A Data-Driven Framework for Identifying Nonlinear Dynamic Models of Genetic Parts

作者：Krishnanathan Kirubhakaran^*; Anderson Sean R; Billings Stephen A; Kadirkamanathan Visakan

来源：ACS Synthetic Biology, 2012, 1(8): 375-384.

DOI：10.1021/sb300009t

• NARMAX
• nonlinear system identification
• biochemical models
• dynamic
• genetic parts

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.

• 出版日期2012-8