title: Design and analysis of a robust genetic Muller C-element.
abstract: This paper presents results on the design and analysis of a robust genetic Muller C-element. The Muller C-element is a standard logic gate commonly used to synchronize independent processes in most asynchronous electronic circuits. Synthetic biological logic gates have been previously demonstrated, but there remain many open issues in the design of sequential (state-holding) logic operations. Three designs are considered for the genetic Muller C-element: a majority gate, a toggle switch, and a speed-independent implementation. While the three designs are logically equivalent, each design requires different assumptions to operate correctly. The majority gate design requires the most timing assumptions, the speed-independent design requires the least, and the toggle switch design is a compromise between the two. This paper examines the robustness of these designs as well as the effects of parameter variation using stochastic simulation. The results show that robustness to timing assumptions does not necessarily increase reliability, suggesting that modifications to existing logic design tools are going to be necessary for synthetic biology. Parameter variation simulations yield further insights into the design principles necessary for building robust genetic gates. The results suggest that high gene count, cooperativity of at least two, tight repression, and balanced decay rates are necessary for robust gates. Finally, this paper presents a potential application of the genetic Muller C-element as a quorum-mediated trigger.
authors.affiliation: University of Texas-Austin, USA.
authors.forename: Nam-phuong
authors.initials: NP
authors.lastname: Nguyen
authors.forename: Chris
authors.initials: C
authors.lastname: Myers
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Kuwahara
authors.forename: Chris
authors.initials: C
authors.lastname: Winstead
authors.forename: James
authors.initials: J
authors.lastname: Keener
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D011401:Promoter Regions, Genetic / Q000235:genetics