RVINN: a flexible modeling for inferring dynamic transcriptional and post-transcriptional regulation using physics-informed neural networks

TL;DR

This paper introduces a new method using physics-informed neural networks to study dynamic gene regulation in breast cancer cells.

Contribution

The novel contribution is a flexible modeling approach using physics-informed neural networks to infer transcriptional and post-transcriptional regulation.

Findings

The model successfully revalidates and visualizes dynamic phenomena like transcriptional ripple and buffering.

Results suggest potential molecular mechanisms behind observed gene regulatory dynamics in breast cancer cells.

Abstract

Dynamic gene expression is controlled by transcriptional and post-transcriptional regulation. Recent studies on transcriptional bursting and buffering have increasingly highlighted the dynamic gene regulatory mechanisms. However, direct measurement techniques still face various constraints and require complementary methodologies, which are both comprehensive and versatile. To address this issue, inference approaches based on transcriptome data and differential equation models representing the messenger RNA lifecycle have been proposed. However, the inference of complex dynamics under diverse experimental conditions and biological scenarios remains challenging. In this study, we developed a flexible modeling using physics-informed neural networks and demonstrated its performance using simulation and experimental data. Our model has the ability to computationally revalidate and visualize dynamic biological phenomena, such as transcriptional ripple, co-bursting, and buffering in a breast cancer cell line. Furthermore, our results suggest putative molecular mechanisms underlying these phenomena. We propose a novel approach for inferring transcriptional and post-transcriptional regulation and expect to offer valuable insights for experimental and systems biology.

https://github.com/omuto/RVINN.

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