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Related Experiment Video

Updated: May 20, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

Published on: May 9, 2017

Reverse engineering biomolecular systems using -omic data: challenges, progress and opportunities.

Chang F Quo¹, Chanchala Kaddi, John H Phan

¹Georgia Institute of Technology, Atlanta, GA 30332, USA.

Briefings in Bioinformatics

|July 27, 2012

Summary

This summary is machine-generated.

Reverse engineering of biomolecular systems (REBMS) uses data mining and systems modeling to uncover biological insights. Synthetic biology offers a novel approach to validate these complex system models experimentally.

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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

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Area of Science:

Biotechnology
Systems Biology
Synthetic Biology

Background:

High-throughput biotechnologies accelerate research in reverse engineering of biomolecular systems (REBMS).
Current approaches integrate data-driven (data mining) and design-driven (systems modeling) methods for analyzing -omic data.
Challenges include data integration, combining modeling approaches, and experimental validation.

Purpose of the Study:

To review progress and opportunities in REBMS.
To explore the role of synthetic biology in validating system models.
To outline an integrated workflow for addressing REBMS challenges.

Main Methods:

Review of current literature and methodologies in REBMS.
Exploration of data mining and systems modeling techniques.
Discussion of synthetic biology as an analysis-by-synthesis approach.

Main Results:

Identified key challenges in data integration, systems modeling, and experimental validation.
Highlighted the potential of synthetic biology for direct model validation.
Proposed an integrated workflow combining data mining, systems modeling, and synthetic biology.

Conclusions:

An integrated approach using data mining, systems modeling, and synthetic biology is crucial for advancing REBMS.
Synthetic biology provides a powerful platform for experimental validation of biomolecular system models.
Addressing current challenges will facilitate deeper understanding and manipulation of biological systems.