We are interested in dynamical modelling of gene transcription regulation. The activity of genes is regulated by transcription factor proteins that can turn different genes on or off. These regulatory relationships can most naturally be studied using time series gene expression data. As the transcription factor protein activities cannot be measured using high-throughput techniques, they have to be inferred from expression data using techniques resembling state-space models. Combining the probabilistic inference techniques with prior information from standard biochemical models is an important topic of study. A method using Gaussian processes as a prior for transcription factor activities and ordinary differential equation transcription models is presented in (Gao et al., 2008).
P. Gao, A. Honkela, M. Rattray, N. D. Lawrence. (2008).
Gaussian
process modelling of latent chemical species: applications to
inferring transcription factor activities.
Bioinformatics
24(16), pp. i70-i75. In Proceedings of ECCB 2008.
doi:10.1093/bioinformatics/btn278
You are at: CIS → Research → Algorithms → Bayes → Research
Page maintained by antti.honkela at tkk.fi, last updated Wednesday, 01-Oct-2008 10:43:43 EEST