Fine-tuning gene networks using simple sequence repeats

Automated characterization and analysis of expression compatibility between regulatory sequences and metabolic genes in Escherichia coli

Utilizing standardized artificial regulatory sequences to fine-tuning the expression of multiple metabolic pathways/genes is a key strategy in the creation of efficient microbial cell factories. However, when regulatory sequence expression strengths are characterized using only a few reporter genes, they may not be applicable across diverse genes. This introduces great uncertainty into the precise regulation of multiple genes at multiple expression levels. To address this, our study adopted a fluorescent protein fusion strategy for a more accurate assessment of target protein expression levels. We combined 41 commonly-used metabolic genes with 15 regulatory sequences, yielding an expression dataset encompassing 520 unique combinations. This dataset highlighted substantial variation in protein expression level under identical regulatory sequences, with relative expression levels ranging from 2.8 to 176-fold. It also demonstrated that improving the strength of regulatory sequences does not necessarily lead to significant improvements in the expression levels of target proteins. Utilizing this dataset, we have developed various machine learning models and discovered that the integration of promoter regions, ribosome binding sites, and coding sequences significantly improves the accuracy of predicting protein expression levels, with a Spearman correlation coefficient of 0.72, where the promoter sequence exerts a predominant influence. Our study aims not only to provide a detailed guide for fine-tuning gene expression in the metabolic engineering of Escherichia coli but also to deepen our understanding of the compatibility issues between regulatory sequences and target genes.

Transfer learning for cross-context prediction of protein expression from 5'UTR sequence

Model-guided DNA sequence design can accelerate the reprogramming of living cells. It allows us to engineer more complex biological systems by removing the need to physically assemble and test each potential design. While mechanistic models of gene expression have seen some success in supporting this goal, data-centric, deep learning-based approaches often provide more accurate predictions. This accuracy, however, comes at a cost - a lack of generalization across genetic and experimental contexts that has limited their wider use outside the context in which they were trained. Here, we address this issue by demonstrating how a simple transfer learning procedure can effectively tune a pre-trained deep learning model to predict protein translation rate from 5' untranslated region (5'UTR) sequence for diverse contexts in Escherichia coli using a small number of new measurements. This allows for important model features learnt from expensive massively parallel reporter assays to be easily transferred to new settings. By releasing our trained deep learning model and complementary calibration procedure, this study acts as a starting point for continually refined model-based sequence design that builds on previous knowledge and future experimental efforts.

Utilizing 5' UTR Engineering Enables Fine-Tuning of Multiple Genes within Operons to Balance Metabolic Flux in Bacillus subtilis

The application of synthetic biology tools to modulate gene expression to increase yield has been thoroughly demonstrated as an effective and convenient approach in industrial production. In this study, we employed a high-throughput screening strategy to identify a 5' UTR sequence from the genome of B. subtilis 168. This sequence resulted in a 5.8-fold increase in the expression level of EGFP. By utilizing the 5' UTR sequence to overexpress individual genes within the rib operon, it was determined that the genes ribD and ribAB serve as rate-limiting enzymes in the riboflavin synthesis pathway. Constructing a 5' UTR library to regulate EGFP expression resulted in a variation range in gene expression levels exceeding 100-fold. Employing the same 5' UTR library to regulate the expression of EGFP and mCherry within the operon led to a change in the expression ratio of these two genes by over 10,000-fold. So, employing a 5' UTR library to modulate the expression of the rib operon gene and construct a synthetic rib operon resulted in a 2.09-fold increase in riboflavin production. These results indicate that the 5' UTR sequence identified and characterized in this study can serve as a versatile synthetic biology toolkit for achieving complex metabolic network reconstruction. This toolkit can facilitate the fine-tuning of gene expression to produce target products.

Using design of experiments to guide genetic optimization of engineered metabolic pathways

Design of experiments (DoE) is a term used to describe the application of statistical approaches to interrogate the impact of many variables on the performance of a multivariate system. It is commonly used for process optimization in fields such as chemical engineering and material science. Recent advances in the ability to quantitatively control the expression of genes in biological systems open up the possibility to apply DoE for genetic optimization. In this review targeted to genetic and metabolic engineers, we introduce several approaches in DoE at a high level and describe instances wherein these were applied to interrogate or optimize engineered genetic systems. We discuss the challenges of applying DoE and propose strategies to mitigate these challenges.

ONE-SENTENCE SUMMARY

This is a review of literature related to applying Design of Experiments for genetic optimization.

