Academic
Publications
Model-driven design based on sensitivity analysis for a synthetic biology application

Model-driven design based on sensitivity analysis for a synthetic biology application,10.1016/B978-0-444-54298-4.50068-4,Nikolaos Anesiadis,William R.

Model-driven design based on sensitivity analysis for a synthetic biology application  
BibTex | RIS | RefWorks Download
Synthetic biology, which involves the design of genetic circuits, has been valuable for understanding and engineering biological systems. Such genetic circuits could be valuable in metabolic engineering applications, where the goal is to manipulate the machinery of the organism in order to force or improve the bioengineering objective (which is typically in conflict with the organism's objective governed by evolution, i.e. to optimally allocate the resources for growth).We recently proposed a synthetic biology approach that manipulates the carbon fluxes severely affecting the competing objectives of growth and product formation. This model-based approach utilizes the natural quorum sensing mechanism as the sensory module and the artificial genetic toggle switch as the control module of Escherichia coli's metabolism. The goal of this strategy is to direct the carbon flux towards growth in the first phase of a batch in order to achieve a high concentration of biocatalyst as quickly as possible and then switch the flux towards the production of the desired metabolite in the second phase.In this paper, we examine the sensitivity of the desired product concentration to the parameters of the genetic circuit using global sensitivity analysis. Based on these results we suggest a rational experimental design for the tuning and optimization of an engineered programmable organism that maximizes the productivity without the need of monitoring and external induction of the production phase.
Published in 2011.
Cumulative Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.