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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.
Golden rice
Golden rice is a genetically modified...

Combinatorial Gene Control

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...

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Updated: Jun 16, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

GOLDBAR: A Framework for Combinatorial Biological Design.

Nicholas Roehner¹, James Roberts^2,3, Andrei Lapets⁴

¹RTX BBN Technologies, Cambridge, Massachusetts 02138, United States.

ACS Synthetic Biology

|August 20, 2024

Summary

This summary is machine-generated.

Synthetic biologists can now optimize DNA designs with GOLDBAR, a new framework for comparing biological design rules. This facilitates automated analysis and machine learning in synthetic biology.

Keywords:

biological design combinatorial engineering design automation genetic design machine learning regular grammar

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Area of Science:

Synthetic Biology
Computational Biology
Bioinformatics

Background:

Synthetic biology relies on combinatorial libraries for DNA design optimization.
Existing design frameworks lack formal comparison capabilities for automated analysis and machine learning.
Massive biological design spaces require advanced computational tools.

Purpose of the Study:

Introduce GOLDBAR, a combinatorial design framework.
Enable formal comparison and merging of biological design rules.
Facilitate automated analysis and machine learning in synthetic biology.

Main Methods:

Developed the GOLDBAR framework for combinatorial design.
Applied GOLDBAR to analyze transcriptional logic circuits and gene clusters.
Demonstrated intersecting and merging design rules for motif extraction and inference.

Main Results:

GOLDBAR refines design spaces for TetR-homologue transcriptional logic circuits.
Verified assembly of a partial nif gene cluster using GOLDBAR.
Inferred novel gene clusters for rebeccamycin biosynthesis.

Conclusions:

GOLDBAR enhances the analysis of biological designs.
The framework supports automated analysis and machine learning applications.
GOLDBAR can facilitate grammar-based machine learning in synthetic biology.