用递归、自顶向下的方法演化模块化遗传调控网络
Evolving Modular Genetic Regulatory Networks with a Recursive, Top-Down Approach
摘要：
能够设计基因调控网络（GRNs）以实现所需的细胞功能是合成生物学的主要目标之一。然而，确定产生期望的时间序列行为的最小GRN是不平凡的。在本文中，我们提出了一个“自上而下”的方法来发展小GRNs，然后使用这些递归引导识别更大，更复杂，模块化的GRNs。我们从相对密集的GRN开始，然后使用差分进化（DE）来进化相互作用系数。当目标动力学行为被发现嵌入在密集的GRN中时，我们缩小搜索的焦点，并开始在每一代结束时积极地修剪多余的相互作用。我们首先表明，该方法可以快速重新发现已知的小GRN的拨动开关和振荡电路。接下来，我们将这些GRN作为不可进化的子网络，用于更复杂的模块化GRN的后续进化。成功的解决方案中发现，在典型的DE，我们截断小的相互作用为零，有或没有相互作用的惩罚项，总是包含许多多余的相互作用。相比之下，通过结合积极的修剪和惩罚项，DE能够在所有测试问题中找到最小或接近最小的GRNs。
英文摘要：
Being able to design genetic regulatory networks (GRNs) to achieve a desired cellular function is one of the main goals of synthetic biology. However, determining minimal GRNs that produce desired time-series behaviors is non-trivial. In this paper, we propose a 'top-down' approach to evolving small GRNs and then use these to recursively boot-strap the identification of larger, more complex, modular GRNs. We start with relatively dense GRNs and then use differential evolution (DE) to evolve interaction coefficients. When the target dynamical behavior is found embedded in a dense GRN, we narrow the focus of the search and begin aggressively pruning out excess interactions at the end of each generation. We first show that the method can quickly rediscover known small GRNs for a toggle switch and an oscillatory circuit. Next we include these GRNs as non-evolvable subnetworks in the subsequent evolution of more complex, modular GRNs. Successful solutions found in canonical DE where we truncated small interactions to zero, with or without an interaction penalty term, invariably contained many excess interactions. In contrast, by incorporating aggressive pruning and the penalty term, the DE was able to find minimal or nearly minimal GRNs in all test problems.
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