Siegal Mark L, Promislow Daniel E L, Bergman Aviv
Department of Biology, New York University, New York, NY 10003, USA.
Genetica. 2007 Jan;129(1):83-103. doi: 10.1007/s10709-006-0035-0. Epub 2006 Aug 8.
The relationship between the topology of a biological network and its functional or evolutionary properties has attracted much recent interest. It has been suggested that most, if not all, biological networks are 'scale free.' That is, their connections follow power-law distributions, such that there are very few nodes with very many connections and vice versa. The number of target genes of known transcriptional regulators in the yeast, Saccharomyces cerevisiae, appears to follow such a distribution, as do other networks, such as the yeast network of protein-protein interactions. These findings have inspired attempts to draw biological inferences from general properties associated with scale-free network topology. One often cited general property is that, when compromised, highly connected nodes will tend to have a larger effect on network function than sparsely connected nodes. For example, more highly connected proteins are more likely to be lethal when knocked out. However, the correlation between lethality and connectivity is relatively weak, and some highly connected proteins can be removed without noticeable phenotypic effect. Similarly, network topology only weakly predicts the response of gene expression to environmental perturbations. Evolutionary simulations of gene-regulatory networks, presented here, suggest that such weak or non-existent correlations are to be expected, and are likely not due to inadequacy of experimental data. We argue that 'top-down' inferences of biological properties based on simple measures of network topology are of limited utility, and we present simulation results suggesting that much more detailed information about a gene's location in a regulatory network, as well as dynamic gene-expression data, are needed to make more meaningful functional and evolutionary predictions. Specifically, we find in our simulations that: (1) the relationship between a gene's connectivity and its fitness effect upon knockout depends on its equilibrium expression level; (2) correlation between connectivity and genetic variation is virtually non-existent, yet upon independent evolution of networks with identical topologies, some nodes exhibit consistently low or high polymorphism; and (3) certain genes show low polymorphism yet high divergence among independent evolutionary runs. This latter pattern is generally taken as a signature of positive selection, but in our simulations its cause is often neutral coevolution of regulatory inputs to the same gene.
生物网络的拓扑结构与其功能或进化特性之间的关系最近引起了广泛关注。有人提出，大多数（即便不是全部）生物网络都是“无标度”的。也就是说，它们的连接遵循幂律分布，即连接非常多的节点很少，而连接很少的节点很多。酿酒酵母中已知转录调节因子的靶基因数量似乎遵循这种分布，其他网络，如蛋白质 - 蛋白质相互作用的酵母网络也是如此。这些发现激发了人们从与无标度网络拓扑相关的一般特性中得出生物学推论的尝试。一个经常被引用的一般特性是，当受到损害时，高度连接的节点对网络功能的影响往往比连接稀疏的节点更大。例如，连接性更高的蛋白质在被敲除时更有可能是致死的。然而，致死性与连接性之间的相关性相对较弱，一些高度连接的蛋白质被去除后可能没有明显的表型效应。同样，网络拓扑结构只能微弱地预测基因表达对环境扰动的反应。本文给出的基因调控网络的进化模拟表明，这种微弱或不存在的相关性是可以预期的，而且可能不是由于实验数据不足。我们认为，基于网络拓扑结构的简单度量对生物学特性进行“自上而下”的推论效用有限，并且我们给出的模拟结果表明，需要更多关于基因在调控网络中的位置的详细信息以及动态基因表达数据，才能做出更有意义的功能和进化预测。具体而言，我们在模拟中发现：（1）基因的连接性与其敲除后的适应性效应之间的关系取决于其平衡表达水平；（2）连接性与遗传变异之间几乎不存在相关性，但在具有相同拓扑结构的网络独立进化时，一些节点始终表现出低多态性或高多态性；（3）某些基因在独立进化过程中表现出低多态性但高度分化。后一种模式通常被视为正选择的标志，但在我们的模拟中，其原因往往是对同一基因的调控输入的中性共同进化。
