(283a) In Silico Design of Synthetic Tetracycline-Inducible Regulatory Gene Networks | AIChE

(283a) In Silico Design of Synthetic Tetracycline-Inducible Regulatory Gene Networks


Sotiropoulos, V. - Presenter, University of Minnesota

Tightly regulated gene networks, precisely controlling the expression of protein molecules, have received considerable interest by the biomedical community due to their promising applications. Among the most well studied inducible transcription systems are the tetracycline regulatory expression systems based on the tetracycline resistance operon of Escherichia coli, Tet-Off (tTA) and Tet-On (rtTA). Despite their initial success and improved designs, limitations still persist, such as low inducer sensitivity. Instead of looking at these networks statically, and simply changing or mutating the promoter and operator regions with trial and error, a systematic investigation of the dynamic behavior of the network can result in rational design of regulatory gene expression systems. Sophisticated algorithms can accurately capture the dynamical behavior of gene networks. Computer simulations allow us to look into the molecular level, portray the dynamic behavior of gene regulatory networks and rationally engineer novel ones with useful applications.

In this work we engineer novel networks by recombining existing genes or part of genes. In particular, we synthesize four novel regulatory networks based on the Tet-Off and Tet-On systems. We model all the known individual biomolecular interactions involved in transcription, translation, regulation and induction using stochastic chemical kinetics models. With multiple time-scale stochastic-discrete and stochastic-continuous models we accurately capture the transient and steady state dynamics of these networks. Important biomolecular interactions are identified and the strength of the interactions engineered to satisfy design criteria. We are able to propose, test and accept or reject design principles for each network. A set of clear design rules is developed and appropriate mutants of regulatory proteins and operator sites are proposed, leading to a fine tuned design for each proposed network.