The aim of this project is to contribute with improved methods for analysis, integration, and visualization of biomedical big data. Recent years it has been a massive digitalization of all types of data and information in the society and the majority of all information in the world is nowadays anticipated to be digitalized. This encompasses enormous possibilities for generation of new knowledge but also puts demands on competence and tools for analysis and interpretation of big and complex data, e.g. to identify and extract patterns and information from different data sources. To meet these increasing demands of large-scale data analysis more competence, better and faster algorithms, and powerful computers are needed for execution these algorithms.
Possible to generate large-scale molecular datasets
Rapid developments in the biomedical field with new advanced molecular techniques have now made it possible to generate large-scale molecular datasets fast and to reasonable costs. The challenges are not data generation any more but rather bioinformatics competence as well as methods and algorithms for analysis and interpretation of the biomedical big data, which continuously are being generated. More competence and advanced bioinformatics methods will facilitate the use of biomedical big data for development of new knowledge in the field of Life science. Examples of biomedical big data are global transcriptional data of mRNA and miRNAs, as well as large-scale data from proteomics, metabolomics, and epigenomics etc. These datasets consist of tens of thousands of measurements collected during controlled and defined conditions.
Improved methods for analysis
The aim of this project is to contribute with improved methods for analysis, integration, and visualization of biomedical big data. The methods are applied and evaluated on datasets from studies of human pluripotent stem cells (hPSCs), with focus on differentiation and disease modeling.
The differentiation studies investigate important regulation of hPSCs during differentiation towards beta cells, hepatocytes and cardiomyocytes (in collaboration with TakaraBio). More knowledge about transcription factors and pathways in control of these processes is of high value and will facilitate optimizations of the stem cell differentiation protocols.
In addition, we also investigate and integrate data from the development of an in vitro disease model based on hPSCs that can mimic aspects of the impaired cardiac functionality in patients with diabetes mellitus (in collaboration with AstraZeneca). The purpose of these studies is to learn more about the mechanistic effects of high glucose levels on cardiomyopathy and heart failure.
All the above-mentioned studies integrate different type of omics data, and combine them using systems biology approaches to provide a more holistic view of stem cell based in vitro model systems.