Document Type

Article

Publication Date

2-17-2023

Keywords

JMG

JAX Source

iScience. 2023;26(2):106029.

ISSN

2589-0042

PMID

36824273

DOI

https://doi.org/10.1016/j.isci.2023.106029

Grant

The study is supported by startup funds from The Jackson Laboratory and Northeastern University, and by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R35GM128717. Special thanks Christopher Baker, Gregory Carter, and Amy Yee, whom together form the Thesis Advisory committee of Benjamin Clauss. We also thank Danial A. Ramirez for reproducing the pre-processing of the scRNA-seq data of human glutamatergic neuron differentiation, as described in reference (La Manno et al. 2018).

Abstract

One of the major challenges in biology is to understand how gene interactions collaborate to determine overall functions of biological systems. Here, we present a new computational framework that enables systematic, high-throughput, and quantitative evaluation of how small transcriptional regulatory circuit motifs, and their coupling, contribute to functions of a dynamical biological system. We illustrate how this approach can be applied to identify four-node gene circuits, circuit motifs, and motif coupling responsible for various gene expression state distributions, including those derived from single-cell RNA sequencing data. We also identify seven major classes of four-node circuits from clustering analysis of state distributions. The method is applied to establish phenomenological models of gene circuits driving human neuron differentiation, revealing important biologically relevant regulatory interactions. Our study will shed light on a better understanding of gene regulatory mechanisms in creating and maintaining cellular states.

Comments

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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