SEARCH · Search bioRxiv

Results for “Synthetic Biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 469 records · Page 26Linked to original sources

bioRxiv2017

Promoter and transcription factor dynamics tune protein mean and noise strength in a quorum sensing-based feedback synthetic circuit

Gene expression is a fundamental cellular process. Its stochastic fluctuations due to intrinsic and extrinsic sources, known generically as gene expression noise, trigger both beneficial and harmful consequences for the cell behavior.\n\nControlling gene expression noise is of interest in many applications in biotechnology, biomedicine and others. Yet, control of the mean expression level is an equally desirable goal. Here, we analyze a gene synthetic network designed to reduce gene expression noise while achieving a desired mean expression level. The circuit combines a negative feedback loop over the gene of interest, and a cell-to-cell communication mechanism based on quorum sensing. We analyze the ability of the circuit to reduce noise as a function of parameters that can be tuned in the wet-lab, and the role quorum sensing plays. Intrinsic noise is generated by the inherent stochasticity of biochemical reactions. On the other hand, extrinsic noise is due to variability in the cell environment and the amounts of cellular components that affect gene expression. We develop a realistic model of the gene synthetic circuit over the population of cells using mass action kinetics and the stochastic Chemical Langevin Equation to include intrinsic noise, with parameters drawn from a distribution to account for extrinsic noise. Stochastic simulations allow us to quantify the mean expression level and noise strength of all species under different scenarios, showing good agreement with system-wide available experimental data of protein abundance and noise in E. coli. Our in silico experiments reveal significant noise attenuation in gene expression through the interplay between quorum sensing and the negative feedback, allowing control of the mean expression and variance of the protein of interest. These in silico conclusions are validated by preliminary experimental results. This gene network could have important implications as a robust protein production system in industrial biotechnology.\n\nAuthor SummaryControlling gene expression level is of interest in many applications in biotechnology, biomedicine and others. Yet, the stochastic nature of biochemical reactions plays an important role in biological systems, and cannot be disregarded. Gene expression noise resulting from this stochasticity has been studied over the past years both in vivo, and in silico using mathematical models. Nowadays, synthetic biology approaches allow to design novel biological circuits, drawing on principles elucidated from biology and engineering, for the purpose of decoupled control of mean gene expression and its variance. We propose a gene synthetic circuit with these characteristics, using negative feedback and quorum sensing based cell-to-cell communication to induce population consensus. Our in silico analysis using stochastic simulations with a realistic model reveal significant noise attenuation in gene expression through the interplay between quorum sensing and the negative feedback, allowing control of the mean expression and variance of the protein of interest. Preliminary in vivo results fully agree with the computational ones.

synthetic biology↗

bioRxiv2017

Secretion of redox-active metabolites as a general strategy for iron acquisition in plants

Iron is an essential but often poorly bioavailable nutrient due to its low solubility, especially in alkaline soils. Plants have evolved at least two distinct strategies to extract iron from soil: solubilization of ferric iron by phytosiderophores, and reduction to the ferrous form at the root surface followed by direct import. Here, we describe the discovery of a novel redox-active catecholic metabolite, termed sideretin, which derives from the coumarin fraxetin, and is the primary molecule exuded by Arabidopsis thaliana roots in response to iron deficiency. Using a combination of metabolomics, heterologous expression, and coexpression analysis we have identified two enzymes that complete the biosynthetic pathway of sideretin. Chemical characterization of synthetic sideretin and biological assays with pathway mutants suggest that sideretin is critical for iron nutrition and support a role for small molecule-mediated iron reduction in A. thaliana under iron-limiting growth conditions. Further, we show that sideretin production is evolutionarily ancient and occurs in eudicot species only distantly related to A. thaliana. In addition to sideretin, untargeted metabolomics of the root exudates of various eudicots revealed the production of structurally diverse redox-active molecules in response to iron deficiency. Our results indicate that secretion of small molecule reductants by roots may be a widespread and previously underappreciated component of the reduction-based iron uptake strategy.

plant biology↗

bioRxiv2018

A synthetic non-histone substrate provides insight into substrate targeting by the Gcn5 HAT and sirtuin HDACs

