Design of experiments for steady-state system identification with applications in genetic and business network modelinG

Abstract

ABSTRACT This paper focuses on the inverse problem of predicting inputs from measured outputs in the context of linear systems in steady-state. For system identification, we propose forward network identification regression (FNIR) and experimental planning involving simultaneously perturbing more than a single gene concentration using D-optimal designs. The proposed methods are compared with alternatives using simulation and data sets motivated by the SOS pathway for Escherichia coli bacteria. Findings include that the optimal experimental planning can likely improve the sensitivity, specificity, and efficiency of the process of deriving genetic networks. Topics for further research are also suggested in this paper.