Krishnanathan Kirubhakaran, Anderson Sean R, Billings Stephen A, Kadirkamanathan Visakan
Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK.
ACS Synth Biol. 2012 Aug 17;1(8):375-84. doi: 10.1021/sb300009t. Epub 2012 Apr 4.
A key challenge in synthetic biology is the development of effective methodologies for characterization of component genetic parts in a form suitable for dynamic analysis and design. In this investigation we propose the use of a nonlinear dynamic modeling framework that is popular in the field of control engineering but is novel to the field of synthetic biology: Nonlinear AutoRegressive Moving Average model with eXogenous inputs (NARMAX). The framework is applied to the identification of a genetic part BBa_T9002 as a case study. A concise model is developed that exhibits accurate representation of the system dynamics and a structure that is compact and consistent across cell populations. A comparison is made with a biochemical model, derived from a simple enzymatic reaction scheme. The NARMAX model is shown to be comparably simple but exhibits much greater prediction accuracy on the experimental data. These results indicate that the data-driven NARMAX framework is an attractive technique for dynamic modeling of genetic parts.
合成生物学中的一个关键挑战是开发有效的方法，以适合动态分析和设计的形式对组件遗传部件进行表征。在本研究中，我们建议使用一种在控制工程领域很流行但在合成生物学领域却是新颖的非线性动态建模框架：具有外部输入的非线性自回归滑动平均模型（NARMAX）。该框架应用于遗传部件BBa_T9002的识别作为案例研究。开发了一个简洁的模型，该模型能够准确表示系统动态，并且具有跨细胞群体紧凑且一致的结构。与从简单酶促反应方案推导的生化模型进行了比较。结果表明，NARMAX模型同样简单，但在实验数据上表现出更高的预测准确性。这些结果表明，数据驱动的NARMAX框架是用于遗传部件动态建模的一种有吸引力的技术。
