and rare genetic variants on disease risk and clinical outcomes.
We believe that integrated modeling of the genetic and functional relationships offers a unique potential to identify key players and their interaction partners in the disease networks, as well as to suggest more effective and selective targets for personalized therapies. Toward clinical translation, such pharmacogenomic approaches will improve both the safety and efficacy of the individualized health care strategies in the future.
Open postdoc positions
The group is seeking postdoctoral researchers, with strong computational and analytical skills, and an interest in applying systems biology approaches to analysing, modeling and integration of large-scale drug testing and molecular profiling datasets. A successful candidate holds a doctoral degree in a suitable field (e.g., computational biology, bioinformatics, biomathematics, biostatistics, or computer science), strong computational and programming skills, a solid publication record and expertise in machine learning, network analysis, and/or analysis of omics datasets. Please submit your CV and cover letter as a single PDF file: email@example.com.