The availability of time series mRNA expression data sets has spurred
the race to infer Genetic Regulatory Networks (GRNs) to explain trend and
causal relationships among genes measured in microarray experiments.
As a complement to such techniques Dimension Reduction Techniques, borrowing
from experience on financial time series can be used to add further insight
to such complex datasets.
Novel Database Models will be developed to make use of the natural
inter-relationships between data arising from microarray experiments carried
out according to, for example, the MIAME standard. Finally, inferential algorithms that aim
at building GRN models from these data are developed. SÓrbu, A., Ruskin, H. J., and Crane, M., EGIA - Evolutionary Optimisation of Gene Regulatory Networks, an Integrative Approach, Complex Networks V, Studies in Computational Intelligence, Volume 549, 2014, pp. 217-229. DOI: 10.1007/978-3-319-05401-8_21.
Selected Group Publications:
Marbach, D., Costello, J.C., KŁffner, R., Vega, N., Prill, R.J., Camacho, D.M., Allison, K.R., the DREAM5 Consortium, Kellis, M., Collins, J.J., and Stolovitzky, G. 2012. Wisdom of crowds for robust gene network inference, Nature Methods 9, 796Ė804.
SÓrbu, A., Ruskin, H. J., and Crane, M., Integrating Heterogeneous Gene Expression Data for Gene Regulatory Network Modelling, Theory In Biosciences, Springer-Verlag, 1-8 , DOI 10.1007/s12064-011-0133-0, 2011.
Kerr, G., Perrin, D., Ruskin, H.J., Crane, M. Edge Weighting of Gene Expression Graphs, Advances in Complex Systems, 13:2, 217-238, World Scientific 2010.