量化小型生物网络中的可进化性
Quantifying evolvability in small biological networks
摘要：
我们引入了一个小的生物网络的能力，以发展的定量措施。我们应用我们的措施，以随机描述的实验装置的Guet等人（科学296：1466，2002），治疗化学诱导剂作为功能输入的生化网络和报告基因的表达作为功能输出。我们采用信息论方法，允许系统设置优化信号处理能力的参数，从而枚举每个网络的最高保真功能。我们发现，通过我们的测量，所有研究的网络都是高度可演化的，这意味着函数的变化几乎不依赖于参数的变化。此外，我们发现每个网络的功能都是通过参数空间中的路径连接的，沿着这些路径，信息不会显着降低，这意味着网络可以不断改变其功能而不会丢失它。这一特性进一步强调了网络的可演化性。
英文摘要：
We introduce a quantitative measure of the capacity of a small biological network to evolve. We apply our measure to a stochastic description of the experimental setup of Guet et al. (Science 296:1466, 2002), treating chemical inducers as functional inputs to biochemical networks and the expression of a reporter gene as the functional output. We take an information-theoretic approach, allowing the system to set parameters that optimize signal processing ability, thus enumerating each network's highest-fidelity functions. We find that all networks studied are highly evolvable by our measure, meaning that change in function has little dependence on change in parameters. Moreover, we find that each network's functions are connected by paths in the parameter space along which information is not significantly lowered, meaning a network may continuously change its functionality without losing it along the way. This property further underscores the evolvability of the networks.
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