Using noise transmission properties to identify stochastic gene regulatory networks

Gene regulatory networks often occur at such small scales that their dynamics are controlled by individual molecular events. This discrete behavior causes significant quantities of intrinsic noise. In some cases, mechanisms exist in the system to repress this noise. With different parameters, the same mechanisms may amplify the noise. By examining the properties of how noise is transmitted through the system, one can gather significant information about the system and aid researchers to identify system properties. In this paper, we consider a few simple analytical schemes to identify the parameters of gene transcription and translation processes with feedback regulation. While protein distributions can be measured with fluorescent protein tagging and flow cytometry, it is much more difficult to measure the quantities of messenger RNAs in a single cell. We show that with the right experimental procedures involving measurements of proteins alone, one can identify transcription and translation parameters.