Gcn5 and sirtuins are highly conserved HAT and HDAC enzymes that were first characterised as regulators of gene expression. Although histone tails are important substrates of these enzymes, these proteins also target many non-histone substrates that participate in diverse biological processes. The mechanisms used by these enzymes to choose their non-histone substrates is unclear. In this work, we use a unique synthetic biology approach in S. cerevisiae to demonstrate that a shared target sequence can act as a determinant of substrate selection for Gcn5 and sirtuins. We also exploit this system to define specific subunits of the Gcn5-containing ADA complex as regulators of non-histone acetylations proteome-wide.

molecular biology↗

bioRxiv2018

Engineering bacteria for biogenic synthesis of chalcogenide nanomaterials

Microbes naturally build nanoscale structures, including structures assembled from inorganic materials. Here we combine the natural capabilities of microbes with engineered genetic control circuits to demonstrate the ability to control biological synthesis of chalcogenide nanomaterials in a heterologous host. We transferred reductase genes from both Shewanella sp. ANA-3 and Salmonella enterica serovar Typhimurium into an heterologous host (Escherichia coli) and examined the mechanisms that regulate the properties of biogenic nanomaterials. Expression of arsenic reductase genes and thiosulfate reductase genes in E. coli resulted in the synthesis of arsenic sulfide nanomaterials. In addition to processing the starting materials via redox enzymes, cellular components also nucleated the formation of arsenic sulfide nanomaterials. The shape of the nanomaterial was influenced by the bacterial culture, with the synthetic E. coli strain producing nanospheres and conditioned media or cultures of wild type Shewanella sp. producing nanofibers. The diameter of these nanofibers also depended on the biological context of synthesis. These results demonstrate the potential for biogenic synthesis of nanomaterials with controlled properties by combining the natural capabilities of wild microbes with the tools from synthetic biology.

bioengineering↗

bioRxiv2018

SCuPhr: A Probabilistic Framework for Cell Lineage Tree Reconstruction

Cell lineage tree reconstruction methods are developed for various tasks, such as investigating the development, differentiation, and cancer progression. Single-cell sequencing technologies enable more thorough analysis with higher resolution. We present Scuphr, a distance-based cell lineage tree reconstruction method using bulk and single-cell DNA sequencing data from healthy tissues. Common challenges of single-cell DNA sequencing, such as allelic dropouts and amplification errors, are included in Scuphr. Scuphr computes the distance between cell pairs and reconstructs the lineage tree using the neighbor-joining algorithm. With its embarrassingly parallel design, Scuphr can do faster analysis than the state-of-the-art methods while obtaining better accuracy. The methods robustness is investigated using various synthetic datasets and a biological dataset of 18 cells. Author summaryCell lineage tree reconstruction carries a significant potential for studies of development and medicine. The lineage tree reconstruction task is especially challenging for cells taken from healthy tissue due to the scarcity of mutations. In addition, the single-cell whole-genome sequencing technology introduces artifacts such as amplification errors, allelic dropouts, and sequencing errors. We propose Scuphr, a probabilistic framework to reconstruct cell lineage trees. We designed Scuphr for single-cell DNA sequencing data; it accounts for technological artifacts in its graphical model and uses germline heterozygous sites to improve its accuracy. Scuphr is embarrassingly parallel; the speed of the computational analysis is inversely proportional to the number of available computational nodes. We demonstrated that Scuphr is fast, robust, and more accurate than the state-of-the-art method with the synthetic data experiments. Moreover, in the biological data experiment, we showed Scuphr successfully identifies different clones and further obtains more support on closely related cells within clones.

bioinformatics↗

bioRxiv2015

A cell-free framework for biological systems engineering

SUMMARYWhile complex dynamic biological networks control gene expression and metabolism in all living organisms, engineering comparable synthetic networks remains challenging1,2. Conducting extensive, quantitative and rapid characterization during the design and implementation process of synthetic networks is currently severely limited due to cumbersome molecular cloning and the difficulties associated with measuring parts, components and systems in cellular hosts. Engineering gene networks in a cell-free environment promises to be an efficient and effective approach to rapidly develop novel biological systems and understand their operating regimes3-5. However, it remains questionable whether complex synthetic networks behave similarly in cells and a cell-free environment, which is critical for in vitro approaches to be of significance to biological engineering. Here we show that synthetic dynamic networks can be readily implemented, characterized, and engineered in a cell-free framework and consequently transferred to cellular hosts. We implemented and characterized the \"repressilator\"6, a three-node negative feedback oscillator in vitro. We then used our cell-free framework to engineer novel three-node, four-node, and five-node negative feedback architectures going from the characterization of circuit components to the rapid analysis of complete networks. We validated our cell-free approach by transferring these novel three-node and five-node oscillators to Escherichia coli, resulting in robust and synchronized oscillations reflecting the in vitro observation. We demonstrate that comprehensive circuit engineering can be performed in a cell-free system and that the in vitro results have direct applicability in vivo. Cell-free synthetic biology thus has the potential to drastically speed up design-build-test cycles in biological engineering and enable the quantitative characterization of synthetic and natural networks.

