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Related Experiment Video

Updated: May 2, 2026

Quantitative Analysis of Random Migration of Cells Using Time-lapse Video Microscopy

Quantitative Analysis of Random Migration of Cells Using Time-lapse Video Microscopy

Published on: May 13, 2012

Computational modeling of single-cell dynamics data.

Wenbo Guo¹, Zeyu Chen, Jin Gu¹

¹MOE Key Lab of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, No. 30 Shuangqing Road, Haidian District, Beijing 100084, China.

Briefings in Bioinformatics

|June 30, 2025

Summary

This summary is machine-generated.

Understanding cell dynamics is crucial for life sciences and medicine. This review covers computational challenges and algorithmic solutions for analyzing dynamic single-cell sequencing data to explore complex biological processes.

Keywords:

algorithms machine learning single-cell dynamics time-series data

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Related Experiment Videos

Last Updated: May 2, 2026

Quantitative Analysis of Random Migration of Cells Using Time-lapse Video Microscopy

Quantitative Analysis of Random Migration of Cells Using Time-lapse Video Microscopy

Published on: May 13, 2012

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

Published on: July 18, 2013

Live-cell Imaging of Single-Cell Arrays LISCA - a Versatile Technique to Quantify Cellular Kinetics

Live-cell Imaging of Single-Cell Arrays LISCA - a Versatile Technique to Quantify Cellular Kinetics

Published on: March 18, 2021

Area of Science:

Computational Biology
Systems Biology
Genomics

Background:

Single-cell sequencing technologies enable dynamic measurements over time.
Analyzing dynamic single-cell data presents significant computational challenges.

Purpose of the Study:

To review challenges in analyzing dynamic single-cell data.
To overview algorithmic advancements for characterizing cell dynamics.
To discuss future directions integrating technology and AI.

Main Methods:

Elaboration of experimental limitations, data features, and biological discovery challenges.
Overview of algorithms for inferring cell dynamics, dissecting mechanisms, predicting fates, and integrating lineage tracing.
Discussion of advancements in biological technologies and AI.

Main Results:

Identification of key challenges in dynamic single-cell data analysis.
Categorization of algorithmic solutions for four critical tasks in characterizing cell dynamics.
Highlighting the potential of emerging technologies and AI.

Conclusions:

Addressing computational challenges is vital for advancing cell dynamics research.
Algorithmic progress is key to unlocking insights from dynamic single-cell data.
Future integration of AI and advanced biological technologies will enhance spatiotemporal exploration of life processes.