Abstract
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.
Highlights
Cell free systems are a widely used research tool in systems and synthetic biology and a promising platform for the manufacturing of proteins and chemicals (Vilkhovoy et al, 2020)
JuPOETs produced an ensemble (N = 140) of the 11 unknown model parameters which captured the transcription of messenger RNA (mRNA) (Figure 2A) and the translation of dual emission green fluorescent protein variant (deGFP) protein (Figure 2B)
Given there was negligible protein degradation
Summary
Cell free systems are a widely used research tool in systems and synthetic biology and a promising platform for the manufacturing of proteins and chemicals (Vilkhovoy et al, 2020). A majority of these models fall into three categories: logical, continuous, and stochastic models (Karlebach and Shamir, 2008) Logical models such as Boolean networks (Glass and Kauffman, 1973) developed using a variety of approaches and data (Pratapa et al, 2020) represent the state of each network entity as a discrete variable, provide a quick but qualitative description of the behavior of the regulatory network. There have been significant advancements in metabolomics (e.g., Park et al, 2016), the rigorous quantification of intracellular messenger RNA (mRNA) copy number or protein abundance remains challenging Toward this challenge, cell free systems offer several advantages for the study of transcription and translation processes
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