# Moment-based analysis of biochemical networks in a heterogeneous population of communicating cells

## TL;DR

This paper introduces a scalable moment-based method to analyze how cell-cell communication influences noise in biochemical networks within heterogeneous cell populations, balancing accuracy and computational efficiency.

## Contribution

It develops a novel, scalable moment-based framework that captures population-level noise effects in communicating cells, using symmetry-based model reduction.

## Key findings

- The method accurately approximates stochastic moments compared to simulations.
- Communication can both reduce and amplify noise depending on network parameters.
- The approach scales efficiently to large populations without increasing complexity.

## Abstract

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.