Models of gene regulatory networks (GRN) have proven useful for understanding many aspects of the highly complex behavior of biological control networks. Randomly generated non-Boolean networks were used in experimental simulations to generate data on dynamic phenotypes as a function of several genotypic parameters. We found that predictive relationships between some phenotypes and quantitative genotypic parameters such as number of network genes, interaction density, and initial condition could be derived depending on the strength of the topological (positional) genotype on specific phenotypes. We quantitated the strength of the topological genotype effect (TGE) on a number of phenotypes in multi-gene networks. For phenotypes with a low influence of topological genotype, derived and empirical relationships using quantitative genotype parameters were accurate in phenotypic outcomes. We found a number of dynamic network properties, including oscillation behaviors, that were largely dependent on genotype topology, and for which no such general quantitative relationships were determinable. It remains to be determined if these results are applicable to biological gene regulatory networks.

中文翻译：

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基因型成分作为模型基因调控网络中表型的预测因子

基因调控网络 (GRN) 模型已被证明有助于理解生物控制网络高度复杂行为的许多方面。在实验模拟中使用随机生成的非布尔网络生成动态表型数据，作为几个基因型参数的函数。我们发现，某些表型与定量基因型参数（例如网络基因数量、相互作用密度和初始条件）之间的预测关系可以根据拓扑（位置）基因型对特定表型的强度推导出来。我们量化了多基因网络中许多表型的拓扑基因型效应 (TGE) 的强度。对于拓扑基因型影响较小的表型，使用定量基因型参数推导出的和经验的关系在表型结果中是准确的。我们发现了许多动态网络特性，包括振荡行为，它们在很大程度上取决于基因型拓扑，并且无法确定这种一般的定量关系。这些结果是否适用于生物基因调控网络还有待确定。