The Biomathematics Research Group works in mathematical ecology. We put a large emphasis on mechanistic modelling, i.e., on deriving models from the underlying biological processes. We use our models to understand how ecological interactions drive evolutionary changes: Members of the group are among the founders of adaptive dynamics, one of the main mathematical frameworks to study evolution by natural selection.
The Biomathematics study track offers exceptionally extensive education in the area of mathematical ecology and evolution. Our focus on ecology provides fast access to the practice of modelling and provides transferable skills in model construction and analysis, which may then be used also in other areas of biomathematics and beyond. Taking this study track as the main line of studies is especially suitable for students who wish to continue in academia. To learn the basics of modelling is however of central importance for everyone: Data make sense only in the light of models. Models provide creative insight and understanding as well as are at the core of modern Bayesian statistics.
Biostatistics develops statistical methods for the life sciences, focusing on how to make principled statistical inference in real-life situations. The methods include, for example, variable selection, statistical clustering, large-scale inference and dimension reduction. Both Bayesian and other approaches are studied. Our research projects include various biomedical disciplines such as epidemiology of complex diseases, genetic association studies, statistics in medicine and population genetics. The courses of the Biostatistics study track closely connect to this research. We collaborate, for example, with the Institute for Molecular Medicine Finland (FIMM), the Faculty of Medicine at University of Helsinki and the Finnish Institute for Health and Welfare.
The Ecological Informatics study track connects to the Environmental and Ecological Statistics Group and to the Data Science and Evolution group at the Faculty of Science. Researchers in these groups have deep background in mathematics, statistics and computer science as well as long experience in applying these methods to ecology and environmental sciences. Their research spans a wide range, from theoretical and computational statistics and machine learning to key questions in ecology and to practical problems of environmental management and risk assessment.
As a student in the Ecological Informatics study track you will also take courses from the Master's Programme in Ecology and Evolutionary Biology at the Faculty of Biology and Environmental Sciences. This gives you a direct link also to the world class research groups in ecology and evolutionary biology. True to the interdisciplinary nature of this study track, you can do your Master's thesis at either of the two Faculties.
Biology and medicine are undergoing a transformation due to vast amounts of data generated by new technologies such as next generation sequencing and high-throughput phenotyping. This so called Big Data is creating great opportunities across fields ranging from personalised cancer therapies to detailed understanding of the evolution of drug resistance. However, data alone are not sufficient to make sustained scientific or clinical progress. Powerful algorithms as well as computational methods are essential to translate these data into scientific discoveries and clinically useful information.
The Bioinformatics and Systems Medicine study track prepares students to the new era of biomedicine, where innovative computational approaches are required to interpret molecular and clinical data from patients or from disease-causing pathogens. The courses of this study track educate you to be an expert who can turn biomedical questions into appropriate challenges for computational data analysis and solve them.
The core of this study track connects directly to three research groups. The Bioinformatics and Evolution research group at the Faculty of Biological and Environmental Sciences and the Faculty of Science aims at understanding and predicting microbial evolution, including drug resistance, combining experimental evolution and data science. The research group Systems Biology of Drug Resistance in Cancer of the Faculty of Medicine develops machine learning methods to integrate molecular data with clinical information in pursuit of personalised medicine to combat drug resistant cell lines of cancers. The Algorithmic Bioinformatics group aims to provide a solid foundation for reliable and scalable methods to enable new breakthroughs based on high-throughput sequencing data.
The courses offered in this study track centre on algorithms and machine learning approaches for analysing molecular data and computational approaches that allow interpretation of high-throughput biomedical data. The curriculum also includes general algorithm and machine learning studies offered by the Master's Programmes in Computer Science, in Data Science, and in Genetics and Molecular Biosciences.