Pfeiffer Thomas, Schuster Stefan
Computational Laboratory, ETH Zurich, CH-8092 Zurich, Switzerland.
Trends Biochem Sci. 2005 Jan;30(1):20-5. doi: 10.1016/j.tibs.2004.11.006.
Evolutionary optimization has been successfully used to increase our understanding of key properties of biochemical systems. Traditional optimization is, however, often insufficient for gaining deeper insights into the evolution of such systems because usually there is a mutual relationship between the properties optimized by evolution and the properties of the environment. Thus, by evolving towards optimal properties, organisms change their environment, which in turn alters the optimum. Evolutionary game theory provides an appropriate framework for analyzing evolution in such 'dynamic fitness landscapes'. We therefore argue that it is a promising approach to studying the evolution of biochemical systems. Indeed, recent studies have applied evolutionary game theory to key issues in the evolution of energy metabolism.
进化优化已成功用于增进我们对生化系统关键特性的理解。然而,传统优化通常不足以更深入地洞察此类系统的进化,因为进化所优化的特性与环境特性之间通常存在相互关系。因此,生物体在朝着最优特性进化的过程中会改变其环境,而这反过来又会改变最优状态。进化博弈论为分析此类“动态适应度景观”中的进化提供了一个合适的框架。所以我们认为,它是研究生化系统进化的一种很有前景的方法。事实上,最近的研究已将进化博弈论应用于能量代谢进化中的关键问题。