Balancing Cell Populations Endowed with a Synthetic Toggle Switch via Adaptive Pulsatile Feedback Control
Balancing Cell Populations Endowed with a Synthetic Toggle Switch via Adaptive Pulsatile Feedback Control
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DOI：
10.1021/acssynbio.9b00464
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发表时间：
2020-04-17
影响因子：
4.7
通讯作者：
di Bernardo, Mario
中科院分区：
文献类型：
--
作者：
Guarino, Agostino;Fiore, Davide;di Bernardo, Mario
控制被赋予基因触发开关的细胞的问题最近被强调为合成生物学中的一个基准问题。已经证明，精心选择的周期性强迫可以平衡处于未分化状态的此类细胞的数量。然而，这些控制策略的有效性可能会受到生物系统中常见的随机扰动和不确定性的存在的阻碍，因此是不稳健的。在这里，我们提出使用反馈控制策略，通过实时选择影响拨动开关行为的脉动输入的幅度和占空比来增强平衡动作的稳健性和性能。通过计算机实验和真实的基于代理的模拟，我们展示了所提出的策略的有效性，即使在存在不确定性、随机效应、细胞生长和诱导剂扩散的情况下也是如此。在这样做的过程中，我们证实了文献中关于使用脉动强迫输入时种群一致性的观察结果，但与过去提出的相反，我们利用反馈控制技术赋予平衡策略前所未有的稳健性和稳定性。我们在硅胶实验中比较了不同的外部控制方案，并从活体实施的角度展示了它们的优势和局限性。
The problem of controlling cells endowed with a genetic toggle switch has been recently highlighted as a benchmark problem in synthetic biology. It has been shown that a carefully selected periodic forcing can balance a population of such cells in an undifferentiated state. The effectiveness of these control strategies, however, can be hindered by the presence of stochastic perturbations and uncertainties typically observed in biological systems and is therefore not robust. Here, we propose the use of feedback control strategies to enhance robustness and performance of the balancing action by selecting in real-time both the amplitude and the duty-cycle of the pulsatile inputs affecting the toggle switch behavior. We show, via in silico experiments and realistic agent-based simulations, the effectiveness of the proposed strategies even in the presence of uncertainties, stochastic effects, cell growth, and inducer diffusion. In so doing, we confirm previous observations made in the literature about coherence of the population when pulsatile forcing inputs are used, but, contrary to what was proposed in the past, we leverage feedback control techniques to endow the balancing strategy with unprecedented robustness and stability properties. We compare via in silico experiments different external control solutions and show their advantages and limitations from an in vivo implementation viewpoint.