Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks
Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks
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DOI：
10.1098/rsif.2018.0079
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发表时间：
2018-06-01
影响因子：
3.9
通讯作者：
Khammash, Mustafa
中科院分区：
文献类型：
--
作者：
Briat, Corentin;Gupta, Ankit;Khammash, Mustafa
细胞调节和适应其内部状态以应对不可预测的环境变化的能力被称为稳态，这种能力对于细胞的生存和正常功能至关重要。了解细胞如何实现动态平衡，尽管它们的动力学中存在固有的噪声或随机性，这对系统和合成生物学都非常重要。在这种情况下，一个显着的发展是所提出的对偶积分反馈（AIF）基序，这是在自然系统中发现的，并被称为确保强大的完美适应的平均动力学的一个给定的分子物种参与一个复杂的随机生物分子反应网络。从应用的角度来看，该基序的一个缺点是，当与组成型（即开环）控制策略相比时，其通常导致增加的细胞间异质性或变化。在本文中，我们的目标是表明，这种性能恶化可以反击相结合的AIF主题和负反馈策略。使用定制的矩封闭方法，我们推导出的受控网络，证明了增加负反馈的强度确实可以降低方差的平稳方差的近似表达式，有时甚至低于其本构水平。数值结果验证了这些结果的准确性，我们通过考虑三个生物分子网络与两种类型的负反馈策略来说明它们。我们的计算分析表明，有一个平衡的平均轨迹的稳定时间的速度和受控物种的平稳方差之间，即较小的方差与较大的稳定时间。
The ability of a cell to regulate and adapt its internal state in response to unpredictable environmental changes is called homeostasis and this ability is crucial for the cell's survival and proper functioning. Understanding how cells can achieve homeostasis, despite the intrinsic noise or randomness in their dynamics, is fundamentally important for both systems and synthetic biology. In this context, a significant development is the proposed antithetic integral feedback (AIF) motif, which is found in natural systems, and is known to ensure robust perfect adaptation for the mean dynamics of a given molecular species involved in a complex stochastic biomolecular reaction network. From the standpoint of applications, one drawback of this motif is that it often leads to an increased cell-to-cell heterogeneity or variance when compared to a constitutive (i.e. open-loop) control strategy. Our goal in this paper is to show that this performance deterioration can be countered by combining the AIF motif and a negative feedback strategy. Using a tailored moment closure method, we derive approximate expressions for the stationary variance for the controlled network that demonstrate that increasing the strength of the negative feedback can indeed decrease the variance, sometimes even below its constitutive level. Numerical results verify the accuracy of these results and we illustrate them by considering three biomolecular networks with two types of negative feedback strategies. Our computational analysis indicates that there is a trade-off between the speed of the settling-time of the mean trajectories and the stationary variance of the controlled species; i.e. smaller variance is associated with larger settling-time.