Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, UK.
School of Mathematics, University of Edinburgh, Maxwell Building, Peter Guthrie Tait Road, Edinburgh, UK.
J R Soc Interface. 2018 Jul;15(144). doi: 10.1098/rsif.2018.0199.
Synthetic biology is a growing interdisciplinary field, with far-reaching applications, which aims to design biochemical systems that behave in a desired manner. With the advancement in nucleic-acid-based technology in general, and strand-displacement DNA computing in particular, a large class of abstract biochemical networks may be physically realized using nucleic acids. Methods for systematic design of the abstract systems with prescribed behaviours have been predominantly developed at the (less-detailed) deterministic level. However, stochastic effects, neglected at the deterministic level, are increasingly found to play an important role in biochemistry. In such circumstances, methods for controlling the intrinsic noise in the system are necessary for a successful network design at the (more-detailed) stochastic level. To bridge the gap, the for designing biochemical networks is developed in this paper. The algorithm structurally modifies any given reaction network under mass-action kinetics, in such a way that (i) controllable state-dependent noise is introduced into the stochastic dynamics, while (ii) the deterministic dynamics are preserved. The capabilities of the algorithm are demonstrated on a production-decay reaction system, and on an exotic system displaying bistability. For the production-decay system, it is shown that the algorithm may be used to redesign the network to achieve noise-induced multistability. For the exotic system, the algorithm is used to redesign the network to control the stochastic switching, and achieve noise-induced oscillations.
合成生物学是一个日益发展的跨学科领域，具有广泛的应用，旨在设计出以期望方式表现的生化系统。随着核酸技术的进步，特别是链置换 DNA 计算的发展，一大类抽象的生化网络可以使用核酸来实现。具有规定行为的抽象系统的系统设计方法主要是在（细节较少的）确定性水平上开发的。然而，在确定性水平上被忽略的随机效应在生物化学中越来越被发现起着重要作用。在这种情况下，需要在（更详细的）随机水平上控制系统固有噪声的方法来成功地进行网络设计。为了缩小这一差距，本文开发了用于设计生化网络的算法。该算法从结构上修改了在质量作用动力学下的任何给定反应网络，使得（i）可控的状态相关噪声被引入到随机动力学中，而（ii）确定性动力学得到保留。该算法的功能在一个生产-衰减反应系统和一个显示双稳性的奇特系统上得到了证明。对于生产-衰减系统，结果表明该算法可用于重新设计网络以实现噪声诱导的多稳定性。对于奇特系统，该算法用于重新设计网络以控制随机切换，并实现噪声诱导的振荡。
