Synthetic Gene Networks Research Articles

AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks.

Many applications in biomedicine and synthetic bioengineering rely on understanding, mapping, predicting, and controlling the complex behavior of chemical and genetic networks. The emerging field of diverse intelligence investigates the problem-solving capacities of unconventional agents. However, few quantitative tools exist for exploring the competencies of non-conventional systems. Here, we view gene regulatory networks (GRNs) as agents navigating a problem space and develop automated tools to map the robust goal states GRNs can reach despite perturbations. Our contributions include: (1) Adapting curiosity-driven exploration algorithms from AI to discover the range of reachable goal states of GRNs, and (2) Proposing empirical tests inspired by behaviorist approaches to assess their navigation competencies. Our data shows that models inferred from biological data can reach a wide spectrum of steady states, exhibiting various competencies in physiological network dynamics without requiring structural changes in network properties or connectivity. We also explore the applicability of these 'behavioral catalogs' for comparing evolved competencies across biological networks, for designing drug interventions in biomedical contexts and synthetic gene networks for bioengineering. These tools and the emphasis on behavior-shaping open new paths for efficiently exploring the complex behavior of biological networks. For the interactive version of this paper, please visit https://developmentalsystems.org/curious-exploration-of-grn-competencies.

AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks

Many applications in biomedicine and synthetic bioengineering rely on understanding, mapping, predicting, and controlling the complex behavior of chemical and genetic networks. The emerging field of diverse intelligence investigates the problem-solving capacities of unconventional agents. However, few quantitative tools exist for exploring the competencies of non-conventional systems. Here, we view gene regulatory networks (GRNs) as agents navigating a problem space and develop automated tools to map the robust goal states GRNs can reach despite perturbations. Our contributions include: (1) Adapting curiosity-driven exploration algorithms from AI to discover the range of reachable goal states of GRNs, and (2) Proposing empirical tests inspired by behaviorist approaches to assess their navigation competencies. Our data shows that models inferred from biological data can reach a wide spectrum of steady states, exhibiting various competencies in physiological network dynamics without requiring structural changes in network properties or connectivity. We also explore the applicability of these ‘behavioral catalogs’ for comparing evolved competencies across biological networks, for designing drug interventions in biomedical contexts and synthetic gene networks for bioengineering. These tools and the emphasis on behavior-shaping open new paths for efficiently exploring the complex behavior of biological networks. For the interactive version of this paper, please visit https://developmentalsystems.org/curious-exploration-of-grn-competencies.

Developmental assembly of multi-component polymer systems through interconnected synthetic gene networks in vitro

Living cells regulate the dynamics of developmental events through interconnected signaling systems that activate and deactivate inert precursors. This suggests that similarly, synthetic biomaterials could be designed to develop over time by using chemical reaction networks to regulate the availability of assembling components. Here we demonstrate how the sequential activation or deactivation of distinct DNA building blocks can be modularly coordinated to form distinct populations of self-assembling polymers using a transcriptional signaling cascade of synthetic genes. Our building blocks are DNA tiles that polymerize into nanotubes, and whose assembly can be controlled by RNA molecules produced by synthetic genes that target the tile interaction domains. To achieve different RNA production rates, we use a strategy based on promoter “nicking” and strand displacement. By changing the way the genes are cascaded and the RNA levels, we demonstrate that we can obtain spatially and temporally different outcomes in nanotube assembly, including random DNA polymers, block polymers, and as well as distinct autonomous formation and dissolution of distinct polymer populations. Our work demonstrates a way to construct autonomous supramolecular materials whose properties depend on the timing of molecular instructions for self-assembly, and can be immediately extended to a variety of other nucleic acid circuits and assemblies.

How Does Escherichia coli Allocate Proteome?

