Computational modeling of RNAi
RNA interference (RNAi) is a recently discovered biological mechanism that appears to be a widespread and highly evolutionarily conserved (i.e., ancient) genetic immune mechanism. Research in the past five years or so has shown that RNAi is involved in defense against some classes of viruses and transposons, as well as in certain cellular regulatory mechanisms. The exciting feature of this mechanism is that it can be exploited mechanistically to target some viruses. This offers hints of the first possible direct treatment for viral infections, as well as the potential ability to selectively knock down the expression of specific genes (via post-transcriptional disruption of the corresponding mRNA), greatly simplifying gene function studies.
Unfortunately, while a reasonable qualitative picture of the mechanics of RNAi has emerged, we are still far from a quantitative and predictive understanding. Currently, activating sequences (siRNA or dsRNA) are hand-picked employing rough "rules of thumb". Our group is attempting to build more quantitative and predictive models by applying machine learning-based bioinformatic techniques to genome and RNAi data sets. Our goals are to produce high-accuracy predictions of the activity of specific sequences and, hopefully, to shed light on some of the mechanical and evolutionary details of RNAi. Along the way, we hope to answer pragmatic questions such as the expected false positive rate (i.e., rate of knockdown of untargeted genes) and minimal covering sets for gene families.
Here's some more information about our RNAi research efforts:
