How does noise propagate in genetic networks? A new approach to understand stochasticity in genetic networks

The emergence of apparently deterministic and reproducible behaviors from highly fluctuating components in genetic networks has been attracting great attentions. However, only little has been known on the mechanisms of such emergence because of the complexity and the digital nature in genetic networks, which make it hard and untractable to analyze them by usual dynamical and stochastic methods. In this work, we propose a new method that employs a numerical evaluation of cumulants and a graphical representation. This method visually describes propagations of fluctuations in genetic networks and facilitates intuitive understanding of stochastic properties of the networks. In addition, this method works well even if the networks consist of many components and reactions and the copy numbers of some components are low.