Mathematical modelling of immune signalling networks

The overall immune response to pathogens emerges from complex, noisy and nonlinear interactions between immune cells and underlying regulatory signalling networks. A hallmark of this process is the ability of cells to propagate inflammatory cues by releasing small proteins called cytokines. This creates regulatory feedback that allows coordinating inflammatory signalling, however it has to be very carefully controlled to avoid out-of-control responses.

We previously used mathematical modelling and live-cell imaging to quantitatively characterize oscillations of the Nuclear Factor kappa B transcription factors, a key regulator of cytokine expression during inflammation (Paszek et al., PNAS, 2010, Ashall et, Science, 2009). We made exiting prediction that NF-κB-mediated signalling may be controlled through subtle changes in single cell dynamics and heterogeneity between individual cells.

The aim of this project is to develop a better understanding how immune cells use systems like NF-kB to encode noisy inflammatory cues and relay those to other cells. The project will involve differential equations and stochastic simulations to evaluate the role of molecular noise in those processes.

We use a multidisciplinary system biology approach involving mathematical modelling, molecular cell biology and immunology. The highlight of the project is use of the state-of-the-art live-cell imaging data collected in the group to gain unique insights into single-cell behaviour.

This is an opportunity for an ambitious student with theoretical background to work in a stimulating interdisciplinary environment. Training in computational modeling, live-cell imaging (including microfluidic approaches) and biology will be provided to fit student’s interests.