Design and assembly of DNA molecules using multi-objective optimization

Area of Science:

• Synthetic Biology
• Bioengineering
• Computational Biology

Background:

• Synthetic biology workflows are often inefficient due to a lack of manufacturing constraint integration in the design phase.
• This limitation reduces the overall yield and scalability of engineering biological systems.

Purpose of the Study:

• To address the challenge of integrating manufacturing constraints into the DNA engineering design process.
• To develop a method that optimizes synthetic biology workflows by finding a balance between design requirements and production limitations.

Main Methods:

• Developed a novel open-source algorithm named Multi-Objective Optimisation algorithm for DNA Design and Assembly (MODDA).
• The algorithm treats DNA engineering as a multi-objective optimization problem.
• MODDA is available as a Python/Anaconda package and a Docker image for accessibility.

Main Results:

• Experimental validation demonstrated that the algorithm produces near-optimal DNA constructs.
• The method exhibits linear scalability with increasing design complexity.
• Successfully paved the way for rational engineering of DNA molecules.

Conclusions:

• The developed algorithm effectively integrates manufacturing constraints into DNA design.
• MODDA enhances the efficiency and yield of synthetic biology applications.
• Enables rational and scalable engineering of DNA from genes to genomes.