代谢网络结构中的算术简单性特征
Signatures of arithmetic simplicity in metabolic network architecture
摘要：
代谢网络在活细胞中执行一些最基本的功能，包括能量转导和构件生物合成。虽然这些是生命系统中最具特征的网络，但了解它们的进化历史和复杂的布线构成了生物学中最迷人的开放问题之一，与生命起源本身的谜团密切相关。新陈代谢的进化是否服从于普遍的原则，超越了不可预测的多重历史事件的积累？在这里，我们寻找这样的原则，适用于人工化学宇宙的一些方法，为研究基因组规模的细胞代谢模型。特别是，我们使用代谢通量约束为基础的模型来彻底搜索人工化学途径，可以最佳地执行一系列的基本代谢功能。尽管所采用的模型简单，我们发现，随后的途径显示出令人惊讶的丰富的属性，包括存在的自催化循环和分层模块，普遍优选的代谢产物和反应的外观，以及作为输入/输出分子大小的函数的途径长度的对数趋势。这些属性中的一些可以通过分析推导出来，借用以前在密码学中使用的方法。此外，通过将生化网络映射到简化的碳原子反应骨架上，我们发现人工化学模型预测的几个属性适用于真实的代谢网络。这些发现表明，优化原则和算术简单性可能隐藏在生化复杂性的某些方面。
英文摘要：
Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that several of the properties predicted by the artificial chemistry model hold for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity.
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