A universal biomolecular integral feedback controller for robust perfect adaptation
A universal biomolecular integral feedback controller for robust perfect adaptation
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DOI：
10.1038/s41586-019-1321-1
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发表时间：
2019-06-27
期刊：
影响因子：
64.8
通讯作者：
Khammash, Mustafa
中科院分区：
文献类型：
--
作者：
Aoki, Stephanie K.;Lillacci, Gabriele;Khammash, Mustafa
稳态是生物学中一个反复出现的主题，它确保了受调节的变量能够稳健地（在某些系统中，完全地）适应环境的扰动。这种鲁棒的完美自适应功能是通过使用积分控制在自然电路中实现的，积分控制是一种负反馈策略，它执行数学积分以实现结构鲁棒调节（1，2）。尽管它的好处，在活细胞中的积分反馈的合成实现仍然难以捉摸，由于所需的生物计算的复杂性。在这里，我们从数学上证明，存在一个单一的基本生物分子控制器拓扑结构（3），它实现积分反馈，并在具有噪声动态的任意细胞内网络中实现鲁棒的完美适应。对于单个细胞的总体平均值和时间平均值，都保证了这种适应性。在这个概念的基础上，我们在活细胞中遗传工程合成积分反馈控制器（4），并证明其可调性和适应性。大肠杆菌的生长率控制应用显示了我们的积分控制器提供鲁棒性的内在能力，并强调了其作为调节不确定网络中生物变量的通用控制器的潜在用途。我们的研究结果提供了概念和实用的工具，在该地区的网络遗传学（3，5），工程合成控制器，引导动态的生命系统（3-9）。
Homeostasis is a recurring theme in biology that ensures that regulated variables robustly-and in some systems, completely-adapt to environmental perturbations. This robust perfect adaptation feature is achieved in natural circuits by using integral control, a negative feedback strategy that performs mathematical integration to achieve structurally robust regulation(1,2). Despite its benefits, the synthetic realization of integral feedback in living cells has remained elusive owing to the complexity of the required biological computations. Here we prove mathematically that there is a single fundamental biomolecular controller topology(3) that realizes integral feedback and achieves robust perfect adaptation in arbitrary intracellular networks with noisy dynamics. This adaptation property is guaranteed both for the population-average and for the time-average of single cells. On the basis of this concept, we genetically engineer a synthetic integral feedback controller in living cells(4) and demonstrate its tunability and adaptation properties. A growth-rate control application in Escherichia coli shows the intrinsic capacity of our integral controller to deliver robustness and highlights its potential use as a versatile controller for regulation of biological variables in uncertain networks. Our results provide conceptual and practical tools in the area of cybergenetics(3,5), for engineering synthetic controllers that steer the dynamics of living systems(3-9).