Synthetic gene circuits have been developed by scientists and engineers that enable them to program the behavior, functionality, and performance of living cells. These circuits are often used to rewire endogenous networks, generate defined dynamics, produce valuable biomolecules, and sense environmental stimuli and show great potential for being used in medical and biotechnological applications in the near future.

Currently, most circuits are built via a trial and error process. However, this relies heavily on designer’s intuition and is always the most efficient way of doing things. “With the increase of circuit complexity, the lack of predictive design guidelines has become a major challenge in realizing the potential if synthetic biology,” said University of Illinois Bioengineering Associate Professor, Ting Lu.

To address this gene circuit design challenge researchers have been turning to quantitative modeling.  This way the models don’t interact with their hosts and instead focus on the biochemical processes that occur within the circuits. “Although highly valuable, the current modeling paradigm is often incapable of quantitatively, or even qualitatively sometimes, describing circuit behaviors,” says Lu. “Increasing experimental evidence has suggested that circuits and their biological host are intimately connected, and their coupling can impact circuit behaviors significantly.”

Lu and his team set about addressing this challenge by building an integrated modeling framework for predicting and describing gene circuit behaviors quantitatively. Using E. coli as a model host, the team demonstrated how the framework was able to predict data pertaining to the host and gene overexpression successfully.  For example, one thing the team discovered was that ppGpp-mediated effects are vital in understanding constitutive gene expression throughout environmental changes. This includes both antibiotic and nutrient changes.

Even though Lu’s framework was demonstrated by using E. coli as the model host, there’s definitely potential for it to be used to describe multiple host organisms. “For example, we found that, by varying only a single parameter, the framework successfully predicted several key host metrics, including RNA-to-protein ratio, RNA contents per cell, and mean peptide elongation rate, for Salmonella typhimurium and Streptomyces coelicolor,” said Lu.

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