Work

Nonlinear gene network inference: integrating sigmoid-response differential equations with sequential thresholded least squares

Year: 2026

Type: article

Abstract: Gene regulatory networks (GRNs) are fundamental to cellular identity, differentiation, and disease. While many computational methods infer GRNs from transcriptomic data, most dynamical approaches rely on linear or non-parametric models, overlooking nonlinear features such as saturation, cooperativity, and feedback. We propose sigmoid-response sequential thresholded least squares (SRSTLS), a framework combining a sigmoid-response dynamical model with a sequential thresholded least squares optimizer to reconstruct nonlinear regulatory interactions from time-series or pseudotemporal gene expression data. SRSTLS is validated on six simulated ODE-based benchmarks, including toggle-switch and larger networks, and two real single-cell RNA-seq datasets from embryonic stem cells and hepatoblasts. Compared to SCODE and AR1MA1-VBEM, SRSTLS shows superior accuracy in edge identification and sign determination, and remains robust across noise levels and pseudotemporal ordering parameters. Our findings demonstrate that incorporating nonlinear dynamics markedly improves GRN inference, positioning SRSTLS as a reliable and interpretable tool for studying regulatory mechanisms in complex biological systems.

Source: npj Systems Biology and Applications

DOI: 10.1038/s41540-026-00819-8

Authors: Zhuozhen Xue, Ruiqi Wang

Institution: Shanghai University

Language: English

Topic: Gene Regulatory Network Analysis

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $3,490

Funder: National Natural Science Foundation of China