A smart RBS library and its prediction model for robust and accurate fine-tuning of gene expression in Bacillus species

Bacillus species, such as Bacillus subtilis and Bacillus licheniformis, are important industrial bacteria. However, there is a lack of standardized and predictable genetic tools for convenient and reproducible assembly of genetic modules in Bacillus species to realize their full potential. In this study, we constructed a Ribosome Binding Site (RBS) library in B. licheniformis, which provides incremental regulation of expression levels over a 104-fold range. Additionally, we developed a model to quantify the resulting translation rates. We successfully demonstrated the robust expression of various target genes using the RBS library and showed that the model accurately predicts the translation rates of arbitrary coding genes. Importantly, we also extended the use of the RBS library and prediction model to B. subtilis, B. thuringiensis, and B. amyloliquefacie. The versatility of the RBS library and its prediction model enables quantification of biological behavior, facilitating reliable forward engineering of gene expression.

Programmable regulation of translation by harnessing the CRISPR-Cas13 system

The ability to control protein expression at both the transcriptional and post-transcriptional levels is instrumental for the cell to integrate multiple molecular signals and then reach high operational sophistication. Although challenging, fully artificial regulations at different levels are required for boosting systems and synthetic biology. Here, we report the development of a novel framework to regulate translation by repurposing the CRISPR-Cas13 immune system, which uses an RNA-guided ribonuclease. By exploiting a cell-free expression system for prototyping gene regulatory structures, our results demonstrate that CRISPR-dCas13a ribonucleoproteins (d means catalytically dead) can be programmed to repress or activate translation initiation. The performance assessment of the engineered systems also revealed guide RNA design principles. Moreover, we show that the system can work in vivo. This development complements the ability to regulate transcription with other CRISPR-Cas systems and offers potential applications.

Engineered multiple translation initiation sites: a novel tool to enhance protein production in Bacillus licheniformis and other industrially relevant bacteria

Gram-positive bacteria are a nascent platform for synthetic biology and metabolic engineering that can provide new opportunities for the production of biomolecules. However, the lack of standardized methods and genetic parts is a major obstacle towards attaining the acceptance and widespread use of Gram-positive bacterial chassis for industrial bioproduction. In this study, we have engineered a novel mRNA leader sequence containing more than one ribosomal binding site (RBS) which could initiate translation from multiple sites, vastly enhancing the translation efficiency of the Gram-positive industrial strain Bacillus licheniformis. This is the first report elucidating the impact of more than one RBS to initiate translation and enhance protein output in B. licheniformis. We also explored the application of more than one RBS for both intracellular and extracellular protein production in B. licheniformis to demonstrate its efficiency, consistency and potential for biotechnological applications. Moreover, we applied these concepts for use in other industrially relevant Gram-positive bacteria, such as Bacillus subtilis and Corynebacterium glutamicum. In all, a highly efficient and robust broad-host expression element has been designed to strengthen and fine-tune the protein outputs for the use of bioproduction in microbial cell factories.

Independent control of mean and noise by convolution of gene expression distributions

Gene expression noise can reduce cellular fitness or facilitate processes such as alternative metabolism, antibiotic resistance, and differentiation. Unfortunately, efforts to study the impacts of noise have been hampered by a scaling relationship between noise and expression level from individual promoters. Here, we use theory to demonstrate that mean and noise can be controlled independently by expressing two copies of a gene from separate inducible promoters in the same cell. We engineer low and high noise inducible promoters to validate this result in Escherichia coli, and develop a model that predicts the experimental distributions. Finally, we use our method to reveal that the response of a promoter to a repressor is less sensitive with higher repressor noise and explain this result using a law from probability theory. Our approach can be applied to investigate the effects of noise on diverse biological pathways or program cellular heterogeneity for synthetic biology applications.

Context-Dependent Stability and Robustness of Genetic Toggle Switches with Leaky Promoters

Multistable switches are ubiquitous building blocks in both systems and synthetic biology. Given their central role, it is thus imperative to understand how their fundamental properties depend not only on the tunable biophysical properties of the switches themselves, but also on their genetic context. To this end, we reveal in this article how these factors shape the essential characteristics of toggle switches implemented using leaky promoters such as their stability and robustness to noise, both at single-cell and population levels. In particular, our results expose the roles that competition for scarce transcriptional and translational resources, promoter leakiness, and cell-to-cell heterogeneity collectively play. For instance, the interplay between protein expression from leaky promoters and the associated cost of relying on shared cellular resources can give rise to tristable dynamics even in the absence of positive feedback. Similarly, we demonstrate that while promoter leakiness always acts against multistability, resource competition can be leveraged to counteract this undesirable phenomenon. Underpinned by a mechanistic model, our results thus enable the context-aware rational design of multistable genetic switches that are directly translatable to experimental considerations, and can be further leveraged during the synthesis of large-scale genetic systems using computer-aided biodesign automation platforms.

