硫代谢的基因型网络、创新和稳健性
Genotype networks, innovation, and robustness in sulfur metabolism
摘要：
代谢网络是一个复杂的系统，它包括数百个化学反应，这些反应从生物体环境中的化学物质合成生物质分子。任何一个生物体的代谢网络都是由代谢基因型编码的，代谢基因型由一组酶编码基因定义，这些基因的产物催化网络的反应。每个代谢基因型具有代谢表型，例如在化学元素和能量的不同来源的光谱上合成生物质的能力。硫代谢是研究代谢网络演化的一个很有吸引力的方面，因为它涉及的反应比碳代谢少得多。具体来说，我们研究所有可能的代谢基因型的空间的属性，并分析不同数量的硫源上可行的随机代谢基因型的属性。我们发现，具有相同表型的代谢基因型形成大的连接基因型网络，通过代谢基因型空间延伸很远。它们通过这个空间到达的距离是这种网络中超必要反应数量的线性函数，即在具有相同表型的所有网络中发生的反应数量。我们发现，任何基因型网络的不同社区都有非常不同的新表型，代谢创新可以在新的硫源上维持生命。我们还分析了进化群体的代谢网络，以探索新的代谢表型的能力。基因型网络的存在促进了这种能力，因为这些网络的不同邻域包含非常不同的新表型。与大分子相比，表型稳健性可能有助于表型创新，我们表明，在这里获得新表型的能力并不单调增加稳健性。
英文摘要：
Metabolic networks are complex systems that comprise hundreds of chemical reactions which synthesize biomass molecules from chemicals in an organism's environment. The metabolic network of any one organism is encoded by a metabolic genotype, defined by a set of enzyme-coding genes whose products catalyze the network's reactions. Each metabolic genotype has a metabolic phenotype, such as the ability to synthesize biomass on a spectrum of different sources of chemical elements and energy. We here focus on sulfur metabolism, which is attractive to study the evolution of metabolic networks, because it involves many fewer reactions than carbon metabolism. Specifically, we study properties of the space of all possible metabolic genotypes, and analyze properties of random metabolic genotypes that are viable on different numbers of sulfur sources. We show that metabolic genotypes with the same phenotype form large connected genotype networks that extend far through metabolic genotype space. How far they reach through this space is a linear function of the number of super-essential reactions in such networks, the number of reactions that occur in all networks with the same phenotype. We show that different neighborhoods of any genotype network harbor very different novel phenotypes, metabolic innovations that can sustain life on novel sulfur sources. We also analyze the ability of evolving populations of metabolic networks to explore novel metabolic phenotypes. This ability is facilitated by the existence of genotype networks, because different neighborhoods of these networks contain very different novel phenotypes. In contrast to macromolecules, where phenotypic robustness may facilitate phenotypic innovation, we show that here the ability to access novel phenotypes does not monotonically increase with robustness.
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