Modelling cellular signalling variability based on single-cell data: the TGFb/SMAD signaling pathway

TL;DR

This paper reviews how mathematical models and single-cell data reveal the sources and implications of variability in the TGFb/SMAD signaling pathway, crucial for cell decision-making processes.

Contribution

It provides a comprehensive review of experimental and theoretical approaches to modeling signaling heterogeneity, focusing on the TGFb/SMAD pathway.

Findings

Heterogeneity originates from stochastic fluctuations in cellular components.

Mathematical models help understand signaling variability.

Single-cell data elucidate pathway dynamics and decision outcomes.

Abstract

Non-genetic heterogeneity is key to cellular decisions, as even genetically identical cells respond in very different ways to the same external stimulus, e.g., during cell differentiation or therapeutic treatment of disease. Strong heterogeneity is typically already observed at the level of signaling pathways that are the first sensors of external inputs and transmit information to the nucleus where decisions are made. Since heterogeneity arises from random fluctuations of cellular components, mathematical models are required to fully describe the phenomenon and to understand the dynamics of heterogeneous cell populations. Here, we review the experimental and theoretical literature on cellular signaling heterogeneity, with special focus on the TGFb/SMAD signaling pathway.