Computational Life Sciences

Research area

Biological research in the life sciences is changing rapidly now that biologists are generating vast amounts of data on very complex regulatory systems. In modern biology bioinformatics plays an essential role in analysing data and modelling is required to help us understand these intricate biological systems.Thus, a major challenge in the next decades is to extract useful information from vast amounts of biological data and to develop computational models to investigate the complex dynamics of living organisms. Accomplishing these goals will depend on collaborations between experimentalists and computational biologists, which is nowadays known as BioComplexity, Systems Biology and/or Quantitative Biology. Our mission with this PhD programme is to train students to become computational biologists with a strong expertise in modelling and/or bioinforatics, and a strong footing in the life sciences.During this programme you will learn to use and develop computational approaches for bioinformatics data analysis and/or for mathematical and computational modelling. Current expertise in the Computational Life Sciences PhD programme involves a variety of biological disciplines and computational approaches. Some of the biological areas that we cover are genome evolution, metagenomics, eco-evolutionary dynamics, gene regulatory networks, bacterial evolution, immunology, cell motility, development, and spatial pattern formation. PhD candidates can therefore be trained a variety of biological areas. Computationally the programme covers scripting (in Python or Perl), programming (in R, C or C++), mathematical modelling (ODEs), and computer simulation (varying from numeric integration, to agent based models and the cellular Potts model).

Associated research groups

All information regarding our researchers and research groups can be found at our website.

Profile of prospective PhD candidates

PhD candidates in Computational Life Sciences should be highly motivated and ideally have a strong background in the life sciences as well as in computational modelling. Because of the interdisciplinary nature of our PhD programme the criteria for admission are flexible and depend on the background of the PhD candidate and the requirements of the PhD project. An optimal preparation for a PhD candidate in Computational Life Sciences is the track Computational Biology in the Master’s programme Molecular & Cellular Life Sciences of the Graduate School of Life Sciences. An excellent add-on is the ‘QBio honours programme’ of the Institute for Biodynamics and Biocomplexity. Students with a Master’s education in mathematics or physics may also be admitted provided that they have successfully completed courses in the biological, modelling and/or bioinformatics areas relevant to the PhD project. Similarly, students with a Master’s degree in the life sciences, that have not yet developed sufficient modelling or bioinformatics skills, should repair their computational skills to be admitted to the school. An excellent preparatory course for any future candidate is the MSc course Computational Biology (B-MCOBI).

Mission of the training programme

  • The main objective of the Computational Life Sciences PhD programme is to train PhD candidates to become excellent and independent computational biologists, with solid expertise in modelling and/or bioinformatics, combined with a good interdisciplinary knowledge in the life sciences.
  • Computational Life Sciences PhDs learn to communicate with members of interdisciplinary research teams (PhD candidates with a theoretical background have to understand the underlying biology, and biologists should master the innovations in computational biology),
  • Computational Life Sciences PhDs learn to become ‘computational biologists’ capable of high-level research on BioComplexity, Systems Biology, Quantitative Biology, and/or Bioinformatics.

Training programme

PhD candidates in our group attend the advanced courses that are given locally and various other national and international courses. Examples of schools and courses attended by our PhD candidates:

  • Q-Bio Summer Schools (organized by the UU and by the Center for Non Linear Studies at Los Alamos National Laboratory),
  • Complexity Summer School (organized by the UU and by the Santa Fe Institute),
  • Computational Biology schools and conferences,
  • Complex Systems Summer School of the Santa Fe Institute
  • Advanced in bioinformatics,
  • Advanced Immunology Courses (e.g, in the Infection & Immunity programme),
  • Statistical and programming courses in R,
  • General courses writing papers and giving presentations.
Programme coordinator