An Optimization Scheme Based on the Simulated Annealing Algorithm for In situ DNA Microarray Synthesis

Area of Science:

• Biotechnology
• Computational Biology
• Genomics

Background:

• DNA microarray synthesis is crucial for large-scale DNA generation.
• Nucleotide addition order impacts synthesis efficiency and accuracy.
• Optimizing synthesis strategies is essential for cost-effective DNA production.

Purpose of the Study:

• To model DNA microarray in situ synthesis as a Traveling Salesman Problem (TSP).
• To develop an optimized nucleotide addition strategy for improved synthesis.
• To evaluate the performance of computational algorithms in DNA synthesis.

Main Methods:

• Established a mathematical model for in situ microarray synthesis.
• Applied greedy and simulated annealing algorithms to optimize nucleotide order.
• Evaluated performance by comparing synthesis cycles across various array scales.

Main Results:

• Simulated annealing reduced synthesis cycles by up to 40.65% compared to traditional methods.
• At larger scales, cycle reductions ranged from 33.80% to 37.26%.
• Simulated annealing consistently outperformed the greedy algorithm, showing significant efficiency gains.

Conclusions:

• Simulated annealing offers a superior strategy for DNA microarray synthesis.
• This optimization enhances efficiency, reduces material usage, and improves cost-effectiveness.
• The approach benefits applications in gene editing, drug development, and DNA data storage.