Stochastic modeling of gene activation and application to cell regulation

TL;DR

This paper develops a stochastic model for gene activation involving transcription factors, analyzing their binding times and probabilities, and applies it to embryonic gene regulation to explain cellular differentiation based on morphogen gradients.

Contribution

It introduces a stochastic framework for TF binding dynamics and demonstrates its application to understanding spatial gene regulation in embryonic development.

Findings

Derived probabilities of multiple TFs binding to target sites.

Applied model to hunchback regulation in fly embryos.

Proposed a mechanism for cells to interpret morphogen gradients.

Abstract

Transcription factors (TFs) are key regulators of gene expression. Based on the classical scenario in which the TF search process switches between one-dimensional motion along the DNA molecule and free Brownian motion in the nucleus, we study the arrival time of several TFs to multiple binding sites and derive, in the presence of competitive binding ligands, the probability that several target sites are bound. We then apply our results to the hunchback regulation by bicoid in the fly embryo and we propose a general mechanism that allows cells to read a morphogenetic gradient and specialize according to their position in the embryo.