Computational models and methods for deciphering evolutionary patterns in bacterial genomic data

Project Details


How do new bacterial species emerge? How does the distribution of antibiotic resistance genes evolve in a bacterial population? The emerging field of bacterial population genomics, involving the analysis of hundreds or thousands of genomes from defined populations, provides an unprecedented opportunity to answer such questions. Simulation models, replicating the evolutionary processes as accurately as possible, constitute the most reliable means to select between competing biological hypotheses. However, standard model fitting techniques are not tractable with such models. Here, we develop new simulation models to investigate the microbial evolution on the level of genome sequences, and computational techniques that enable fitting of such models. The models will be used to extract new knowledge from several previously published data sets, together with international microbiology collaborators from Harvard, USA, and Cambridge, UK. The primary site of research is Aalto University.
Effective start/end date01/09/201531/08/2020

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being


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