Microorganisms are shown to actively partition their intracellular resources, such as proteins, for growth optimization. Recent experiments have begun to reveal molecular components unpinning the partition; however, quantitatively, it remains unclear how individual parts orchestrate to yield precise resource allocation that is both robust and dynamic. Here, we developed a coarse-grained mathematical framework that centers on guanosine pentaphosphate (ppGpp)-mediated regulation and used it to systematically uncover the design principles of proteome allocation in Escherichia coli. Our results showed that the cellular ability of resource partition lies in an ultrasensitive, negative feedback-controlling topology with the ultrasensitivity arising from zero-order amino acid kinetics and the negative feedback from ppGpp-controlled ribosome synthesis. In addition, together with the time-scale separation between slow ribosome kinetics and fast turnovers of ppGpp and amino acids, the network topology confers the organism an optimization mechanism that mimics sliding mode control, a nonlinear optimization strategy that is widely used in man-made systems. We further showed that such a controlling mechanism is robust against parameter variations and molecular fluctuations and is also efficient for biomass production over time. This work elucidates the fundamental controlling mechanism of E. coli proteome allocation, thereby providing insights into quantitative microbial physiology as well as the design of synthetic gene networks.

Classification of 2-node excitatory–inhibitory networks

We classify connected 2-node excitatory–inhibitory networks under various conditions. We assume that, as well as for connections, there are two distinct node-types, excitatory and inhibitory. In our classification we consider four different types of excitatory–inhibitory networks: restricted, partially restricted, unrestricted and completely unrestricted. For each type we give two different classifications. Using results on ODE-equivalence and minimality, we classify the ODE-classes and present a minimal representative for each ODE-class. We also classify all the networks with valence ≤2. These classifications are up to renumbering of nodes and the interchange of ‘excitatory’ and ‘inhibitory’ on nodes and arrows. These classifications constitute a first step towards analysing dynamics and bifurcations of excitatory–inhibitory networks. The results have potential applications to biological network models, especially neuronal networks, gene regulatory networks, and synthetic gene networks.

Degron tagging for rapid protein degradation in mice.

Degron tagging allows proteins of interest to be rapidly degraded, in a reversible and tuneable manner, in response to a chemical stimulus. This provides numerous opportunities for understanding disease mechanisms, modelling therapeutic interventions and constructing synthetic gene networks. In recent years, many laboratories have applied degron tagging successfully in cultured mammalian cells, spurred by rapid advances in the fields of genome editing and targeted protein degradation. In this At a Glance article, we focus on recent efforts to apply degron tagging in mouse models, discussing the distinct set of challenges and opportunities posed by the in vivo environment.

Analysis of the most probable exit path in the synthetic gene network with genetic toggle

In this paper, the most probable exit path is applied to understand the switching of a bistable synthetic gene network with genetic toggle in Escherichia coli from a new perspective. It is worth mentioning that the most probable exit path is a deterministic indicator for researching stochastic system, and its role is analogous to the phase diagram and time history diagram of deterministic system. Firstly, the large deviation theory proves that when a dynamic system has a stable equilibrium point, the cumulative effect of its long-term action can make its sample trajectory reach another region with a probability of 1 from the attractive region under weak random disturbance. The concept of the most probable exit path is given by defining Freidlin-Wentzell action functional. Moreover, considering the noise intensity, Onsager-Machlup action functional is defined to obtain the most probable exit path of the system subject to random disturbance. Based on the Onsager-Machlup action functional, it can be observed that the impact of two types of noise intensities on the most probable exit path for the transition of the system. In addition, based on vector field decomposition, energy landscape is used to analyze the global stability of the system. Finally, this article intuitively demonstrates the switching process of the system with genetic toggle. The analysis of Onsager-Machlup action functional reveals that two independent noises have opposite effects on the system switching. The increase in the intensity of the second Gaussian noise is conductive to more accurately observing the most probable exit path of the system. According to the energy landscape, the reduction of noise intensity is not conductive to switching of the system with genetic toggle.

Modeling Control of Supercoiling Dynamics and Transcription Using DNA-Binding Proteins.