Synthetic Biology↗

bioRxiv2017

Verbalizing phylogenomic conflict: Representation of node congruence across competing reconstructions of the neoavian explosion

Phylogenomic research is accelerating the publication of landmark studies that aim to resolve deep divergences of major organismal groups. Meanwhile, systems for identifying and integrating the novel products of phylogenomic inference - such as newly supported clade concepts - have not kept pace. However, the ability to verbalize both node concept congruence and conflict across multiple, (in effect) simultaneously endorsed phylogenomic hypotheses, is a critical prerequisite for building synthetic data environments for biological systematics, thereby also benefitting other domains impacted by these (conflicting) inferences. Here we develop a novel solution to the conflict verbalization challenge, based on a logic representation and reasoning approach that utilizes the language of Region Connection Calculus (RCC-5) to produce consistent alignments of node concepts endorsed by incongruent phylogenomic studies. The approach employs clade concept labels to individuate concepts used by each source, even if these carry identical names. Indirect RCC-5 modeling of intensional (property-based) node concept definitions, facilitated by the local relaxation of coverage constraints, allows parent concepts to attain congruence in spite of their differentially sampled children. To demonstrate the feasibility of this approach, we align two recently published phylogenomic reconstructions of higher-level avian groups that entail strong conflict in the \"neoavian explosion\" region. According to our representations, this conflict is constituted by 26 instances of input \"whole concept\" overlap. These instances are further resolvable in the output labeling schemes and visualizations as \"split concepts\", thereby providing the provenance services needed to build truly synthetic phylogenomic data environments. Because the RCC-5 alignments fundamentally reflect the trained, logic-enabled judgments of systematic experts, future designs for such environments need to promote a culture where experts routinely assess the intensionalities of node concepts published by our peers - even and especially when we are not in agreement with each other.

evolutionary biology↗

bioRxiv2016

Experimental evolution of Escherichia coli harboring an ancient translation protein

The ability to design synthetic genes and engineer biological systems at the genome scale opens new means by which to characterize phenotypic states and the responses of biological systems to perturbations. One emerging method involves inserting artificial genes into bacterial genomes, and examining how the genome and its new genes adapt to each other. Here we report the development and implementation of a modified approach to this method, in which phylogenetically inferred genes are inserted into a microbial genome, and laboratory evolution is then used to examine the adaptive potential of the resulting hybrid genome. Specifically, we engineered an approximately 700-million-year old inferred ancestral variant of tufB, an essential gene encoding Elongation Factor Tu, and inserted it in a modern Escherichia coli genome in place of the native tufB gene. While the ancient homolog was not lethal to the cell, it did cause a two-fold decrease in organismal fitness, mainly due to reduced protein dosage. We subsequently evolved replicate hybrid bacterial populations for 2,000 generations in the laboratory, and examined the adaptive response via fitness assays, whole-genome sequencing, proteomics and biochemical assays. Hybrid lineages exhibit a general adaptive strategy in which the fitness cost of the ancient gene was ameliorated in part by up-regulation of protein production. We expect that this ancient-modern recombinant method may pave the way for the synthesis of organisms that exhibit ancient phenotypes, and that laboratory evolution of these organisms may prove useful in elucidating insights into historical adaptive processes.

