Construction of gene regulatory networks with colored noise
Given recent investigations of gene regulatory networks, an increasing amount of attention is focused on the nonlinearity and randomness in these networks. It has always been assumed that gene regulation is a random process with Gaussian white noise. However, in practice, there is no ideal white noise; therefore results obtained from a model with white noise are not always exactly correct. We constructed a dynamic model of gene regulatory networks based on a first-order stochastic differential equation, which is often used for quantitative analysis of gene regulatory networks. For biological realism, we added a colored noise item, based on a sliding autoregressive model. The abilities of regulatory genes and the intensities of the colored noise item were estimated using an extended recursive least-square algorithm. We applied the model to budding yeast data and reconstructed regulatory networks. Our experimental results showed that the proposed model is suitable for the description of real gene regulatory networks.
gene regulatory networks colored noise stochastic differential equation mutual information extended recursive least squares