Paladugu S R, Chickarmane V, Deckard A, Frumkin J P, McCormack M, Sauro H M
Keck Graduate Institute, 535 Watson Drive, Claremont, CA 91711, USA.
Syst Biol (Stevenage). 2006 Jul;153(4):223-35. doi: 10.1049/ip-syb:20050096.
Understanding the large reaction networks found in biological systems is a daunting task. One approach is to divide a network into more manageable smaller modules, thus simplifying the problem. This is a common strategy used in engineering. However, the process of identifying biological modules is still in its infancy and very little is understood about the range and capabilities of motif structures found in biological modules. In order to delineate these modules, a library of functional motifs has been generated via in silico evolution techniques. On the basis of their functional forms, networks were evolved from four broad areas: oscillators, bistable switches, homeostatic systems and frequency filters. Some of these motifs were constructed from simple mass action kinetics, others were based on Michaelis-Menten kinetics as found in protein/protein networks and the remainder were based on Hill equations as found in gene/protein interaction networks. The purpose of the study is to explore the capabilities of different network architectures and the rich variety of functional forms that can be generated. Ultimately, the library may be used to delineate functional motifs in real biological networks.
理解生物系统中发现的大型反应网络是一项艰巨的任务。一种方法是将网络划分为更易于管理的较小模块，从而简化问题。这是工程中常用的策略。然而，识别生物模块的过程仍处于起步阶段，人们对生物模块中发现的基序结构的范围和能力了解甚少。为了描绘这些模块，通过计算机进化技术生成了一个功能基序库。根据其功能形式，网络从四个广泛领域进化而来：振荡器、双稳态开关、稳态系统和频率滤波器。其中一些基序由简单的质量作用动力学构建而成，其他基序基于蛋白质/蛋白质网络中发现的米氏动力学，其余的则基于基因/蛋白质相互作用网络中发现的希尔方程。该研究的目的是探索不同网络架构的能力以及可以生成的丰富多样的功能形式。最终，该库可用于描绘真实生物网络中的功能基序。
