title: A data-driven framework for identifying nonlinear dynamic models of genetic parts.
abstract: 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.
authors: Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK. k.krishnanathan@sheffield.ac.uk
authors: Kirubhakaran
authors: K
authors: Krishnanathan
authors: Sean R
authors: SR
authors: Anderson
authors: Stephen A
authors: SA
authors: Billings
authors: Visakan
authors: V
authors: Kadirkamanathan
mesh_terms: D003198:Computer Simulation; D008954:Models, Biological; D008957:Models, Genetic; D017711:Nonlinear Dynamics; D053038:Quorum Sensing / Q000235:genetics / Q000502:physiology; D058615:Synthetic Biology; D049490:Systems Biology