A key goal of systems biology is the predictive mathematical description of
gene regulatory circuits. Different approaches are used such as deterministic
and stochastic models, models that describe cell growth and division explicitly
or implicitly etc. Here we consider simple systems of unregulated
(constitutive) gene expression and compare different mathematical descriptions
systematically to obtain insight into the errors that are introduced by various
common approximations such as describing cell growth and division by an
effective protein degradation term. In particular, we show that the population
average of protein content of a cell exhibits a subtle dependence on the
dynamics of growth and division, the specific model for volume growth and the
age structure of the population. Nevertheless, the error made by models with
implicit cell growth and division is quite small. Furthermore, we compare
various models that are partially stochastic to investigate the impact of
different sources of (intrinsic) noise. This comparison indicates that
different sources of noise (protein synthesis, partitioning in cell division)
contribute comparable amounts of noise if protein synthesis is not or only
weakly bursty. If protein synthesis is very bursty, the burstiness is the
dominant noise source, independent of other details of the model. Finally, we
discuss two sources of extrinsic noise: cell-to-cell variations in protein
content due to cells being at different stages in the division cycles, which we
show to be small (for the protein concentration and, surprisingly, also for the
protein copy number per cell) and fluctuations in the growth rate, which can
have a significant impact.Comment: 23 pages, 5 figures; Journal of Statistical physics (2012
