(152e) Computational Design of Synthetic Biological Circuits

Recent years has witnessed an increasing number of studies on constructing simple synthetic genetic circuits that exhibit desired properties such as oscillatory behavior, inducer specific activation/repression, etc. The hope is that these simple circuits are the vanguards of more complex ones with far ranging implications to biotechnology and medicine bringing to fruition the promise of synthetic biology. In the last few years tremendous progress has been made towards cataloguing these components into encompassing spare parts lists (http://parts.mit.edu/igem). These developments bring at the forefront the central question of how to chose and interconnect basic genetic elements such as promoters, proteins, reporters, etc. to assemble circuits that generate a desired response. This response may be conditional on the presence, absence or even narrow concentration range of one or multiple inducers. This is a challenging task because the required complexity in the design of circuits to meet multiple inducer-specific response targets becomes prohibitively high to manage without a model and computations. In addition, the total number of ways that you can chose and interconnect components grows exponentially with the size of the spare parts list.

To meet these emerging challenges, in this work we introduce an optimization based framework that automatically identifies the circuit components from a list and connectivity that brings about the desired functionality. Our procedure generates not only one but exhaustively all architectures that satisfy the imposed response criteria. Multiple literature sources are used to compile a comprehensive compilation of kinetic descriptions of promoter-protein pairs. The dynamics that govern the interactions between the elements of the genetic circuit are currently modeled using deterministic ordinary differential equations but the framework is general enough to accommodate stochastic simulations. The desired circuit response is abstracted as the maximization/minimization of an appropriately constructed objective function. For example, expression only in the presence of an inducer is encoded by maximizing the separation in the expression levels of the reporter in the presence and absence of inducer respectively.

The optimization framework has been applied on a variety of applications ranging from the design of circuits that exhibit a specific time course response (e.g., repressilator [1] system) and circuits that discriminate between the presence, absence and level of external stimuli (e.g., toggle switch [2]). The results for the repressilator study reveal that the proposed framework is able to identify all possible circuits yielding and oscillatory response. The results for the toggle switch example demonstrate the ability of the framework to (i) generate the complete list of circuit designs of varying complexity that exhibit the desired response; (ii) rectify a non-functional biological circuit and restore functionality by modifying an existing component and/or identifying additional components to append to the circuit; (iii) pinpoint what promoter's strength, interaction parameter or protein degradation constant to modify to meet the desired response.

References

1. M. B. Elowitz and S. Leibler, "A synthetic oscillatory network of transcriptional regulators". Nature,403(6767): p. 335-8. 2000.

2. T. S. Gardner, C. R. Cantor, and J. J. Collins, "Construction of a genetic toggle switch in Escherichia coli". Nature,403(6767): p. 339-42. 2000.