Mazurie Aurélien, Bonchev Danail, Schwikowski Benno, Buck Gregory A
Institut Pasteur, Systems Biology Lab, Department of Genomes and Genetics, F-75015 Paris, France.
BMC Syst Biol. 2010 May 11;4:59. doi: 10.1186/1752-0509-4-59.
Comparison of metabolic networks across species is a key to understanding how evolutionary pressures shape these networks. By selecting taxa representative of different lineages or lifestyles and using a comprehensive set of descriptors of the structure and complexity of their metabolic networks, one can highlight both qualitative and quantitative differences in the metabolic organization of species subject to distinct evolutionary paths or environmental constraints.
We used a novel representation of metabolic networks, termed network of interacting pathways or NIP, to focus on the modular, high-level organization of the metabolic capabilities of the cell. Using machine learning techniques we identified the most relevant aspects of cellular organization that change under evolutionary pressures. We considered the transitions from prokarya to eukarya (with a focus on the transitions among the archaea, bacteria and eukarya), from unicellular to multicellular eukarya, from free living to host-associated bacteria, from anaerobic to aerobic, as well as the acquisition of cell motility or growth in an environment of various levels of salinity or temperature. Intuitively, we expect organisms with more complex lifestyles to have more complex and robust metabolic networks. Here we demonstrate for the first time that such organisms are not only characterized by larger, denser networks of metabolic pathways but also have more efficiently organized cross communications, as revealed by subtle changes in network topology. These changes are unevenly distributed among metabolic pathways, with specific categories of pathways being promoted to more central locations as an answer to environmental constraints.
Combining methods from graph theory and machine learning, we have shown here that evolutionary pressures not only affects gene and protein sequences, but also specific details of the complex wiring of functional modules in the cell. This approach allows the identification and quantification of those changes, and provides an overview of the evolution of intracellular systems.
跨物种代谢网络的比较是理解进化压力如何塑造这些网络的关键。通过选择代表不同谱系或生活方式的分类群，并使用一套全面的代谢网络结构和复杂性描述符，人们可以突出受到不同进化路径或环境限制的物种在代谢组织上的定性和定量差异。
我们使用了一种新颖的代谢网络表示方法，称为相互作用途径网络（NIP），来关注细胞代谢能力的模块化、高级组织。我们使用机器学习技术确定了在进化压力下细胞组织中最相关的变化方面。我们考虑了从原核生物到真核生物的转变（重点是古细菌、细菌和真核生物之间的转变）、从单细胞真核生物到多细胞真核生物的转变、从自由生活细菌到宿主相关细菌的转变、从厌氧到需氧的转变，以及在不同盐度或温度环境中细胞运动性或生长的获得。直观地说，我们预计生活方式更复杂的生物体具有更复杂和强大的代谢网络。在这里，我们首次证明，这些生物体不仅以更大、更密集的代谢途径网络为特征，而且还具有更高效组织的交叉通信，这通过网络拓扑结构的细微变化得以揭示。这些变化在代谢途径中分布不均，特定类别的途径被提升到更中心的位置以应对环境限制。
结合图论和机器学习方法，我们在此表明进化压力不仅影响基因和蛋白质序列，还影响细胞中功能模块复杂连接的特定细节。这种方法允许识别和量化这些变化，并提供细胞内系统进化的概述。
