A quantitative evaluation of topological motifs and their coupling in gene circuit state distributions

Benjamin Clauss; Mingyang Lu

doi:10.1016/j.isci.2023.106029

A quantitative evaluation of topological motifs and their coupling in gene circuit state distributions

iScience. 2023 Jan 23;26(2):106029. doi: 10.1016/j.isci.2023.106029. eCollection 2023 Feb 17.

Authors

Benjamin Clauss^{1

2

3}, Mingyang Lu^{4

1

2

3}

Affiliations

¹ Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115, USA.
² Genetics Program, Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA.
³ The Jackson Laboratory, Bar Harbor, ME 04609, USA.
⁴ Department of Bioengineering, Northeastern University, Boston, MA 02115, USA.

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.

Keywords: Gene network; Systems biology.

Grants and funding

R35 GM128717/GM/NIGMS NIH HHS/United States