title: Computational design of synthetic regulatory networks from a genetic library to characterize the designability of dynamical behaviors.
abstract: The engineering of synthetic gene networks has mostly relied on the assembly of few characterized regulatory elements using rational design principles. It is of outmost importance to analyze the scalability and limits of such a design workflow. To analyze the design capabilities of libraries of regulatory elements, we have developed the first automated design approach that combines such elements to search the genotype space associated to a given phenotypic behavior. Herein, we calculated the designability of dynamical functions obtained from circuits assembled with a given genetic library. By designing circuits working as amplitude filters, pulse counters and oscillators, we could infer new mechanisms for such behaviors. We also highlighted the hierarchical design and the optimization of the interface between devices. We dissected the functional diversity of a constrained library and we found that even such libraries can provide a rich variety of behaviors. We also found that intrinsic noise slightly reduces the designability of digital circuits, but it increases the designability of oscillators. Finally, we analyzed the robust design as a strategy to counteract the evolvability and noise in gene expression of the engineered circuits within a cellular background, obtaining mechanisms for robustness through non-linear negative feedback loops.
authors.affiliation: Institute of Systems and Synthetic Biology (ISSB), Genopole - Université d'Évry Val d'Essonne - CNRS UPS3201, 91030 Évry Cedex, France.
authors.forename: Guillermo
authors.initials: G
authors.lastname: Rodrigo
authors.forename: Javier
authors.initials: J
authors.lastname: Carrera
authors.forename: Alfonso
authors.initials: A
authors.lastname: Jaramillo
mesh_terms: D019295:Computational Biology / Q000379:methods; D015723:Gene Library; D053263:Gene Regulatory Networks; D005818:Genetic Engineering / Q000379:methods*; D008957:Models, Genetic