Evolutionary Biology↗

bioRxiv2018

Construction and Characterization of a Synthetic Baculovirus-inducible 39K Promoter

The low expression activity and specificity of natural promoters limit the applications of genetic engineering. To construct a highly efficient synthetic inducible promoter in the Bombyx mori (Lepidoptera), we analyzed the regulatory elements and functional regions of the B. mori nucleopolyhedrovirus (BmNPV) 39K promoter. The results of truncated mutation analysis of the 39K promoter showed that the transcriptional regulatory region spanning positions -573 to -274 and +1 to +62 is essential for virus-inducible promoter activity. Further investigation using electrophoretic mobility shift assay (EMSA) revealed that the baculovirus IE-1 protein binds to the 39K promoter at the -310 to -355 region, and transcription activates the expression of 39K promoter assay. Finally, we successfully constructed a synthetic inducible promoter that increase the virus-inducing activity of other promoters using the baculovirus-inducible transcriptional activation region that binds to specific core elements of 39K (i.e., spanning the region -310 to -355). In summary, we describes a novel, synthetic, and highly efficient biological tool, namely, a virus-inducible 39K promoter, which provides endless possibilities for future gene function research, gene therapy, and pest control in genetic engineering.

bioengineering↗

bioRxiv2015

Antithetic Integral Feedback: A new motif for robust perfect adaptation in noisy biomolecular networks

Homeostasis is a running theme in biology. Often achieved through feedback regulation strategies, homeostasis allows living cells to control their internal environment as a means for surviving changing and unfavourable environments. While many endogenous homeostatic motifs have been studied in living cells, some other motifs may remain under-explored or even undiscovered. At the same time, known regulatory motifs have been mostly analyzed at the deterministic level, and the effect of noise on their regulatory function has received low attention. Here we lay the foundation for a regulation theory at the molecular level that explicitly takes into account the noisy nature of biochemical reactions and provides novel tools for the analysis and design of robust homeostatic circuits. Using these ideas, we propose a new regulation motif, which we refer to as antithetic integral feedback, and demonstrate its effectiveness as a strategy for generically regulating a wide class of reaction networks. By combining tools from probability and control theory, we show that the proposed motif preserves the stability of the overall network, steers the population of any regulated species to a desired set point, and achieves robust perfect adaptation - all with low prior knowledge of reaction rates. Moreover, our proposed regulatory motif can be implemented using a very small number of molecules and hence has a negligible metabolic load. Strikingly, the regulatory motif exploits stochastic noise, leading to enhanced regulation in scenarios where noise-free implementations result in dysregulation. Finally, we discuss the possible manifestation of the proposed antithetic integral feedback motif in endogenous biological circuits and its realization in synthetic circuits.

Systems Biology↗

bioRxiv2018

CHEMOMETRIC APPROACHES FOR DEVELOPING INFRARED NANOSENSORS TO IMAGE ANTHRACYCLINES

Generation, identification, and validation of optical probes to image molecular targets in a biological milieu remains a challenge. Synthetic molecular recognition approaches leveraging the intrinsic near-infrared fluorescence of single-walled carbon nanotubes is a promising approach for chronic biochemical imaging in tissues. However, generation of nanosensors for selective imaging of molecular targets requires a heuristic approach. Here, we present a chemometric platform for rapidly screening libraries of candidate single-walled carbon nanotube nanosensors against biochemical analytes to quantify fluorescence response to small molecules including vitamins, neurotransmitters, and chemotherapeutics. We further show this approach can be leveraged to identify biochemical analytes that selectively modulate the intrinsic near-infrared fluorescence of candidate nanosensors. Chemometric analysis thus enables identification of nanosensor-analyte hits and also nanosensor fluorescence signaling modalities such as wavelength-shifts that are optimal for translation to biological imaging. Through this approach, we identify and characterize a nanosensor for the chemotherapeutic anthracycline doxorubicin, which provides an up to 17 nm fluorescence red-shift and exhibits an 8 {micro}M limit of detection, compatible with peak circulatory concentrations of doxorubicin common in therapeutic administration. We demonstrate selectivity of this nanosensor over dacarbazine, a chemotherapeutic commonly co-injected with DOX. Lastly, we demonstrate nanosensor tissue compatibility for imaging of doxorubicin in muscle tissue by incorporating nanosensors into the mouse hindlimb and measuring nanosensor response to exogenous DOX administration. Our results motivate chemometric approaches to nanosensor discovery for chronic imaging of drug partitioning into tissues and towards real-time monitoring of drug accumulation.

