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Simulated Evolution of Mass Conserving Reaction Networks

机译：质量守恒反应网络的模拟演化

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With the rise of systems biology, the systematic analysis and construction of behavioral mechanisms in both natural and artificial biochemical networks has become a vital part of understanding and predicting the inner workings of intracellu-lar signaling networks. As a modeling platform, artificial chemistries are commonly adopted to study and construct artificial reaction network motifs that exhibit complex computational behaviors. Here, we present a genetic algorithm to evolve networks that can compute elementary mathematical functions by transforming initial input molecules into the steady state concentrations of output molecules. More specifically, the proposed algorithm implicitly guarantees mass conservation through an atom based description of the molecules and reaction networks. We discuss the adopted approach for the artificial evolution of these chemical networks, evolve networks to compute the square root function. Finally, we provide an extensive deterministic and stochastic analysis of a core square root network motif present in these resulting networks, confirming that the motif is indeed capable of computing the square root function.
机译：随着系统生物学的兴起，对自然和人工生化网络的系统分析和行为机制的构建已成为理解和预测细胞内信号网络内部工作的重要组成部分。作为建模平台，通常使用人造化学来研究和构建表现出复杂计算行为的人造反应网络主题。在这里，我们提出了一种遗传算法来演化网络，该网络可以通过将初始输入分子转换为输出分子的稳态浓度来计算基本数学函数。更具体地说，所提出的算法通过对分子和反应网络进行基于原子的描述来隐式保证质量守恒。我们讨论了对这些化学网络进行人工进化所采用的方法，并通过进化网络来计算平方根函数。最后，我们对这些结果网络中存在的核心平方根网络主题进行了广泛的确定性和随机性分析，确认该主题确实能够计算平方根函数。

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