Abstract The problem of choosing an optimal codon sequence arises when synthetic protein-coding genes are added to cloning vectors for expression within a non-native host organism: to maximize yield, the chosen codons should have a high frequency in the host genome, but particular nucleotide bases sequences (called “motifs”) should be avoided or, instead, included. Dynamic programming (DP) has successfully been used in previous approaches to this problem. However, DP has a computational limit, especially when long motifs are forbidden, and does not allow control of motif positioning and combination. We reformulate the problem as an integer linear program (IP) and show that, with the same computational resources, one can easily solve problems with much more nucleotide bases and much longer forbidden/desired motifs than with DP. Moreover, IP (i) offers more flexibility than DP to treat constraints/objectives of different nature, and (ii) can efficiently deal with newly discovered critical motifs by dynamically re-optimizing additional variables and mathematical constraints.

中文翻译：

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通过 0-1 线性规划优化密码子

摘要 当将合成蛋白质编码基因添加到克隆载体以在非天然宿主生物体内表达时，会出现选择最佳密码子序列的问题：为了最大化产量，选择的密码子应该在宿主基因组中具有高频率，但特别是应避免或包含核苷酸碱基序列（称为“基序”）。动态规划 (DP) 已成功用于解决此问题的先前方法。然而，DP 有一个计算限制，特别是当长模体被禁止时，并且不允许控制模体定位和组合。我们将问题重新表述为整数线性程序 (IP)，并表明，使用相同的计算资源，与 DP 相比，可以轻松解决具有更多核苷酸碱基和更长禁止/所需基序的问题。而且，