Estimation of gene expression by a bank of particle filters

This paper addresses the problem of joint estimation of time series of gene expressions and identification of the coefficients of gene interactions defining the network. The proposed method exploits a state-space structure describing the system so that a bank of particle filters can be used to efficiently track each of the time series separately. Since each gene interacts with some of the other genes, the individual filters need to exchange information about the states (genes) that they track. The analytical derivation of the posterior distribution of the states given the observed data allows for marginalization of the matrix describing the interactions in the network and for efficient implementation of the method. Computer simulations reveal a promising performance of the proposed approach when compared to the conventional particle filter that attempts to track the time series of all the genes and which, as a result, suffers from the curse-of-dimensionality.