A trade-off relationship between energetic cost and entropic cost for in vitro evolution

作者：Aita Takuyo^*

来源：Biosystems, 2010, 101(3): 194-199.

DOI：10.1016/j.biosystems.2010.07.003

• Artificial evolution
• Fitness landscape
• Optimization
• Information
• Search algorithm
• Sequence space

In this paper, we consider two complementary cost functions for the landscape exploring processes to obtain the global optimum sequence through in vitro evolution protocol: one is the entropic cost C(etp), which is based on the deviation from the uniformity of a mutant distribution in sequence space, and the other is the energetic cost C(eng), which is based on the total number of sequences to be generated and evaluated. Based on a prior knowledge about the structure of a given fitness landscapes, the conductor of the experiment can think up the efficient search algorithm (ESA), which requires the minimum number of points (=sequences) to be searched up to the global optimum. For five typical fitness landscapes, we considered their respective (putative) ESA, C(etp)* and C(eng)* based on the ESA. As a result, we found a trade-off relationship between C(etp)* and C(eng)* for every case, that is, C(eng)* + C(etp)* is approximately equal to the logarithm of the volume of the sequence space. C(etp)* and C(eng)* are interpreted in terms of the information-theoretic concepts.

• 出版日期2010-9