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QWENDY: Gene Regulatory Network Inference by Quadruple Covariance Matrices.

Yue Wang¹, Xueying Tian²

¹Irving Institute for Cancer Dynamics and Department of Statistics, Columbia University, New York, 10027, NY, USA. yuewang@ihes.fr.

Bulletin of Mathematical Biology

|October 22, 2025

Summary

We developed QWENDY, a new method using single-cell gene expression data to infer gene regulatory networks (GRNs). QWENDY provides a unique solution and outperforms previous methods on experimental data.

Keywords:

Gene regulatory network Inference Mathematical modeling

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Area of Science:

Computational Biology
Systems Biology
Genomics

Background:

Gene regulatory networks (GRNs) are crucial for understanding biological processes.
Inferring GRNs from gene expression data is a key challenge in systems biology.

Purpose of the Study:

To introduce QWENDY, a novel computational method for inferring GRNs.
To leverage single-cell gene expression data across multiple time points for improved GRN inference.

Main Methods:

QWENDY employs a linear gene expression model.
It infers GRNs by solving the transformation of covariance matrices.
The method avoids non-convex optimization, yielding a unique solution.

Main Results:

QWENDY demonstrated superior performance on three experimental datasets compared to existing methods.
The method achieved the top rank on experimental data inference.
Performance on synthetic datasets was not as strong.

Conclusions:

QWENDY offers a robust and unique approach to inferring gene regulatory networks from time-series single-cell expression data.
The method shows significant promise for analyzing experimental biological data.