Precise and reliable control of gene expression in Agrobacterium tumefaciens

Agrobacterium tumefaciens is a soil-borne bacterium that is known for its DNA delivery ability and widely exploited for plant transformation. Despite continued interest in improving the utility of the organism, the lack of well-characterized engineering tools limits the realization of its full potential. Here, we present a synthetic biology toolkit that enables precise and effective control of gene expression in A. tumefaciens. We constructed and characterized six inducible expression systems. Then, we optimized the one regulated by cumic acid through amplifier introduction and promoter engineering and evaluated its 15 cognate promoters. To establish fine-tunability, we constructed a series of spacers and a promoter library to systematically modulate both translational and transcriptional rates. We finally demonstrated the application of the tools by co-expressing genes with altered expression levels using a single signal. This study provides precise expression tools for A. tumefaciens, facilitating rational engineering of the bacterium for advanced plant biotechnological applications.

Construction of an Integrated mCherry Red Fluorescent Protein Expression System for Labeling and Tracing in Lactiplantibacillus plantarum WCFS1

Thorough intestinal adhesion and colonization greatly promote the probiotic properties of lactic acid bacteria (LAB). Labeling and tracing with fluorescent proteins are effective and reliable for studying the in vivo physiological activities of LAB including localization, adhesion, and colonization. Lactiplantibacillus plantarum WCFS1 was successfully traced with a red fluorescent protein (RFP), which was expressed by the bacteria-carrying recombinant plasmids. In this study, we aimed to construct a stable RFP mCherry expression system, whose encoding gene was integrated into the bacterial chromosome via double-crossed homologous recombination, and use it for labeling WCFS1 with the goal of avoiding the potential loss of non-chromosomal plasmids along with intestinal growth. First, the constitutive expression of the mCherry protein was improved after adjusting the length of the spacer between the promoter and the gene start codon. Then, the optimized mCherry gene expression cassette was integrated into the chromosome of WCFS1. The resulting strain had normal unimpaired growth and strong fluorescent signals, even after 100 generations, indicating its stability. Furthermore, quantitative polymerase chain reaction (PCR) results revealed a strong positive correlation between the fluorescence intensity of the strain and the number of viable cells, demonstrating its potential usage for the quantification of in vivo WCFS1 cells. Finally, the increased adhesion ability of WCFS1 due to the recombinant expression of the bsh gene was visualized and evaluated using fluorescence intensity, the results of which were consistent with those obtained using the previously established quantification methods. These results suggest that the chromosomal-integrated mCherry labeling system can be extensively used to examine the distribution, colonization, and survival of LAB in vivo in order to determine the mechanism of its probiotic function.

Biosensor-Based Multigene Pathway Optimization for Enhancing the Production of Glycolate

Glycolate is widely used in industry, especially in the fields of chemical cleaning, cosmetics, and medical materials, and has broad market prospects for the future. Recent advances in metabolic engineering and synthetic biology have significantly improved the titer and yield of glycolate. However, an expensive inducer was used in previous studies, which is not feasible for use in large-scale industrial fermentations. To constitutively biosynthesize glycolate, the expression level of each gene of the glycolate synthetic pathway needs to be systemically optimized. The main challenge of multigene pathway optimization is being able to select or screen the optimum strain from the randomly assembled library by an efficient high-throughput method within a short time. To overcome these challenges, we firstly established a glycolate-responsive biosensor and developed agar plate- and 48-well deep-well plate-scale high-throughput screening methods for the rapid screening of superior glycolate producers from a large library. A total of 22 gradient-strength promoter-5'-untranslated region (UTR) complexes were randomly cloned upstream of the genes of the glycolate synthetic pathway, generating a large random assembled library. After rounds of screening, the optimum strain was obtained from 6 × 10⁵ transformants in a week, and it achieved a titer of 40.9 ± 3.7 g/liter glycolate in a 5-liter bioreactor. Furthermore, high expression levels of the enzymes YcdW and GltA were found to promote glycolate production, whereas AceA has no obvious impact on glycolate production. Overall, the glycolate biosensor-based pathway optimization strategy presented in this work provides a paradigm for other multigene pathway optimizations. IMPORTANCE The use of strong promoters, such as pTrc and T7, to control gene expression not only needs the addition of expensive inducers but also results in excessive protein expression that may result in unbalanced metabolic flux and the waste of cellular building blocks and energy. To balance the metabolic flux of glycolate biosynthesis, the expression level of each gene needs to be systemically optimized in a constitutive manner. However, the lack of high-throughput screening methods restricted glycolate synthetic pathway optimization. Our work firstly established a glycolate-response biosensor, and agar plate- and 48-well plate-scale high-throughput screening methods were then developed for the rapid screening of optimum pathways from a large library. Finally, we obtained a glycolate-producing strain with good biosynthetic performance, and the use of the expensive inducer isopropyl-β-d-thiogalactopyranoside (IPTG) was avoided, which broadens our understanding of the mechanism of glycolate synthesis.

