Using Models to (Re-)Design Synthetic Circuits

Synthetic gene circuits in plants: recent advances and challenges

Plant synthetic biology is a rapidly advancing multidisciplinary research area that applies engineering principles to design, construct, and implement new plant capabilities at the molecular, cellular, and whole organism scales. Synthetic gene circuits are important tools for enabling increased customizability in the control of gene expression in plants, with widespread applications spanning new approaches for basic biology to introduction of new traits for advancing agriculture. In this review, we first aimed to provide a comprehensive understanding of synthetic circuits. Next, we discuss recent progress in the construction of advanced synthetic gene circuits in plants for programmable control of gene expression. Finally, we discuss the current challenges associated with developing and applying effective circuits while also highlighting future prospects and research directions, including quantitative measurement, high-throughput testing, and circuit modelling.

The Constrained Disorder Principle Overcomes the Challenges of Methods for Assessing Uncertainty in Biological Systems

Different disciplines are developing various methods for determining and dealing with uncertainties in complex systems. The constrained disorder principle (CDP) accounts for the randomness, variability, and uncertainty that characterize biological systems and are essential for their proper function. Per the CDP, intrinsic unpredictability is mandatory for the dynamicity of biological systems under continuously changing internal and external perturbations. The present paper describes some of the parameters and challenges associated with uncertainty and randomness in biological systems and presents methods for quantifying them. Modeling biological systems necessitates accounting for the randomness, variability, and underlying uncertainty of systems in health and disease. The CDP provides a scheme for dealing with uncertainty in biological systems and sets the basis for using them. This paper presents the CDP-based second-generation artificial intelligence system that incorporates variability to improve the effectiveness of medical interventions. It describes the use of the digital pill that comprises algorithm-based personalized treatment regimens regulated by closed-loop systems based on personalized signatures of variability. The CDP provides a method for using uncertainties in complex systems in an outcome-based manner.

Synthetic bacterial therapies for intestinal diseases based on quorum-sensing circuits

Bacterial therapy has become a key strategy against intestinal infectious diseases in recent years. Moreover, regulating the gut microbiota through traditional fecal microbiota transplantation and supplementation of probiotics faces controllability, efficacy, and safety challenges. The infiltration and emergence of synthetic biology and microbiome provide an operational and safe treatment platform for live bacterial biotherapies. Synthetic bacterial therapy can artificially manipulate bacteria to produce and deliver therapeutic drug molecules. This method has the advantages of solid controllability, low toxicity, strong therapeutic effects, and easy operation. As an essential tool for dynamic regulation in synthetic biology, quorum sensing (QS) has been widely used for designing complex genetic circuits to control the behavior of bacterial populations and achieve predefined goals. Therefore, QS-based synthetic bacterial therapy might become a new direction for the treatment of diseases. The pre-programmed QS genetic circuit can achieve a controllable production of therapeutic drugs on particular ecological niches by sensing specific signals released from the digestive system in pathological conditions, thereby realizing the integration of diagnosis and treatment. Based on this as well as the modular idea of synthetic biology, QS-based synthetic bacterial therapies are divided into an environmental signal sensing module (senses gut disease physiological signals), a therapeutic molecule producing module (plays a therapeutic role against diseases), and a population behavior regulating module (QS system). This review article summarized the structure and function of these three modules and discussed the rational design of QS gene circuits as a novel intervention strategy for intestinal diseases. Moreover, the application prospects of QS-based synthetic bacterial therapy were summarized. Finally, the challenges faced by these methods were analyzed to make the targeted recommendations for developing a successful therapeutic strategy for intestinal diseases.

In vivo, in vitro and in silico: an open space for the development of microbe-based applications of synthetic biology

Living systems are studied using three complementary approaches: living cells, cell-free systems and computer-mediated modelling. Progresses in understanding, allowing researchers to create novel chassis and industrial processes rest on a cycle that combines in vivo, in vitro and in silico studies. This design-build-test-learn iteration loop cycle between experiments and analyses combines together physiology, genetics, biochemistry and bioinformatics in a way that keeps going forward. Because computer-aided approaches are not directly constrained by the material nature of the entities of interest, we illustrate here how this virtuous cycle allows researchers to explore chemistry which is foreign to that present in extant life, from whole chassis to novel metabolic cycles. Particular emphasis is placed on the importance of evolution.