A Computational Modeling Approach for the Design of Genetic Control Systems that Respond to Transcriptional Activity.

Recent progress in synthetic biology has enabled the design of complex genetic circuits that interface with innate cellular functions, such as gene transcription, and control user-defined outputs. Implementing these genetic networks in mammalian cells, however, is a cumbersome process that requires several steps of optimization and benefits from the use of predictive modeling. Combining deterministic mathematical models with software-based numerical computing platforms allows researchers to quickly design, evaluate, and optimize multiple circuit topologies to establish experimental constraints that generate the desired control systems. In this chapter, we present a systematic approach based on predictive mathematical modeling to guide the design and construction of gene activity-based sensors. This approach enables user-driven circuit optimization through iterations of sensitivity analyses and parameter scans, providing a universal method to engineer sense and respond cells for diverse applications.

Effective Biophysical Modeling of Cell Free Transcription and Translation Processes.

Transcription and translation are at the heart of metabolism and signal transduction. In this study, we developed an effective biophysical modeling approach to simulate transcription and translation processes. The model, composed of coupled ordinary differential equations, was tested by comparing simulations of two cell free synthetic circuits with experimental measurements generated in this study. First, we considered a simple circuit in which sigma factor 70 induced the expression of green fluorescent protein. This relatively simple case was then followed by a more complex negative feedback circuit in which two control genes were coupled to the expression of a third reporter gene, green fluorescent protein. Many of the model parameters were estimated from previous biophysical studies in the literature, while the remaining unknown model parameters for each circuit were estimated by minimizing the difference between model simulations and messenger RNA (mRNA) and protein measurements generated in this study. In particular, either parameter estimates from published studies were used directly, or characteristic values found in the literature were used to establish feasible ranges for the parameter estimation problem. In order to perform a detailed analysis of the influence of individual model parameters on the expression dynamics of each circuit, global sensitivity analysis was used. Taken together, the effective biophysical modeling approach captured the expression dynamics, including the transcription dynamics, for the two synthetic cell free circuits. While, we considered only two circuits here, this approach could potentially be extended to simulate other genetic circuits in both cell free and whole cell biomolecular applications as the equations governing the regulatory control functions are modular and easily modifiable. The model code, parameters, and analysis scripts are available for download under an MIT software license from the Varnerlab GitHub repository.