Stability and Robustness of Unbalanced Genetic Toggle Switches in the Presence of Scarce Resources

While the vision of synthetic biology is to create complex genetic systems in a rational fashion, system-level behaviors are often perplexing due to the context-dependent dynamics of modules. One major source of context-dependence emerges due to the limited availability of shared resources, coupling the behavior of disconnected components. Motivated by the ubiquitous role of toggle switches in genetic circuits ranging from controlling cell fate differentiation to optimizing cellular performance, here we reveal how their fundamental dynamic properties are affected by competition for scarce resources. Combining a mechanistic model with nullcline-based stability analysis and potential landscape-based robustness analysis, we uncover not only the detrimental impacts of resource competition, but also how the unbalancedness of the switch further exacerbates them. While in general both of these factors undermine the performance of the switch (by pushing the dynamics toward monostability and increased sensitivity to noise), we also demonstrate that some of the unwanted effects can be alleviated by strategically optimized resource competition. Our results provide explicit guidelines for the context-aware rational design of toggle switches to mitigate our reliance on lengthy and expensive trial-and-error processes, and can be seamlessly integrated into the computer-aided synthesis of complex genetic systems.

An Automated Model Test System for Systematic Development and Improvement of Gene Expression Models

Gene expression models greatly accelerate the engineering of synthetic metabolic pathways and genetic circuits by predicting sequence-function relationships and reducing trial-and-error experimentation. However, developing models with more accurate predictions remains a significant challenge. Here we present a model test system that combines advanced statistics, machine learning, and a database of 9862 characterized genetic systems to automatically quantify model accuracies, accept or reject mechanistic hypotheses, and identify areas for model improvement. We also introduce model capacity, a new information theoretic metric for correct cross-data-set comparisons. We demonstrate the model test system by comparing six models of translation initiation rate, evaluating 100 mechanistic hypotheses, and uncovering new sequence determinants that control protein expression levels. We then applied these results to develop a biophysical model of translation initiation rate with significant improvements in accuracy. Automated model test systems will dramatically accelerate the development of gene expression models, and thereby transition synthetic biology into a mature engineering discipline.

Tunable Gene Expression System Independent of Downstream Coding Sequence

Fine control of the expression levels of proteins constitutes a major challenge in synthetic biology and metabolic engineering. However, the dependence of translation initiation on the downstream coding sequence (CDS) obscures accurate prediction of the protein expression levels from mRNA sequences. Here, we present a tunable gene-expression system comprising 24 expression cassettes that produce predefined relative expression levels of proteins ranging from 0.001 to 1 without being influenced by the downstream CDS. To validate the practical utility of the tunable expression system, it was applied to a synthetic circuit displaying three states of fluorescence depending on the difference in protein expression levels. To demonstrate the suitability of application to metabolic engineering, this system was used to diversify the levels of key metabolic enzymes. As a result, expression-optimized strains were capable of producing 2.25 g/L of cadaverine, 2.59 g/L of L-proline, and 95.7 mg/L of 1-propanol. Collectively, the tunable expression system could be utilized to optimize genetic circuits for desired operation and to optimize metabolic fluxes through biosynthetic pathways for enhancing production yields of bioproducts. This tunable system will be useful for studying basic and applied biological sciences in addition to applications in synthetic biology and metabolic engineering.

