Centrum voor Wiskunde en Informatica, PO Box 94079, NL-1090 GB Amsterdam, The Netherlands.
IEEE/ACM Trans Comput Biol Bioinform. 2011 Sep-Oct;8(5):1358-72. doi: 10.1109/TCBB.2011.40.
The genetic code is known to have a high level of error robustness and has been shown to be very error robust compared to randomly selected codes, but to be significantly less error robust than a certain code found by a heuristic algorithm. We formulate this optimization problem as a Quadratic Assignment Problem and use this to formally verify that the code found by the heuristic algorithm is the global optimum. We also argue that it is strongly misleading to compare the genetic code only with codes sampled from the fixed block model, because the real code space is orders of magnitude larger. We thus enlarge the space from which random codes can be sampled from approximately 2.433 × 10(18) codes to approximately 5.908 × 10(45) codes. We do this by leaving the fixed block model, and using the wobble rules to formulate the characteristics acceptable for a genetic code. By relaxing more constraints, three larger spaces are also constructed. Using a modified error function, the genetic code is found to be more error robust compared to a background of randomly generated codes with increasing space size. We point out that these results do not necessarily imply that the code was optimized during evolution for error minimization, but that other mechanisms could be the reason for this error robustness.
遗传密码具有高度的容错性，与随机选择的密码相比，它的容错性非常强，但与启发式算法发现的特定代码相比，它的容错性要差得多。我们将这个优化问题表述为二次分配问题，并利用它正式验证启发式算法发现的代码是全局最优的。我们还认为，仅将遗传密码与从固定块模型中抽样的代码进行比较是非常具有误导性的，因为实际的代码空间要大几个数量级。因此，我们将随机抽样代码的空间从大约 2.433×10(18)个代码扩大到大约 5.908×10(45)个代码。我们通过放弃固定块模型，并使用摆动规则来制定遗传密码可接受的特征来实现这一点。通过放宽更多的约束，还构建了三个更大的空间。使用修改后的错误函数，与随着空间大小增加而随机生成的代码背景相比，遗传密码被发现具有更高的容错性。我们指出，这些结果并不一定意味着代码在进化过程中是为了最小化错误而进行优化的，而是可能有其他机制导致了这种容错性。
