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arXiv · 2509.06234

Minimum-Cost Synthetic Genome Planning: An Algorithmic Framework

Michail Patsakis·Ioannis Mouratidis·Ilias Georgakopoulos-Soares
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Abstract

As synthetic genomics scales toward the construction of increasingly larger genomes, computational strategies are needed to address technical feasibility. We introduce an algorithmic framework for the Minimum-Cost Synthetic Genome Planning problem, aiming to identify the most cost-effective strategy to assemble a target genome from a source genome through a combination of reuse, synthesis, and join operations. By comparing dynamic programming and greedy heuristic strategies under diverse cost regimes, we demonstrate how algorithmic choices influence the cost-efficiency of large-scale genome construction. In parallel, solving the Minimum-Cost Synthetic Genome Planning problem can help us better understand genome architecture and evolution. We applied our framework in case studies on viral genomes, including SARS-CoV-2, to examine how source-target genome similarity shapes construction costs. Our analyses revealed that conserved regions such as ORF1ab can be reconstructed cost-effectively from related templates, while highly variable regions such as the S (spike) gene are more reliant on DNA synthesis, highlighting the biological and economic trade-offs of genome design.

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Michail Patsakis, Ioannis Mouratidis, Ilias Georgakopoulos-Soares. 2025-09-07. Minimum-Cost Synthetic Genome Planning: An Algorithmic Framework. https://arxiv.org/abs/2509.06234

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