biochemistry↗

bioRxiv2016

Synthetic lipid-containing scaffolds enhance production by co-localizing enzymes

Subcellular organization is critical for isolating, concentrating, and protecting biological activities. Natural subcellular organization is often achieved using co-localization of proteins on scaffold molecules, thereby enhancing metabolic fluxes and enabling co-regulation. Synthetic scaffolds extend these benefits to new biological processes, and are typically constructed from proteins or nucleic acids. To expand the range of available building materials, we use a minimal set of components from the lipid-encapsulated bacteriophage {Phi}6 to form synthetic lipid-containing scaffolds (SLSs) in E. coli. Analysis of diffusive behavior by tracking particles in live cells indicates that SLSs are >20 nm in diameter; furthermore, density measurements demonstrate that SLSs contain a mixture of lipids and proteins. The fluorescent proteins mCitrine and mCerulean can be co-localized to SLSs. To test for effects on enzymatic production, we localized two enzymes involved in indigo biosynthesis to SLSs. We observed a scaffold-dependent increase in indigo production, showing that SLSs can enhance metabolic reactions.

Synthetic Biology↗

bioRxiv2018

Regulation by competition: a hidden layer of gene regulatory network

Molecular competition is ubiquitous, essential and multifunctional throughout diverse biological processes. Competition brings about trade-offs of shared limited resources among the cellular components, and it thus introduce a hidden layer of regulatory mechanism by connecting components even without direct physical interactions. By abstracting the analogous competition mechanism behind diverse molecular systems, we built a unified coarse-grained competition motif model to systematically compare experimental evidences in these processes and analyzed general properties shared behind them. We could predict in what molecular environments competition would reveal threshold behavior or display a negative linear dependence. We quantified how competition can shape regulator-target dose-response curve, modulate dynamic response speed, control target expression noise, and introduce correlated fluctuations between targets. This work uncovered the complexity and generality of molecular competition effect, which might act as a hidden regulatory mechanism with multiple functions throughout biological networks in both natural and synthetic systems.

systems biology↗

bioRxiv2018

Engineering Translational Resource Allocation Controllers: Mechanistic Models, Design Guidelines, and Potential Biological Implementations

The use of orthogonal ribosomes in combination with dynamic resource allocation controllers is a promising approach for relieving the negative effects of cellular resource limitations on the modularity of synthetic gene circuits. Here, we develop a detailed mechanistic model of gene expression and resource allocation, which when simplified to a tractable level of complexity, allows the rational design of translational resource allocation controllers. Analysis of this model reveals a fundamental design trade-off; that reducing coupling acts to decrease gene expression. Through a sensitivity analysis of the experimentally tuneable controller parameters, we identify how each controller design parameter affects the overall closed-loop behaviour of the system, leading to a detailed set of design guidelines for optimally managing this trade-off. Based on our designs, we evaluated a number of alternative potential experimental implementations of the proposed system using commonly available biological components. Finally, we show that the controller is capable of dynamically allocating ribosomes as needed to restore modularity in a number of more complex synthetic circuits, such as the repressilator, and activation cascades composed of multiple interacting modules.

synthetic biology↗

bioRxiv2018

Seamless Assembly of Biological Parts into Functional Devices and Higher Order Multi-Device Systems.

A new method is described for the seamless assembly of independent, prefabricated and functionally tested blunt-end, double strand nucleic acid parts (DNA fragments) into more complex biological devices (vectors) and higher order multi-device systems. Individual parts include bacterial selection markers, bacterial origins of replication, promoters from a variety of different species, transcription terminators, shuttle sequences and a variety of \"N\" and \"C\" terminal solubility/affinity expression tags. Pre-assembly modification of parts with DNA modifying enzymes is not required. Seamless assembly of multiple parts is accomplished in a single step using a specialized thermostable enzyme blend in about 30 minutes. Combinatorial assembly of parts is an inherent feature of the new process, substantially simplifying device and system optimization. To underscore the utility of the new process, parts were assembled into several protein expression devices in order to identify the optimal expression construct for a model target gene, as an example of the utility of the assembly process, and a higher order multi-device system is also described, for the over-expression of a four-enzyme bio-synthetic pathway, and optimized for end-product accumulation in E. coli as a paradigm for how this assembly process could be used to address the assembly of more complex biological pathways.

synthetic biology↗

bioRxiv2017

Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS)

