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Synthetic Biology

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
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Data Integration and Mining for Synthetic Biology Design.

Göksel Mısırlı¹, Jennifer Hallinan¹, Matthew Pocock^1,2

¹School of Computing Science, Newcastle University , NE1 7RU Newcastle upon Tyne, United Kingdom.

ACS Synthetic Biology

|April 26, 2016

Summary

Synthetic biology design is accelerated by SyBiOnt, a new ontology and knowledge base. It integrates scattered biological data, enabling automated mining of biological parts for faster engineering.

Keywords:

Semantic Web automated identification of biological parts data integration data mining ontologies synthetic biology

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Area of Science:

Synthetic Biology
Bioinformatics
Computational Biology

Background:

Synthetic biology aims to create predictable biological systems using modular parts.
Current limitations stem from a lack of well-defined and characterized biological parts and devices.
Existing biological information is fragmented across diverse databases and formats, hindering data mining.

Purpose of the Study:

To develop a computational approach for integrating disparate biological data.
To present SyBiOnt, an ontology for synthetic biology design.
To create a knowledge base (SyBiOntKB) using SyBiOnt for automated biological part discovery.

Main Methods:

Developed SyBiOnt, an ontology modeling biological parts and their relationships.
Created the SyBiOntKB knowledge base by integrating existing life sciences ontologies and standards.
Applied ontology reasoning capabilities to automate the mining of biological parts.

Main Results:

SyBiOnt facilitates the modeling of information about biological parts and their relationships.
SyBiOntKB successfully incorporated and built upon existing life sciences ontologies and standards.
Automated mining of biological parts from SyBiOntKB was achieved using ontology reasoning.

Conclusions:

The SyBiOnt ontology and SyBiOntKB knowledge base provide an integrated format for biological data.
This approach automates the mining of biological parts, speeding up synthetic biology design.
The proposed method aids in automating the biological engineering life cycle.