Issue 3, Volume 2 – 4 articles

Synthetic biology has revolutionized our ability to engineer biological systems, with synthetic gene circuits serving as the fundamental building blocks for programming cellular behavior. These circuits, composed of genes encoding proteins or RNA molecules that regulate each other's expression, have enabled the creation of novel biological functions and reprogramming of cellular responses. However, creating predictable and robust circuits remains challenging due to the inherent complexities of biological systems, such as stochasticity and intrinsic noise. To address these challenges, researchers are employing innovative approaches that combine theoretical modeling with experimental validation, particularly using cell-free systems.

Cell-free systems have emerged as a powerful tool for characterizing and prototyping synthetic gene regulatory networks. These systems offer several advantages overin vivo experiments, including decreased complexity, absence of membrane barriers, and elimination of cell viability concerns. The ability to lyophilize and reactivate cell-free reactions enhances experimental flexibility and facilitates applications in non-laboratory settings. Moreover, cell-free systems have been successfully applied in various contexts, including pollutant identification, RNA genetic circuit dynamics analysis, enzyme expression, rapid field-portable diagnostics, and industrial applications.

Coupling mathematical modeling with cell-free experiments has become an effective strategy for exploring novel synthetic gene circuits. Deterministic first principle-based models can provide insights into the required kinetic parameters and the specific regulatory components needed to deliver the desired circuit dynamics. Nevertheless, challenges persist in accurately modeling the stochastic nature of genetic circuits and addressing issues of model structural and parameter identifiability. Recent studies have highlighted the importance of considering resource competition and changes in cellular context when designing synthetic gene circuits. Resource availability can significantly impact circuit performance, thus necessitating a more comprehensive understanding of the circuit-host interactions for robust and predictable circuits. To overcome limitations in traditional modeling approaches, researchers are also exploring new approaches. Physics-informed machine learning combined with transfer learning shows promise in developing more adaptable models. This approach could potentially reduce the cost of materials and computation while improving model transferability across different systems. In addition, machine learning techniques, such as decision trees and deep neural networks, are also being applied to better understand and predict the behavior of cell-free systems and circuit components. These methods offer the potential to characterize system behavior without requiring extensive a priori physical knowledge, although they typically demand large datasets for training.

As the field progresses, researchers are addressing challenges related to circuit robustness, scalability, and host-circuit interactions. Efforts are proceeding to develop standardized parts and modular design principles that can facilitate the creation of more complex and reliable synthetic biological systems. The integration of advanced technologies, such as CRISPR,is expanding the toolkit available to synthetic biologists. The ongoing development of synthetic gene circuits, coupled with advancements in cell-free systems and modeling techniques, represents a significant step towards harnessing and redirecting cellular functions for both scientific discovery and practical applications.

In this review, we present recent progress in the application of mathematical modeling and cell-free systems for the design and characterization of novel gene circuits, particularly on the efficient exploration of gene circuitdesigns, and the selection of specific regulatory parts for desiredoutput dynamics. We anticipate the combined use of modelingand cell-free platformto continue benefit the prosperity of synthetic gene circuits design, and the field of synthetic biology.