The complex interplay between proteins, genes, metabolites, and other components make up the
functional underpinnings of a living cell. The goal of this project is to develop computational
algorithms and methods that use high-throughput biological data to build comprehensive networks
of how genes and their producs interact with each other. This is achieved by systems biology
approaches such as building global association networks of functional coupling (see
FunCoup.sbc.su.se/) and Gene Regulatory Network inference on perturbation-based gene
expression data. There is an overlap between these types of networks which will be exploited in
order to improve the quality and usefulness of both.
Methods include regression models, Bayesian integration, various statistical analyses, and in-house
developed modelling techniques. In FunCoup, heterogeneous publicly available high-throughput
data sources are combined to predict functional coupling between proteins in order to build global
networks that model pathways and interaction cascades. The project aims to expand FunCoup to
also use physical regulatory evidence such as ChIP-Seq or ATAC-seq to infer regulatory links, and
enzymatic activities to infer directed links. These can serve as a prior when inferring gene
regulatory networks in order to limit the search space. We are developing new algorithms for
perturbation-based gene regulatory network inference with a focus on improving reliability and
accuracy when using real data. The project involves programming, data analysis, benchmarking,
and modelling, as well as application of the developed methods to experimental data generated by
the group.
Science for Life Laboratory in Stockholm, Sweden, is a strong research environment
for large-scale life science research, and a joint physical center for a number of computational and
life science groups at Stockholm University, KTH, and Karolinska Institutet. The research project
will be supervised by Professor Erik Sonnhammer ( sonnhammer.org/).
The successful candidate must be highly motivated and have an M.Sc. in bioinformatics or related
field, and knowledge of molecular biology. Alternatively, an M.Sc. in molecular biology or related
field and at least 1 year of documented practical experience in bioinformatics research and
programming. Demonstrable familiarity with sequence and molecular data analysis techniques is
essential. Excellent skills in computer programming with Java, Matlab, Python, R, (Perl, C++), UNIX, and
knowledge of biological database systems are necessary merits.
To apply, send your CV, a cover letter, and the email address of 2 references to
Erik.Sonnhammer@scilifelab.se. The position is fully funded for 4 years of full-time study and
offers a competitive salary and excellent computational resources. For further information about the
research project, contact Erik.Sonnhammer@scilifelab.se, Tel: +46-(0)70-5586395,
sonnhammer.org