Nearly all natural and synthetic gene networks rely on the fundamental process of transcription to enact biological feedback, genetic programs, and living circuitry. In this work, we investigate the efficacy of controlling transcription using a new biophysical mechanism, control of localized supercoiling near a gene of interest. We postulate a basic reaction network model for describing the general phenomenon of transcription and introduce a separate set of equations to describe the dynamics of supercoiling. We show that supercoiling and transcription introduce a shared reaction flux term in the model dynamics and illustrate how the modulation of supercoiling can be used to control transcription rates. We show the supercoiling-transcription model can be written as a nonlinear state-space model, with a radial basis function nonlinearity to capture the empirical relationship between supercoiling and transcription rates. We show the system admits a single, globally exponentially stable equilibrium point. Notably, we show that mRNA steady-state levels can be controlled directly by increasing a length-scale parameter for genetic spacing. Finally, we build a mathematical model to explore the use of a DNA binding protein, to define programmable boundary conditions on supercoiling propagation, which we show can be used to control transcriptional bursting or pulsatile transcriptional response. We show there exists a stabilizing control law for mRNA tracking, using the method of control Lyapunov functions and illustrate these results with numerical simulations.

Circular single-stranded DNA as switchable vector for gene expression in mammalian cells

Synthetic gene networks in mammalian cells are currently limited to either protein-based transcription factors or RNA-based regulators. Here, we demonstrate a regulatory approach based on circular single-stranded DNA (Css DNA), which can be used as an efficient expression vector with switchable activity, enabling gene regulation in mammalian cells. The Css DNA is transformed into its double-stranded form via DNA replication and used as vectors encoding a variety of different proteins in a wide range of cell lines as well as in mice. The rich repository of DNA nanotechnology allows to use sort single-stranded DNA effectors to fold Css DNA into DNA nanostructures of different complexity, leading the gene expression to programmable inhibition and subsequently re-activation via toehold-mediated strand displacement. The regulatory strategy from Css DNA can thus expand the molecular toolbox for the realization of synthetic regulatory networks with potential applications in genetic diagnosis and gene therapy.

Homogeneous detection of viral nucleic acid via selective recognition proximity ligation and signal amplification with T7 transcription and CRISPR/Cas12a system

The synthetic biology has employed the synthetic gene networks through engineering to construct various functions in biological systems. However, the use of gene circuits to create sensors for detecting low-abundance targets has been limited due to the lack of signal amplification strategies beyond direct output of detection signals. To address this issue, we introduce a novel method utilizing Selective Recognition Proximity Ligation and signal amplification with T7 Transcription and CRISPR/Cas12a system (SRPL-TraCs), which permits the incorporation of cell-free gene circuits with signal amplification and enables the construction of high-order cascade signal amplification strategy to detect biomarkers in homogeneous systems. Specifically, the SRPL-TraCs utilizes selective recognition proximity ligation with high-fidelity T4 DNA ligase and generates a unique crRNA via T7 transcription, along with target-activated Cas12a/crRNA system to achieve excellent specificity for HIV-1 DNA. With this straightforward synthetic biology-based method, the proposed SRPL-TraCs has the potential to detect numerous other interesting targets beyond the nucleic acids.

Spatial biology of Ising-like synthetic genetic networks

BackgroundUnderstanding how spatial patterns of gene expression emerge from the interaction of individual gene networks is a fundamental challenge in biology. Developing a synthetic experimental system with a common theoretical framework that captures the emergence of short- and long-range spatial correlations (and anti-correlations) from interacting gene networks could serve to uncover generic scaling properties of these ubiquitous phenomena.ResultsHere, we combine synthetic biology, statistical mechanics models, and computational simulations to study the spatial behavior of synthetic gene networks (SGNs) in Escherichia coli quasi-2D colonies growing on hard agar surfaces. Guided by the combined mechanisms of the contact process lattice simulation and two-dimensional Ising model (CPIM), we describe the spatial behavior of bi-stable and chemically coupled SGNs that self-organize into patterns of long-range correlations with power-law scaling or short-range anti-correlations. These patterns, resembling ferromagnetic and anti-ferromagnetic configurations of the Ising model near critical points, maintain their scaling properties upon changes in growth rate and cell shape.ConclusionsOur findings shed light on the spatial biology of coupled and bistable gene networks in growing cell populations. This emergent spatial behavior could provide insights into the study and engineering of self-organizing gene patterns in eukaryotic tissues and bacterial consortia.

Noise Minimization in Cell-Free Gene Expression.

