Department of Physics, Columbia University, New York, USA.
IET Syst Biol. 2009 Sep;3(5):379-87. doi: 10.1049/iet-syb.2008.0165.
The authors introduce a quantitative measure of the capacity of a small biological network to evolve. The measure is applied to a stochastic description of the experimental setup of Guet et al. (Science 2002, 296, pp. 1466), treating chemical inducers as functional inputs to biochemical networks and the expression of a reporter gene as the functional output. The authors take an information-theoretic approach, allowing the system to set parameters that optimise signal processing ability, thus enumerating each network's highest-fidelity functions. All networks studied are highly evolvable by the measure, meaning that change in function has little dependence on change in parameters. Moreover, 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 completely losing it along the way. This property further underscores the evolvability of the networks.
作者介绍了一种衡量小型生物网络进化能力的定量方法。该方法应用于 Guet 等人的实验设置的随机描述（Science 2002, 296, pp. 1466），将化学诱导物视为生化网络的功能输入，将报告基因的表达视为功能输出。作者采用信息论方法，允许系统设置参数以优化信号处理能力，从而枚举每个网络的最高保真度功能。根据该衡量方法，所有研究的网络都具有高度的可进化性，这意味着功能的变化与参数的变化几乎没有关系。此外，每个网络的功能在参数空间中都由路径连接，信息在这些路径上不会显著降低，这意味着网络可以连续改变其功能，而不会在此过程中完全丢失功能。这一特性进一步强调了网络的可进化性。
