Gene expression is a set of processes by which the information encoded in a genetic template is used to make active molecules, especially proteins. The development of new technologies enabling the measurement of gene expression at the level of individual molecules continually motivates the research of mathematical models describing the gene expression dynamics. At our department, we primarily focus on the methodology using differential equations and stochastic simulations.

Selected papers

Bokes, P., King, J. R., Wood, A. T., & Loose, M. (2012). Exact and approximate distributions of protein and mRNA levels in the low-copy regime of gene expression. Journal of mathematical biology, 64(5), 829-854.

Singh, A., & Bokes, P. (2012). Consequences of mRNA transport on stochastic variability in protein levels. Biophysical journal, 103(5), 1087-1096.

Bokes, P., King, J. R., Wood, A. T., & Loose, M. (2013). Transcriptional bursting diversifies the behaviour of a toggle switch: hybrid simulation of stochastic gene expression. Bulletin of mathematical biology, 75(2), 351-371.

Soltani, M., Bokes, P., Fox, Z., & Singh, A. (2015). Nonspecific transcription factor binding can reduce noise in the expression of downstream proteins. Physical biology, 12(5), 055002.

Bokes, P., & King, J. R. (2019). Limit-cycle oscillatory coexpression of cross-inhibitory transcription factors: a model mechanism for lineage promiscuity. Mathematical Medicine and Biology: A Journal of the IMA 36(1), 113–137.