  Cells can utilize chemical communication to exchange information and
coordinate their behavior in the presence of noise. Communication can reduce
noise to shape a collective response, or amplify noise to generate distinct
phenotypic subpopulations. Here we discuss a moment-based approach to study how
cell-cell communication affects noise in biochemical networks that arises from
both intrinsic and extrinsic sources. We derive a system of approximate
differential equations that captures lower-order moments of a population of
cells, which communicate by secreting and sensing a diffusing molecule. Since
the number of obtained equations grows combinatorially with number of
considered cells, we employ a previously proposed model reduction technique,
which exploits symmetries in the underlying moment dynamics. Importantly, the
number of equations obtained in this way is independent of the number of
considered cells such that the method scales to arbitrary population sizes.
Based on this approach, we study how cell-cell communication affects population
variability in several biochemical networks. Moreover, we analyze the accuracy
and computational efficiency of the moment-based approximation by comparing it
with moments obtained from stochastic simulations.
