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Related Experiment Video

Updated: May 21, 2026

An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions

An Optogenetic Method to Control and Analyze Gene Expression Patterns in Cell-to-cell Interactions

Published on: March 22, 2018

Inferring stochastic dynamics by biophysical Neural ODE using single-cell transcriptomics.

Jingyu Dou^1,2, Wentao Lyu², Feng Chen¹

¹Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.

Nature Communications

|May 19, 2026

Summary

This summary is machine-generated.

DynNet, a novel deep learning method, infers gene regulatory dynamics for cell fate decisions from single-cell data. It overcomes limitations of existing models, accurately reconstructing cell state transitions and developmental trajectories.

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Area of Science:

Computational Biology
Systems Biology
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) enables cellular heterogeneity analysis but inferring dynamic processes from static data is challenging.
Existing computational models for cell fate decisions involve a trade-off between biological realism and interpretability.
Understanding gene regulatory dynamics is crucial for dissecting cell fate decisions and developmental processes.

Purpose of the Study:

To develop a deep learning framework, DynNet, that integrates Neural Ordinary Differential Equations (Neural ODEs) with biophysical models and prior knowledge.
To accurately infer stochastic gene regulatory dynamics governing cell fate decisions from scRNA-seq data.
To overcome the limitations of current mechanistic and data-driven approaches in modeling cellular dynamics.

Main Methods:

DynNet integrates Neural ODEs with biophysical models and prior knowledge of gene expression.
The method learns the stochastic dynamics of gene regulatory systems for cell fate decisions.
Benchmarking was performed on synthetic datasets and real biological data (hepatocyte differentiation and EMT).

Main Results:

DynNet successfully infers stable cell states and reconstructs dynamical trajectories from static scRNA-seq data.
The method characterizes multi-stable cell fate transitions and quantifies transition probabilities.
Application to hepatocyte differentiation and EMT data revealed developmental trajectories, cell fate landscapes, and critical gene regulations.

Conclusions:

DynNet provides a powerful, interpretable deep learning approach for inferring gene regulatory dynamics and cell fate landscapes from scRNA-seq data.
The method advances our understanding of cellular heterogeneity and cell fate decisions in developmental and disease contexts.
DynNet offers a promising tool for dissecting complex biological processes at the single-cell level.