Biochemical reactions that involve small numbers of molecules are accompanied by a degree of inherent randomness that results in noisy reaction outcomes. In synthetic biology, the ability to minimize noise particularly during the reconstitution of future synthetic protocells is an outstanding challenge to secure robust and reproducible behavior. Here we show that by encapsulation of a bacterial cell-free gene expression system in water-in-oil droplets, in vitro-synthesized MazF reduces cell-free gene expression noise >2-fold. With stochastic simulations we identify that this noise minimization acts through both increased degradation and the autoregulatory feedback of MazF. Specifically, we find that the expression of MazF enhances the degradation rate of mRNA up to 18-fold in a sequence-dependent manner. This sequence specificity of MazF would allow targeted noise control, making it ideal to integrate into synthetic gene networks. Therefore, including MazF production in synthetic biology can significantly minimize gene expression noise, impacting future design principles of more complex cell-free gene circuits.

Methods for Embedding Cell-Free Protein Synthesis Reactions in Macro-Scale Hydrogels

Synthetic gene networks provide a platform for scientists and engineers to design and build novel systems with functionality encoded at a genetic level. While the dominant paradigm for the deployment of gene networks is within a cellular chassis, synthetic gene networks may also be deployed in cell-free environments. Promising applications of cell-free gene networks include biosensors, as these devices have been demonstrated against biotic (Ebola, Zika, and SARS-CoV-2 viruses) and abiotic (heavy metals, sulfides, pesticides, and other organic contaminants) targets. Cell-free systems are typically deployed in liquid form within a reaction vessel. Being able to embed such reactions in a physical matrix, however, may facilitate their broader application in a wider set of environments. To this end, methods for embedding cell-free protein synthesis (CFPS) reactions in a variety of hydrogel matrices have been developed. One of the key properties of hydrogels conducive to this work is the high-water reconstitution capacity of hydrogel materials. Additionally, hydrogels possess physical and chemical characteristics that are functionally beneficial. Hydrogels can be freeze-dried for storage and rehydrated for use later. Two step-by-step protocols for the inclusion and assay of CFPS reactions in hydrogels are presented. First, a CFPS system can be incorporated into a hydrogel via rehydration with a cell lysate. The system within the hydrogel can then be induced or expressed constitutively for complete protein expression through the hydrogel. Second, cell lysate can be introduced to a hydrogel at the point of polymerization, and the entire system can be freeze-dried and rehydrated at a later point with an aqueous solution containing the inducer for the expression system encoded within the hydrogel. These methods have the potential to allow for cell-free gene networks that confer sensory capabilities to hydrogel materials, with the potential for deployment beyond the laboratory.

Numerical methods for control-based continuation of relaxation oscillations

Control-based continuation (CBC) is an experimental method that can reveal stable and unstable dynamics of physical systems. It extends the path-following principles of numerical continuation to experiments and provides systematic dynamical analyses without the need for mathematical modelling. CBC has seen considerable success in studying the bifurcation structure of mechanical systems. Nevertheless, the method is not practical for studying relaxation oscillations. Large numbers of Fourier modes are required to describe them, and the length of the experiment significantly increases when many Fourier modes are used, as the system must be run to convergence many times. Furthermore, relaxation oscillations often arise in autonomous systems, for which an appropriate phase constraint is required. To overcome these challenges, we introduce an adaptive B-spline discretisation that can produce a parsimonious description of responses that would otherwise require many Fourier modes. We couple this to a novel phase constraint that phase-locks control target and solution phase. Results are demonstrated on simulations of a slow-fast synthetic gene network and an Oregonator model. Our methods extend CBC to a much broader range of systems than have been studied so far, opening up a range of novel experimental opportunities on slow-fast systems.

