Minimum-Cost Synthetic Genome Planning: An Algorithmic Framework.

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. Using both single closely related templates (e.g., bat coronavirus RaTG13) and diverse multisource consensus analyses, our results revealed that conserved regions such as ORF1ab can be reconstructed cost-effectively via sequence reuse. In contrast, highly variable regions such as the S (Spike) gene necessitate expensive