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Updated: May 3, 2026

A Microfluidics Approach for the Functional Investigation of Signaling Oscillations Governing Somitogenesis

A Microfluidics Approach for the Functional Investigation of Signaling Oscillations Governing Somitogenesis

Published on: March 19, 2021

Fluidic and microfluidic tools for quantitative systems biology.

Burak Okumus¹, Sadik Yildiz², Erdal Toprak²

¹Department of Systems Biology, Harvard Medical School, Boston, MA, USA.

Current Opinion in Biotechnology

|February 4, 2014

Summary

This summary is machine-generated.

Engineering approaches using advanced fluid-handling technologies enable high-throughput studies to link genotype and phenotype. These methods allow precise single-cell analysis, rare phenotype detection, and development of in vitro disease models.

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Last Updated: May 3, 2026

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Area of Science:

Biotechnology
Genetics
Systems Biology

Background:

Understanding complex gene functions and genotype-phenotype relationships is challenging.
High-throughput, sensitive studies are crucial for genetic research.
Biological complexity necessitates advanced analytical tools.

Purpose of the Study:

To explore how engineering approaches, particularly fluid-handling technologies, can advance genetic studies.
To enable precise identification of genetic factors influencing phenotypes.
To facilitate single-cell analysis and disease modeling.

Main Methods:

Development and application of macro- and micro-scale fluid-handling technologies.
High-throughput screening and systematic genetic studies.
Single-cell perturbation, monitoring, and analysis.

Main Results:

Enabled identification of genetic factors linked to specific phenotypes.
Facilitated evaluation of cell-to-cell variability and detection of rare phenotypes.
Allowed construction of accurate in vitro disease models.

Conclusions:

Advanced fluid-handling technologies provide powerful engineering solutions for complex biological questions.
These tools enhance sensitivity and throughput for genotype-phenotype correlation studies.
The approach supports precise cellular analysis and the development of disease models.