Building Blocks of Artificial CRISPR-Based Systems beyond Nucleases

Tools developed in the fields of genome engineering, precise gene regulation, and synthetic gene networks have an increasing number of applications. When shared with the scientific community, these tools can be used to further unlock the potential of precision medicine and tissue engineering. A large number of different genetic elements, as well as modifications, have been used to create many different systems and to validate some technical concepts. New studies have tended to optimize or improve existing elements or approaches to create complex synthetic systems, especially those based on the relatively new CRISPR technology. In order to maximize the output of newly developed approaches and to move from proof-of-principle experiments to applications in regenerative medicine, it is important to navigate efficiently through the vast number of genetic elements to choose those most suitable for specific needs. In this review, we have collected information regarding the main genetic elements and their modifications, which can be useful in different synthetic systems with an emphasis of those based on CRISPR technology. We have indicated the most suitable elements and approaches to choose or combine in planning experiments, while providing their deeper understanding, and have also stated some pitfalls that should be avoided.

A Toolkit for Precise, Multigene Control in Saccharomyces cerevisiae.

Systems that allow researchers to precisely control the expression of genes are fundamental to biological research, biotechnology, and synthetic biology. However, few inducible gene expression systems exist that can enable simultaneous multigene control under common nutritionally favorable conditions in the important model organism and chassis Saccharomyces cerevisiae. Here we repurposed ligand binding domains from mammalian type I nuclear receptors to establish a family of up to five orthogonal synthetic gene expression systems in yeast. Our systems enable tight, independent, multigene control through addition of inert hormones and are capable of driving robust and rapid gene expression outputs, in some cases achieving up to 600-fold induction. As a proof of principle, we placed expression of four enzymes from the violacein biosynthetic pathway under independent expression control to selectively route pathway flux by addition of specific inducer combinations. Our results establish a modular, versatile, and potentially expandable toolkit for multidimensional control of gene expression in yeast that can be used to construct and control naturally occurring and synthetic gene networks.

Regulation of Transgene Expression by the Natural Sweetener Xylose.

Next-generation gene and engineered-cell therapies benefit from incorporating synthetic gene networks that can precisely regulate the therapeutic output in response to externally administered signal inputs that are safe, readily bioavailable and pleasant to take. To enable such therapeutic control, a mammalian gene switch is designed to be responsive to the natural sweetener xylose and its functionality is assessed in mouse studies. The gene switch consists of the bacterial transcription regulator XylR fused to a mammalian transactivator, which binds to an optimized promoter in the presence of xylose, thereby allowing dose-dependent transgene expression. The sensitivity of SWEET (sweetener-inducible expression of transgene) is improved by coexpressing a xylose transporter. Mice implanted with encapsulated SWEET-engineered cells show increased blood levels of cargo protein when taking xylose-sweetened water or coffee, or highly concentrated apple extract, while they do not respond to intake of a usual amount of carrots, which contain xylose. In a proof-of-concept therapeutic application study, type-1 diabetic mice engineered with insulin-expressing SWEET show lowered glycemia and increased insulin levels when administered this fairly diabetic-compliant sweetener, compared to untreated mice. A SWEET-based therapy appears to have the potential to integrate seamlessly into patients' life-style and food habits in the move toward personalized medicine.

Re] A Multi-Functional Synthetic Gene Network

Re] A Multi-Functional Synthetic Gene Network

Control-Based Continuation: A New Approach to Prototype Synthetic Gene Networks.

Control-Based Continuation (CBC) is a general and systematic method to carry out the bifurcation analysis of physical experiments. CBC does not rely on a mathematical model and thus overcomes the uncertainty introduced when identifying bifurcation curves indirectly through modeling and parameter estimation. We demonstrate, in silico, CBC applicability to biochemical processes by tracking the equilibrium curve of a toggle switch, which includes additive process noise and exhibits bistability. We compare the results obtained when CBC uses a model-free and model-based control strategy and show that both can track stable and unstable solutions, revealing bistability. We then demonstrate CBC in conditions more representative of an in vivo experiment using an agent-based simulator describing cell growth and division, cell-to-cell variability, spatial distribution, and diffusion of chemicals. We further show how the identified curves can be used for parameter estimation and discuss how CBC can significantly accelerate the prototyping of synthetic gene regulatory networks.

Evaluation of Benzinger etal.: Optogenetic circuits for dynamic signal processing.

One snapshot of the peer review process for "Synthetic gene networks recapitulate dynamic signal decoding and differential gene expression" (Benzinger etal., 2022).