Unraveling the Complex Interplay of Fis and IHF Through Synthetic Promoter Engineering

Bacterial promoters are usually formed by multiple cis-regulatory elements recognized by a plethora of transcriptional factors (TFs). From those, global regulators are key elements since these TFs are responsible for the regulation of hundreds of genes in the bacterial genome. For instance, Fis and IHF are global regulators that play a major role in gene expression control in Escherichia coli, and usually, multiple cis-regulatory elements for these proteins are present at target promoters. Here, we investigated the relationship between the architecture of the cis-regulatory elements for Fis and IHF in E. coli. For this, we analyze 42 synthetic promoter variants harboring consensus cis-elements for Fis and IHF at different distances from the core -35/-10 region and in various numbers and combinations. We first demonstrated that although Fis preferentially recognizes its consensus cis-element, it can also recognize, to some extent, the consensus-binding site for IHF, and the same was true for IHF, which was also able to recognize Fis binding sites. However, changing the arrangement of the cis-elements (i.e., the position or number of sites) can completely abolish the non-specific binding of both TFs. More remarkably, we demonstrated that combining cis-elements for both TFs could result in Fis and IHF repressed or activated promoters depending on the final architecture of the promoters in an unpredictable way. Taken together, the data presented here demonstrate how small changes in the architecture of bacterial promoters could result in drastic changes in the final regulatory logic of the system, with important implications for the understanding of natural complex promoters in bacteria and their engineering for novel applications.

Interaction variability shapes succession of synthetic microbial ecosystems

Cellular interactions are a major driver for the assembly and functioning of microbial communities. Their strengths are shown to be highly variable in nature; however, it is unclear how such variations regulate community behaviors. Here we construct synthetic Lactococcus lactis consortia and mathematical models to elucidate the role of interaction variability in ecosystem succession and to further determine if casting variability into modeling empowers bottom-up predictions. For a consortium of bacteriocin-mediated cooperation and competition, we find increasing the variations of cooperation, from either altered labor partition or random sampling, drives the community into distinct structures. When the cooperation and competition are additionally modulated by pH, ecosystem succession becomes jointly controlled by the variations of both interactions and yields more diversified dynamics. Mathematical models incorporating variability successfully capture all of these experimental observations. Our study demonstrates interaction variability as a key regulator of community dynamics, providing insights into bottom-up predictions of microbial ecosystems.

Cellular interactions are a major driver of microbial communities and shown highly variable in strength. Here the authors construct synthetic consortia and mathematical models to elucidate the role of interaction variability in driving ecosystem succession.

A synthetic system for asymmetric cell division in Escherichia coli

We describe a synthetic genetic circuit for controlling asymmetric cell division in E. coli in which a progenitor cell creates a differentiated daughter cell while retaining its original phenotype. Specifically, we engineered an inducible system that can bind and segregate plasmid DNA to a single position in the cell. Upon cell division, co-localized plasmids are kept by one and only one of the daughter cells. The other daughter cell receives no plasmid DNA and is hence irreversibly differentiated from its sibling. In this way, we achieved asymmetric cell division through asymmetric plasmid partitioning. We then used this system to achieve physical separation of genetically distinct cells by tying motility to differentiation. Finally, we characterized an orthogonal inducible circuit that enables the simultaneous asymmetric partitioning of two plasmid species, resulting in cells that have four distinct differentiated states. These results point the way towards engineering multicellular systems from prokaryotic hosts.

Diversity, versatility and complexity of bacterial gene regulation mechanisms: opportunities and drawbacks for applications in synthetic biology

Gene expression occurs in two essential steps: transcription and translation. In bacteria, the two processes are tightly coupled in time and space, and highly regulated. Tight regulation of gene expression is crucial. It limits wasteful consumption of resources and energy, prevents accumulation of potentially growth inhibiting reaction intermediates, and sustains the fitness and potential virulence of the organism in a fluctuating, competitive and frequently stressful environment. Since the onset of studies on regulation of enzyme synthesis, numerous distinct regulatory mechanisms modulating transcription and/or translation have been discovered. Mostly, various regulatory mechanisms operating at different levels in the flow of genetic information are used in combination to control and modulate the expression of a single gene or operon. Here, we provide an extensive overview of the very diverse and versatile bacterial gene regulatory mechanisms with major emphasis on their combined occurrence, intricate intertwinement and versatility. Furthermore, we discuss the potential of well-characterized basal expression and regulatory elements in synthetic biology applications, where they may ensure orthogonal, predictable and tunable expression of (heterologous) target genes and pathways, aiming at a minimal burden for the host.

