Translating weighted probabilistic bits to synthetic genetic circuits.

Towards programming-based synthetic genetic circuit enabled high-lysine maize

In the field of synthetic biology, genetic networks are designed by combining well-characterized genetic parts, similar to electronic circuits. Such gene networks are called synthetic genetic circuits. The design approach for synthetic genetic circuits is based on mathematical modeling and numerical simulation. The approach allows the realization of various cellular functions. However, unavoidable differences in the initial states or fluctuations of the gene expression in cells have prevented the precise prediction and control of cellular behavior. Therefore, the design of synthetic genetic circuits is not sufficient, and the dynamic control of the circuits is also required. In this report, we provide examples of synthetic circuit designs and the control of synthetic biological systems, as well as perspectives on design and control.KeywordsSynthetic biologyControl engineeringSynthetic genetic circuits

A key goal of synthetic morphogenesis is the identification and implementation of methods to control morphogenesis. One line of research is the use of synthetic genetic circuits guiding the self-organization of cell ensembles. This approach has led to several recent successes, including control of cellular rearrangements in 3D via control of cell-cell adhesion by user-designed artificial genetic circuits. However, the methods employed to reach such achievements can still be optimized along three lines: identification of circuits happens by hand, 3D structures are spherical, and effectors are limited to cell-cell adhesion. Here we show the identification, in a computational framework, of genetic circuits for volumetric axial elongation via control of proliferation, tissue fluidity, and cell-cell signaling. We then seek to implement this design in mammalian cell aggregates in vitro. We start by identifying effectors to control tissue growth and fluidity in vitro. We then combine these new modules to construct complete circuits that control cell behaviors of interest in space and time, resulting in measurable tissue deformation along an axis that depends on the engineered signaling modules. Finally, we contextualize in vitro and in silico implementations within a unified morphospace to suggest further elaboration of this initial family of circuits towards more robust programmed axial elongation. These results and integrated in vitro/in silico pipeline demonstrate a promising method for designing, screening, and implementing synthetic genetic circuits of morphogenesis, opening the way to the programming of various user-defined tissue shapes.

Synthetic genetic circuits are the core research objects in synthetic biology, and the programming of cell behavior is achieved through the combination of engineered gene elements. Mathematical modeling provides crucial support for understanding and designing synthetic genetic circuits, enabling researchers to predict the dynamic behavior of the circuits and guide experimental optimization. This study reviews the categories of synthetic genetic circuits (such as gene switches, oscillators, feedback circuits, etc.) and their biological mechanisms, with a focus on the application of ordinary differential equation (ODE) models, stochastic modeling, and network topology dynamics models in circuit modeling. We expounded on the estimation of model parameters, sensitivity analysis, and the integration methods of experimental data and models, and compared the characteristics of numerical simulation algorithms and commonly used software tools (such as MATLAB, COPASI, BioNetGen, etc.). Through the discussion of the steady-state, oscillation behavior, multiple steady-state and bifurcation analysis of system dynamics, the understanding of the influence of positive and negative feedback mechanisms on system stability is deepened. In addition, we took the classic synthetic gene oscillator Repressilator as a case to conduct modeling and simulation analysis, and compared the model predictions with the experimental data. Finally, the application prospects of synthetic genetic circuits in the fields of bioengineering and medicine were summarized, and the future directions of promoting the design of synthetic circuits with the help of model optimization and artificial intelligence-assisted design were prospected. Research shows that mathematical modeling and computational simulation have become key tools for the study and design of synthetic genetic circuits, providing a theoretical basis and practical guidance for the engineering transformation of complex biological systems.

: Protein overexpression leads to growth inhibition and decreased expression of native proteins. This effect is mostly due to competition for RNAP and ribosomes. This competition affects also synthetic biology circuits, which may not behave as expected. The aims of this project are to: (1) characterize/quantify the effect of competition for RNAPs and ribosomes on synthetic genetic circuits; (2) determine the extent to which RNAPs or ribosomes are limiting; (3) develop models that predict the change of behavior of circuits due to competition; (4) demonstrate the predictions on the design of a synthetic cascade. We have constructed a synthetic circuit in E. coli and characterized through theory and experiments the effect of competition on the expression of a constitutively expressed gene when a different inducible gene is activated. For this system, we have also developed predictive models, both analytical and computational. Our finding quantifies the extent of competition and provides initial tools to predict this effect. Our experimental results and models will enable design methods for next-generation circuits that are robust to competition (to be investigated later). This ability will be critical for constructing large circuits reliably.

Synthetic models in biology are artifacts constructed from biological components, such as genes and proteins, to form biological parts or wholes. They have the same materiality as natural biological systems but are not the results of natural evolutionary processes. This entry focuses on synthetic genetic circuits, in particular the repressilator, which is one of the first and most famous of them. The introduction of the first synthetic genetic circuits in 2000 has often been taken as the beginning of synthetic biology proper. While they are intricate experimental systems, synthetic genetic circuits can be considered models in contrast to many other synthetic biology constructs. Being based on engineering principles and the enterprise of standardizing biological parts, synthetic models have enabled researchers to probe gene regulation by building small circuits implemented within living cells. Although synthetic modeling has been successful by many yardsticks, researchers have faced challenges due to biochemical particularities, as well as the stochasticity of biological systems and their unintuitive designs.

