Computational Modelling Group

• Login/Register

You are here:

• CMG Home
• People
• Sonya Ridden

I am a PhD student in the School of Mathematics, working on the mathematical modelling of cell-to-cell variability in stem cell populations. The development and behaviour of a cell is governed by its cell fate regulatory network, which is a dynamical system that describes the interactions between the genes and the products of their expression (mRNA and proteins). Cell fate regulatory networks are very complex and the interactions are non-linear, due to a plethora of feedback loops and protein-protein interactions. This inherent complexity makes it impossible to understand the relationships between cell behaviour and regulatory network architecture using experiment and intuition alone. A mathematical model can provide the basis for theoretical description of the system, and offer a deeper insight into the mechanisms of cell fate specification. I focus on stochastic differential equations, exploring the origins of fluctuations in gene expression levels that have been observed in clonal cell populations. This provides a basis for simulation models that can be used to predict how changes in the level of a particular protein can lead to specific cell types during development.

This research is funded by the EPSRC.

Further information about me and my research can be found at:

http://uk.linkedin.com/in/sonyaridden/

BSc in Mathematics (First) (2002) - University of Southampton

Dip Act Sci (with distinction), Actuarial Science (2003) - Heriot-Watt University

MSc in Biometry (with distinction) (2006) - University of Reading

Research Interests

Life sciences simulation: Bioinformatics, Biomathematics, Biomedical, Biomolecular simulations, Developmental Biology, Epigenetics, Evolution, Systems biology

Algorithms and computational methods: Evolutionary Algorithms, Graph Theory, Inverse problems, Molecular Dynamics, Monte Carlo, statistical analysis

Programming languages and libraries: Matlab, Python, R

Computational platforms: Mac OS X, Windows

Transdisciplinary tags: Complex Systems, IfLS, Quantitative Biology

Working with...

Benjamin Macarthur Lecturer, Mathematics (FSHS)

Research Groups