Optogenetic control of Bacillus subtilis gene expression

The Gram-positive bacterium Bacillus subtilis exhibits complex spatial and temporal gene expression signals. Although optogenetic tools are ideal for studying such processes, none has been engineered for this organism. Here, we port a cyanobacterial light sensor pathway comprising the green/red photoreversible two-component system CcaSR, two metabolic enzymes for production of the chromophore phycocyanobilin (PCB), and an output promoter to control transcription of a gene of interest into B. subtilis. Following an initial non-functional design, we optimize expression of pathway genes, enhance PCB production via a translational fusion of the biosynthetic enzymes, engineer a strong chimeric output promoter, and increase dynamic range with a miniaturized photosensor kinase. Our final design exhibits over 70-fold activation and rapid response dynamics, making it well-suited to studying a wide range of gene regulatory processes. In addition, the synthetic biology methods we develop to port this pathway should make B. subtilis easier to engineer in the future.

Cell-free microcompartmentalised transcription-translation for the prototyping of synthetic communication networks

Recent efforts in synthetic biology have shown the possibility of engineering distributed functions in populations of living cells, which requires the development of highly orthogonal, genetically encoded communication pathways. Cell-free transcription-translation (TXTL) reactions encapsulated in microcompartments enable prototyping of molecular communication channels and their integration into engineered genetic circuits by mimicking critical cell features, such as gene expression, cell size, and cell individuality within a community. In this review, we discuss the uses of cell-free transcription-translation reactions for the development of synthetic genetic circuits, with a special focus on the use of microcompartments supporting this reaction. We highlight several studies where molecular communication between non-living microcompartments and living cells have been successfully engineered.

Design of Adjacent Transcriptional Regions to Tune Gene Expression and Facilitate Circuit Construction

Polycistronic architecture is common for synthetic gene circuits, however, it remains unknown how expression of one gene is affected by the presence of other genes/noncoding regions in the operon, termed adjacent transcriptional regions (ATR). Here, we constructed synthetic operons with a reporter gene flanked by different ATRs, and we found that ATRs with high GC content, small size, and low folding energy lead to high gene expression. Based on these results, we built a model of gene expression and generated a metric that takes into account ATRs. We used the metric to design and construct logic gates with low basal expression and high sensitivity and nonlinearity. Furthermore, we rationally designed synthetic 5'ATRs with different GC content and sizes to tune protein expression levels over a 300-fold range and used these to build synthetic toggle switches with varying basal expression and degrees of bistability. Our comprehensive model and gene expression metric could facilitate the future engineering of more complex synthetic gene circuits.

A novel riboregulator switch system of gene expression for enhanced microbial production of succinic acid

In this paper, a novel riboregulator Switch System of Gene Expression including an OFF-TO-ON switch and an ON-TO-OFF switch was designed to regulate the expression state of target genes between "ON" and "OFF" by switching the identifiability of ribosome recognition site (RBS) based on the thermodynamic stability of different RNA-RNA hybridizations between RBS and small noncoding RNAs. The proposed riboregulator switch system was employed for the fermentative production of succinic acid using an engineered strain of E. coli JW1021, during which the expression of mgtC gene was controlled at "ON" state and that of pepc and ecaA genes were controlled at the "OFF" state in the lag phase and switched to the "OFF" and "ON" state once the strain enters the logarithmic phase. The results showed that using the strain of JW1021, the yield and productivity of succinic acid can reach 0.91 g g^-1 and 3.25 g L^-1 h^-1, respectively, much higher than those using the strains without harboring the riboregulator switch system.

Mapping the operational landscape of microRNAs in synthetic gene circuits

MicroRNAs are a class of short, noncoding RNAs that are ubiquitous modulators of gene expression, with roles in development, homeostasis, and disease. Engineered microRNAs are now frequently used as regulatory modules in synthetic biology. Moreover, synthetic gene circuits equipped with engineered microRNA targets with perfect complementarity to endogenous microRNAs establish an interface with the endogenous milieu at the single-cell level. The function of engineered microRNAs and sensor systems is typically optimized through extensive trial-and-error. Here, using a combination of synthetic biology experimentation in human embryonic kidney cells and quantitative analysis, we investigate the relationship between input genetic template abundance, microRNA concentration, and output under microRNA control. We provide a framework that employs the complete operational landscape of a synthetic gene circuit and enables the stepwise development of mathematical models. We derive a phenomenological model that recapitulates experimentally observed nonlinearities and contains features that provide insight into the microRNA function at various abundances. Our work facilitates the characterization and engineering of multi-component genetic circuits and specifically points to new insights on the operation of microRNAs as mediators of endogenous information and regulators of gene expression in synthetic biology.

