Proportional and derivative controllers for buffering noisy gene expression
Proportional and derivative controllers for buffering noisy gene expression
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DOI：
10.1109/cdc40024.2019.9030175
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发表时间：
2019-12
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影响因子：
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通讯作者：
Saurabh Modi;Supravat Dey;Abhyudai Singh
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文献类型：
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作者：
Saurabh Modi;Supravat Dey;Abhyudai Singh
在单个细胞内，由于低拷贝数和生化过程的固有概率性质，蛋白质群体计数受到分子噪声的影响。蛋白质水平的这种随机波动严重影响细胞内生物网络的功能，毫不奇怪，细胞编码多种调节机制来缓冲噪音。我们调查的有效性的比例和导数为基础的反馈控制器，以抑制蛋白质计数波动来自两个噪声源：突发性表达的蛋白质，和蛋白质合成的外部干扰。生化反应的比例和微分控制器的功能的设计进行了讨论，并相应的闭环系统进行了分析，随机控制器的实现。我们的研究结果表明，比例控制器是有效的缓冲蛋白质拷贝数的波动，从两个噪声源，但这种噪声抑制的代价是降低静态灵敏度的输出到输入信号。接下来，我们讨论了一个耦合前馈反馈生化电路的设计，近似功能作为一个微分控制器。分析表明，该微分控制器有效地缓冲了突发随机表达式引起的输出波动，同时保持了开环系统的静态输入输出灵敏度。正如预期的那样，微分控制器在抑制外部干扰方面表现不佳。总之，这项研究提供了一个系统的随机分析的生化控制器，并铺平了道路，他们的合成设计和实施，以尽量减少有害的基因产物水平的波动。
Inside individual cells, protein population counts are subject to molecular noise due to low copy numbers and the inherent probabilistic nature of biochemical processes. Such random fluctuations in the level of a protein critically impact functioning of intracellular biological networks, and not surprisingly, cells encode diverse regulatory mechanisms to buffer noise. We investigate the effectiveness of proportional and derivative-based feedback controllers to suppress protein count fluctuations originating from two noise sources: bursty expression of the protein, and external disturbance in protein synthesis. Designs of biochemical reactions that function as proportional and derivative controllers are discussed, and the corresponding closed-loop system is analyzed for stochastic controller realizations. Our results show that proportional controllers are effective in buffering protein copy number fluctuations from both noise sources, but this noise suppression comes at the cost of reduced static sensitivity of the output to the input signal. Next, we discuss the design of a coupled feedforward-feedback biochemical circuit that approximately functions as a derivate controller. Analysis reveals that this derivative controller effectively buffers output fluctuations from bursty stochastic expression, while maintaining the static inputoutput sensitivity of the open-loop system. As expected, the derivative controller performs poorly in terms of rejecting external disturbances. In summary, this study provides a systematic stochastic analysis of biochemical controllers, and paves the way for their synthetic design and implementation to minimize deleterious fluctuations in gene product levels.