Different noise assumptions yield qualitatively different landscapes and transition paths in gene regulation models

Intrinsic gene expression noise is a major source of phenotypic variability in cancer biology, and noise-induced transitions are thought to contribute to everything from developmental error correction to drug resistance. It is increasingly common to incorporate noise into mathematical models of gene networks, but limited experimental knowledge forces noise to be modeled in a phenomenological/approximate way. Do the different ad-hoc ways noise is included in these models qualitatively affect their predictions? Building on earlier work that analyzed one and two gene toy models, we present results on how noise assumptions affect landscapes and transition paths in models of the epithelial-to-mesenchymal transition (EMT) and early T cell development. We focus on two aspects of modeling noise: its functional form (constant/additive, multiplicative/linearly dependent on concentration, or the ‘canonical’ Gillespie-like prescription) and its symmetry (whether different genes have the same amount of noise). We find that different assumptions about noise can dramatically impact (i) the relative occupancy of different states, (ii) the stability/existence of intermediate states, and (iii) transition rates and paths.