Department of Computer Science, University of Oxford, Oxford, United Kingdom.
Randall Division of Cell and Molecular Biophysics and Institute of Mathematical and Molecular Biomedicine, King's College London, London, United Kingdom; Pázmány Péter Catholic University, Budapest, Hungary.
Biophys J. 2018 Jun 19;114(12):3000-3011. doi: 10.1016/j.bpj.2018.05.009.
Living systems are inherently stochastic and operate in a noisy environment, yet despite all these uncertainties, they perform their functions in a surprisingly reliable way. The biochemical mechanisms used by natural systems to tolerate and control noise are still not fully understood, and this issue also limits our capacity to engineer reliable, quantitative synthetic biological circuits. We study how representative models of biochemical systems propagate and attenuate noise, accounting for intrinsic as well as extrinsic noise. We investigate three molecular noise-filtering mechanisms, study their noise-reduction capabilities and limitations, and show that nonlinear dynamics such as complex formation are necessary for efficient noise reduction. We further suggest that the derived molecular filters are widespread in gene expression and regulation and, particularly, that microRNAs can serve as such noise filters. To our knowledge, our results provide new insight into how biochemical networks control noise and could be useful to build robust synthetic circuits.
生命系统本质上是随机的，并且在嘈杂的环境中运行，但尽管存在所有这些不确定性，它们仍以惊人的可靠方式执行其功能。天然系统用于耐受和控制噪声的生化机制尚不完全清楚，这个问题也限制了我们设计可靠、定量的合成生物电路的能力。我们研究了生化系统的代表性模型如何传播和衰减噪声，同时考虑了内在噪声和外在噪声。我们研究了三种分子噪声过滤机制，研究了它们的降噪能力和局限性，并表明复杂形成等非线性动力学对于有效降噪是必要的。我们进一步表明，所得到的分子滤波器在基因表达和调控中广泛存在，特别是 microRNAs 可以作为这种噪声滤波器。据我们所知，我们的结果为生化网络如何控制噪声提供了新的见解，并可能有助于构建稳健的合成电路。