A set of synthetic versatile genetic control elements for the efficient expression of genes in Actinobacteria

The design and engineering of secondary metabolite gene clusters that are characterized by complicated genetic organization, require the development of collections of well-characterized genetic control elements that can be reused reliably. Although a few intrinsic terminators and RBSs are used routinely, their translation and termination efficiencies have not been systematically studied in Actinobacteria. Here, we analyzed the influence of the regions surrounding RBSs on gene expression in these bacteria. We demonstrated that inappropriate RBSs can reduce the expression efficiency of a gene to zero. We developed a genetic device – an in vivo RBS-selector – that allows selection of an optimal RBS for any gene of interest, enabling rational control of the protein expression level. In addition, a genetic tool that provides the opportunity for measurement of termination efficiency was developed. Using this tool, we found strong terminators that lead to a 17–100-fold reduction in downstream expression and are characterized by sufficient sequence diversity to reduce homologous recombination when used with other elements. For the first time, a C-terminal degradation tag was employed for the control of protein stability in Streptomyces. Finally, we describe a collection of regulatory elements that can be used to control metabolic pathways in Actinobacteria.

High-throughput recombinant protein expression in Escherichia coli: current status and future perspectives

The ease of genetic manipulation, low cost, rapid growth and number of previous studies have made Escherichia coli one of the most widely used microorganism species for producing recombinant proteins. In this post-genomic era, challenges remain to rapidly express and purify large numbers of proteins for academic and commercial purposes in a high-throughput manner. In this review, we describe several state-of-the-art approaches that are suitable for the cloning, expression and purification, conducted in parallel, of numerous molecules, and we discuss recent progress related to soluble protein expression, mRNA folding, fusion tags, post-translational modification and production of membrane proteins. Moreover, we address the ongoing efforts to overcome various challenges faced in protein expression in E. coli, which could lead to an improvement of the current system from trial and error to a predictable and rational design.

Multiplexing Engineered Receptors for Multiparametric Evaluation of Environmental Ligands

Engineered cell-based therapies comprise a promising, emerging biomedical technology. Broad utilization of this strategy will require new approaches for implementing sophisticated functional programs, such as sensing and responding to the environment in a defined fashion. Toward this goal, we investigated whether our self-contained receptor and signal transduction system (MESA) could be multiplexed to evaluate extracellular cues, with a focus on elucidating principles governing the integration of such engineered components. We first developed a set of hybrid promoters that exhibited AND gate activation by two transcription factors. We then evaluated these promoters when paired with two MESA receptors and various ligand combinations. Unexpectedly, although the multiplexed system exhibited distinct responses to ligands applied individually and in combination, the same synergy was not observed as when promoters were characterized with soluble transcription factors. Therefore, we developed a mechanistic computational model leveraging these observations, to both improve our understanding of how the receptors and promoters interface and to guide the design and implementation of future systems. Notably, the model explicitly accounts for the impact of intercellular variation on system characterization and performance. Model analysis identified key factors that affect the current receptors and promoters, and enabled an in silico exploration of potential modifications that inform the design of improved logic gates and their robustness to intercellular variation. Ultimately, this quantitative design-driven approach may guide the use and multiplexing of synthetic receptors for diverse custom biological functions beyond the case study considered here.

The Timing of Transcriptional Regulation in Synthetic Gene Circuits

Transcription factors and their target promoters are central to synthetic biology. By arranging these components into novel gene regulatory circuits, synthetic biologists have been able to create a wide variety of phenotypes, including bistable switches, oscillators, and logic gates. However, transcription factors (TFs) do not instantaneously regulate downstream targets. After the gene encoding a TF is turned on, the gene must first be transcribed, the transcripts must be translated, and sufficient TF must accumulate in order to bind operator sites of the target promoter. The time to complete this process, here called the "signaling time," is a critical aspect in the design of dynamic regulatory networks, yet it remains poorly characterized. In this work, we measured the signaling time of two TFs in Escherichia coli commonly used in synthetic biology: the activator AraC and the repressor LacI. We found that signaling times can range from a few to tens of minutes, and are affected by the expression rate of the TF. Our single-cell data also show that the variability of the signaling time increases with its mean. To validate these signaling time measurements, we constructed a two-step genetic cascade, and showed that the signaling time of the full cascade can be predicted from those of its constituent steps. These results provide concrete estimates for the time scales of transcriptional regulation in living cells, which are important for understanding the dynamics of synthetic transcriptional gene circuits.

