Department of Biosystems Science and Engineering, The Swiss Federal Institute of Technology Zurich, Zurich, Switzerland.
PLoS One. 2012;7(7):e39194. doi: 10.1371/journal.pone.0039194. Epub 2012 Jul 3.
Switch like responses appear as common strategies in the regulation of cellular systems. Here we present a method to characterize bistable regimes in biochemical reaction networks that can be of use to both direct and reverse engineering of biological switches. In the design of a synthetic biological switch, it is important to study the capability for bistability of the underlying biochemical network structure. Chemical Reaction Network Theory (CRNT) may help at this level to decide whether a given network has the capacity for multiple positive equilibria, based on their structural properties. However, in order to build a working switch, we also need to ensure that the bistability property is robust, by studying the conditions leading to the existence of two different steady states. In the reverse engineering of biological switches, knowledge collected about the bistable regimes of the underlying potential model structures can contribute at the model identification stage to a drastic reduction of the feasible region in the parameter space of search. In this work, we make use and extend previous results of the CRNT, aiming not only to discriminate whether a biochemical reaction network can exhibit multiple steady states, but also to determine the regions within the whole space of parameters capable of producing multistationarity. To that purpose we present and justify a condition on the parameters of biochemical networks for the appearance of multistationarity, and propose an efficient and reliable computational method to check its satisfaction through the parameter space.
开关样反应似乎是细胞系统调控的常见策略。在这里，我们提出了一种方法来描述生化反应网络中的双稳态区，这对于生物开关的直接和反向工程都可能有用。在设计合成生物开关时，研究底层生化网络结构的双稳能力非常重要。基于其结构特性，化学反应网络理论（CRNT）可以帮助在这一水平上决定给定网络是否具有多个正平衡点的能力。然而，为了构建一个工作开关，我们还需要通过研究导致两个不同稳定状态存在的条件来确保双稳特性的稳健性。在生物开关的反向工程中，关于潜在模型结构的双稳区的知识可以在模型识别阶段为搜索参数空间中的可行区域的急剧减少做出贡献。在这项工作中，我们利用并扩展了 CRNT 的先前结果，不仅旨在区分生化反应网络是否可以表现出多个稳定状态，还旨在确定整个参数空间中能够产生多稳态的区域。为此，我们提出并证明了生化网络参数出现多稳态的条件，并提出了一种高效可靠的计算方法来通过参数空间检查其满足情况。
