Systems Biotechnology, Faculty of Mechanical Engineering, Technical University of Munich, Garching, Germany.
PLoS One. 2018 Mar 26;13(3):e0194779. doi: 10.1371/journal.pone.0194779. eCollection 2018.
Random fluctuations in the amount of cellular components like mRNA and protein molecules are inevitable due to the stochastic and discrete nature of biochemical reactions. If large enough, this so-called "cellular noise" can lead to random transitions between the expression states of a multistable genetic circuit. That way, heterogeneity within isogenic populations is created. Our aim is to understand which dynamical features of a simple autoregulatory system determine its intrinsic noise level, and how they can be modified in order to regulate state-transitions. To that end, novel mathematical methods for the state-dependent characterization and prediction of noise in multistable systems are developed. First, we introduce the hybrid LNA, a modified version of the Linear Noise Approximation. It yields good predictions on variances of mRNA and protein fluctuations, even for reaction systems comprising low-copy-number components (e.g. mRNA) and highly nonlinear reaction rates. Furthermore, the temporal structure of fluctuations and the skewness of the protein distribution are characterized via state-dependent protein burst sizes and burst frequencies. Based on this mathematical framework, we develop graphical methods which support the intuitive design of regulatory circuits with a desired noise pattern. The methods are then used to predict how overall noise in the system can be adapted, and how state-specific noise modifications are possible that allow, e.g., the generation of unidirectional transitions. Our considerations are validated by stochastic simulations. This way, a design of genetic circuits is possible that takes population heterogeneity into account and is valuable in applications of synthetic biology and biotechnology. Moreover, natural phenomena like the bimodal development of genetic competence can be studied.
由于生化反应的随机性和离散性，细胞成分（如 mRNA 和蛋白质分子）的数量会不可避免地出现随机波动。如果这种波动足够大，就会导致多稳态遗传电路的表达状态发生随机转换，所谓的“细胞噪声”就会出现。这样，同基因群体内部就会产生异质性。我们的目标是了解简单的自调节系统的哪些动态特征决定了其固有噪声水平，以及如何修改这些特征以调节状态转换。为此，开发了用于多稳态系统中依赖于状态的噪声特征描述和预测的新数学方法。首先，我们引入了混合 LNA，这是线性噪声逼近的一种改进版本。即使对于包含低拷贝数组件（例如 mRNA）和高度非线性反应速率的反应系统，它也能对 mRNA 和蛋白质波动的方差进行很好的预测。此外，通过依赖于状态的蛋白质爆发大小和爆发频率来描述波动的时间结构和蛋白质分布的偏度。基于这个数学框架，我们开发了图形方法，这些方法支持具有所需噪声模式的调节回路的直观设计。然后，我们使用这些方法来预测如何适应系统中的整体噪声，以及如何进行特定状态的噪声修改，例如，产生单向转换。我们的考虑通过随机模拟进行了验证。通过这种方式，可以设计出考虑群体异质性的遗传回路，这在合成生物学和生物技术的应用中很有价值。此外，还可以研究自然现象，如遗传能力的双峰发育。
