Noise Propagation in Transcriptional Genetic Cascades

Cellular processes are inherently stochastic, leading to fluctuations in protein concentration quantifiable as noise in gene expression. Precise description of noise propagation in gene networks is essential for designing noise-tolerant gene circuits and understanding signal reliability in biological networks, but current models for noise propagation are primarily system-specific or limited to short gene cascades. Here we present an analytical expression for noise propagation in gene expression that works for long cascades and incorporates global noise. Since modelling all aspect of noise can be prohibitively complicated, in many situations only intrinsic or global noise is considered, but general criteria for when each type is dominant are still lacking. As an example of the possible use of our analytical expression, we examine the role different aspects of the network have on the balance between intrinsic and global noises and their propagation. We show that the type of cascade, cascade length, sensitivity, and basal transcription rates have an effect beyond simple protein abundance. This has practical implications for designing synthetic gene networks in prokaryotes and improving our knowledge on noise propagation in gene networks, and could shed light on how evolution may shape circuit sizes to balance signal fidelity and metabolic cost.