Modelling the stochastic evolution of B cells
This is a Master’s thesis opportunity and is a collaboration with Prof. Beerenwinkel’s group at ETH D-BSSE in Basel and IBM Research – Zurich.
Germinal centers (GCs) are specialized compartments within the secondary lymphoid organs, where B cells proliferate, mutate their antibody genes and differentiate into plasma cell or memory B cells. Although we have a good comprehension of plasma cell differentiation, memory B cell differentiation is still incompletely understood. The Systems Biology group at IBM Research – Zurich has recently developed a quantitative stochastic model of the intracellular and extracellular dynamics governing B cell maturation and exit from the GC [ More ].
The model includes an intracellular ODE component, which accounts for the gene regulatory events that shape the differentiation decision, and an extracellular component that models the stochastic interactions with other cells within the GCs. To simulate this model efficiently, our group has developed a modified version of the Gillespie algorithm, which enables the efficient simulation of entities with individual properties. This is an example of Poisson thinning, where the propensities of undesired reactions might be computed and subsequently rejected.
B cells that carry receptors with a higher affinity to antigens have a competitive advantage that enables them to proliferate at a faster rate and promote the emergence of high-affinity clones of cells. However, the mutational process accounting for B cell maturation is currently not included in the model. During this Master’s thesis research, we will modify the GC model to account explicitly for the changes in the sequences of B cell receptor genes and study their impact in B cell differentiation. For this task, we will use probabilistic approaches to model the mutagenic process and infer the phylogenetic tree of B cell evolution. The inferred tree will be coupled to the stochastic model of the GC to investigate the biological impact of mutations that confer higher affinity to B cells.
The Systems Biology group at IBM Research – Zurich aims to develop new mathematical and computational approaches to model and understand biomedical systems. Candidates should have a strong background in mathematics or physics, including statistics and probability, and be interested in biologically-related research.
- Good knowledge of statistics, probability and mathematical modeling
- Working knowledge of C or C++
- Working knowledge of Matlab, R or equivalent
- In addition, some knowledge of molecular biology, genetic and systems biology, as well as high-throughput technologies for the molecular characterization of cancer samples would be beneficial, although it is not essential.
IBM is committed to diversity at the workplace. With us you will find an open, multicultural environment. Excellent, flexible working arrangements enable all genders to strike the desired balance between their professional development and their personal lives.
Questions? For more information, please contact Dr. María Rodríguez Martínez.