BackgroundCell population control allows for the maintenance of a specific cell population density. In this study, we use lysis gene BBa_K117000 from the Registry of Standard Biological Parts, formed by MIT, to lyse Escherichia coli (E. coli). The lysis gene is regulated by a synthetic genetic lysis circuit, using an inducer-regulated promoter-RBS component. To make the design more easily, it is necessary to provide a systematic approach for a genetic lysis circuit to achieve control of cell population density.ResultsFirstly, the lytic ability of the constructed genetic lysis circuit is described by the relationship between the promoter-RBS components and inducer concentration in a steady state model. Then, three types of promoter-RBS libraries are established. Finally, according to design specifications, a systematic design approach is proposed to provide synthetic biologists with a prescribed I/O response by selecting proper promoter-RBS component set in combination with suitable inducer concentrations, within a feasible range.ConclusionThis study provides an important systematic design method for the development of next-generation synthetic gene circuits, from component library construction to genetic circuit assembly. In future, when libraries are more complete, more precise cell density control can be achieved.

Precision medicine in oncology needs to enhance its capabilities to match diagnostic and therapeutic technologies to individual patients. Synthetic biology streamlines the design and construction of functionalized devices through standardization and rational engineering of basic biological elements decoupled from their natural context. Remarkable improvements have opened the prospects for the availability of synthetic devices of enhanced mechanism clarity, robustness, sensitivity, as well as scalability and portability, which might bring new capabilities in precision cancer medicine implementations. In this review, we begin by presenting a brief overview of some of the major advances in the engineering of synthetic genetic circuits aimed to the control of gene expression and operating at the transcriptional, post-transcriptional/translational, and post-translational levels. We then focus on engineering synthetic circuits as an enabling methodology for the successful establishment of precision technologies in oncology. We describe significant advancements in our capabilities to tailor synthetic genetic circuits to specific applications in tumor diagnosis, tumor cell- and gene-based therapy, and drug delivery.

A recurring challenge in the design of genetic circuits is context dependence, the fact that the behavior of a functional module is influenced by the state of the wider cellular milieu with which it interacts. One key player in context dependence is the scarcity of shared cellular resources, especially those required for transcription and translation during gene expression. Because of competition for these limited resources, the behavior of modules becomes coupled in subtle ways, preventing circuits from working as expected. This paper proposes a classical feedback control approach to mitigate the steady state effects of the competition for resources necessary for gene expression. In particular, we analyze and compare the ability of several inhibitory feedback regulation architectures to reduce the interdependence between different gene expression processes due to resource limitations.

Reprogramming human fibroblasts to induced pluripotent stem cells (iPSCs) is inefficient, with heterogeneity among transcription factor (TF) trajectories driving divergent cell states. Nevertheless, the impact of TF dynamics on reprogramming efficiency remains uncharted. Here, we identify the successful reprogramming trajectories of the core pluripotency TF, OCT4, and design a genetic controller that enforces such trajectories with high precision. By combining a genetic circuit that generates a wide range of OCT4 trajectories with live-cell imaging, we track OCT4 trajectories with clonal resolution and find that a distinct constant OCT4 trajectory is required for colony formation. We then develop a synthetic genetic circuit that yields a tight OCT4 distribution around the identified trajectory and outperforms in terms of reprogramming efficiency other circuits that less accurately regulate OCT4. Our synthetic biology approach is generalizable for identifying and enforcing TF dynamics for cell fate programming applications.

Synthetic genetic circuits allow us to modify the behavior of living cells. However, changes in environmental conditions and unforeseen interactions with the host cell can cause deviations from a desired function, resulting in the need for time-consuming reassembly to fix these issues. Here, we use a regulatory motif that controls transcription and translation to create genetic devices whose response functions can be dynamically tuned. This allows us, after construction, to shift the on and off states of a sensor by 4.5- and 28-fold, respectively, and modify genetic NOT and NOR logic gates to allow their transitions between states to be varied over a >6-fold range. In all cases, tuning leads to trade-offs in the fold-change and the ability to distinguish cellular states. This work lays the foundation for adaptive genetic circuits that can be tuned after their physical assembly to maintain functionality across diverse environments and design contexts.

Advances in synthetic biology now allow for the reprogramming of microorganisms to execute specific tasks. Here, we describe the development of an engineered strain of E. coli capable of sensing and responding to the presence of a mammalian inflammatory signal. The synthetic gene regulatory circuit is designed to permanently alter gene expression in response to the well characterized inflammatory signal nitric oxide. The detection of nitric oxide initiates the expression of a DNA recombinase, causing the permanent activation of a DNA switch. We demonstrate that E. coli containing this synthetic circuit respond to nitric oxide from both chemical and biological sources, with permanent DNA recombination occurring in the presence of nitric oxide donor compounds or inflamed mouse ileum explants. In the future, this synthetic genetic circuit will be optimized to allow E. coli to reliably detect and respond to inflammation in vivo.

The construction of stable and functional synthetic circuits in bacteria is necessary in the areas of systems and synthetic biology. The common approach using plasmids to carry foreign genetic circuits offers convenience in genetic construction but is poor in genetic stability (e.g., variation in copy number). Genome recombination provides the stable genetic maintenance of synthetic circuits, but is often labor intensive and time consuming when the genetic circuits are complex and the DNA fragments become larger. The method introduced here is modified from that reported by Wanner's group and is available for integration of complex genetic circuits into the Escherichia coli chromosome.

A sensitive and specific nano-vehicle based on self-amplified dual-input synthetic gene circuit for intracellular imaging and treatment

Chapter Six - Directed Evolution of Promoters and Tandem Gene Arrays for Customizing RNA Synthesis Rates and Regulation