Neurons use sequences of action potentials (spikes) to convey information across neuronal networks. In neurophysiology experiments, information about external stimuli or behavioral tasks has been frequently characterized in term of neuronal firing rate. The firing rate is conventionally estimated by averaging spiking responses across multiple similar experiments (or trials). However, there exist a number of applications in neuroscience research that require firing rate to be estimated on a single trial basis. Estimating firing rate from a single trial is a challenging problem and current state-of-the-art methods do not perform well. To address this issue, we develop a new method for estimating firing rate based on kernel smoothing technique that considers the bandwidth as a random variable with prior distribution that is adaptively updated under a Bayesian framework. By carefully selecting the prior distribution together with Gaussian kernel function, an analytical expression can be achieved for the kernel bandwidth. We refer to the proposed method as Bayesian Adaptive Kernel Smoother (BAKS). We evaluate the performance of BAKS using synthetic spike train data generated by biologically plausible models: inhomogeneous Gamma (IG) and inhomogeneous inverse Gaussian (IIG). We also apply BAKS to real spike train data from non-human primate (NHP) motor and visual cortex. We benchmark the proposed method against the established and previously reported methods. These include: optimized kernel smoother (OKS), variable kernel smoother (VKS), local polynomial fit (Locfit), and Bayesian adaptive regression splines (BARS). Results using both synthetic and real data demonstrate that the proposed method achieves better performance compared to competing methods. This suggests that the proposed method could be useful for understanding the encoding mechanism of neurons in cognitive-related tasks. The proposed method could also potentially improve the performance of brain-machine interface (BMI) decoder that relies on estimated firing rate as the input.

neuroscience↗

bioRxiv2016

A two-state photoconversion model predicts the spectral response dynamics of optogenetic systems

In optogenetics, light signals are used to control genetically engineered photoreceptors, and in turn manipulate biological pathways with unmatched precision. Recently, evolved photoreceptors with diverse in vitro-measured wavelength and intensity-dependent photoswitching properties have been repurposed for synthetic control of gene expression, proteolysis, and numerous other cellular processes. However, the relationship between the input light spectrum and in vivo photoreceptor response dynamics is poorly understood, restricting the utility of these optogenetic tools. Here, we advance a classic in vitro two-state photoreceptor model to reflect the in vivo environment, and combine it with simplified mathematical descriptions of signal transduction and output gene expression through our previously engineered green/red and red/far red photoreversible bacterial two-component systems (TCSs). Additionally, we leverage our recent open-source optical instrument to develop a workflow of spectral and dynamical characterization experiments to parameterize the model for both TCSs. To validate our approach, we challenge the model to predict experimental responses to a series of complex light signals very different from those used during parameterization. We find that the model generalizes remarkably well, predicting the results of all categories of experiments with high quantitative accuracy for both systems. Finally, we exploit this predictive power to program two simultaneous and independent dynamical gene expression signals in bacteria expressing both TCSs. This multiplexed gene expression programming approach will enable entirely new studies of how metabolic, signaling, and decision-making pathways integrate multiple gene expression signals. Additionally, our approach should be compatible with a wide range of optogenetic tools and model organisms.\n\nSignificance statementLight-switchable signaling pathways (optogenetic tools) enable precision studies of how biochemical networks underlie cellular behaviors. We have developed a versatile mathematical model based on a two-state photoconversion mechanism that we have applied to the E. coli CcaSR and Cph8-OmpR optogenetic tools. This model enables accurate prediction of the gene expression response to virtually any light source or mixture of light sources. We express both optogenetic tools in the same cell and apply our model to program two simultaneous and independent gene expression signals in the same cell. This method can be used to study how biological pathways integrate multiple inputs and should be extensible to other optogenetic tools and host organisms.

synthetic biology↗

bioRxiv2018

Architectural Principles for Characterizing the Performance of Sequestration Feedback Networks

As we begin to design increasingly complex synthetic biomolecular systems, it is essential to develop rational design methodologies that yield predictable circuit performance. Here we apply theoretical tools from the theory of control and dynamical systems to yield practical insights into the architecture and function of a particular class of biological feedback circuit. Specifically, we show that it is possible to analytically characterize both the operating regime and performance tradeoffs of a sequestration feedback circuit architecture. Further, we demonstrate how these principles can be applied to inform the design process of a particular synthetic feedback circuit.

synthetic biology↗