Engineered Control of Genetic Variability Reveals Interplay among Quorum Sensing, Feedback Regulation, and Biochemical Noise

Stochastic fluctuations in gene expression trigger both beneficial and harmful consequences for cell behavior. Therefore, achieving a desired mean protein expression level while minimizing noise is of interest in many applications, including robust protein production systems in industrial biotechnology. Here, we consider a synthetic gene circuit combining intracellular negative feedback and cell-to-cell communication based on quorum sensing. Accounting for both intrinsic and extrinsic noise, stochastic simulations allow us to analyze the capability of the circuit to reduce noise strength as a function of its parameters. We obtain mean expression levels and noise strengths for all species under different scenarios, showing good agreement with system-wide available experimental data of protein abundance and noise in Escherichia coli. Our in silico experiments, validated by preliminary in vivo results, reveal significant noise attenuation in gene expression through the interplay between quorum sensing and negative feedback and highlight the differential role that they play in regard to intrinsic and extrinsic noise.

A real-time control system of gene expression using ligand-bound nucleic acid aptamer for metabolic engineering

Artificial control of bio-functions through regulating gene expression is one of the most important and attractive technologies to build novel living systems that are useful in the areas of chemical synthesis, nanotechnology, pharmacology, cell biology. Here, we present a novel real-time control system of gene regulation that includes an enhancement element by introducing duplex DNA aptamers upstream promoter and a repression element by introducing a RNA aptamer upstream ribosome binding site. With the presence of ligands corresponding to the DNA aptamers, the expression of the target gene can be potentially enhanced at the transcriptional level by strengthening the recognition capability of RNAP to the recognition region and speeding up the separation efficiency of the unwinding region due to the induced DNA bubble around the thrombin-bound aptamers; while with the presence of RNA aptamer ligand, the gene expression can be repressed at the translational level by weakening the recognition capability of ribosome to RBS due to the shielding of RBS by the formed aptamer-ligand complex upstream RBS. The effectiveness and potential utility of the developed gene regulation system were demonstrated by regulating the expression of ecaA gene in the cell-free systems. The realistic metabolic engineering application of the system has also tested by regulating the expression of mgtC gene and thrombin cDNA in Escherichia coli JD1021 for controlling metabolic flux and improving thrombin production, verifying that the real-time control system of gene regulation is able to realize the dynamic regulation of gene expression with potential applications in bacterial physiology studies and metabolic engineering.

Taking control over microbial populations: Current approaches for exploiting biological noise in bioprocesses

Phenotypic plasticity of microbial cells has attracted much attention and several research efforts have been dedicated to the description of methods aiming at characterizing phenotypic heterogeneity and its impact on microbial populations. However, different approaches have also been suggested in order to take benefit from noise in a bioprocess perspective, e.g. by increasing the robustness or productivity of a microbial population. This review is dedicated to outline these controlling methods. A common issue, that has still to be addressed, is the experimental identification and the mathematical expression of noise. Indeed, the effective interfacing of microbial physiology with external parameters that can be used for controlling physiology depends on the acquisition of reliable signals. Latest technologies, like single cell microfluidics and advanced flow cytometric approaches, enable linking physiology, noise, heterogeneity in productive microbes with environmental cues and hence allow correctly mapping and predicting biological behavior via mathematical representations. However, like in the field of electronics, signals are perpetually subjected to noise. If appropriately interpreted, this noise can give an additional insight into the behavior of the individual cells within a microbial population of interest. This review focuses on recent progress made at describing, treating and exploiting biological noise in the context of microbial populations used in various bioprocess applications.

Synthetic metabolic bypass for a metabolic toggle switch enhances acetyl-CoA supply for isopropanol production by Escherichia coli

Almost all synthetic pathways for biofuel production are designed to require endogenous metabolites in glycolysis, such as phosphoenolpyruvate, pyruvate, and acetyl-CoA. However, such metabolites are also required for bacterial cell growth. To reduce the metabolic imbalance between cell growth and target chemical production, we previously constructed a metabolic toggle switch (MTS) as a conditional flux redirection tool controlling metabolic flux of TCA cycle toward isopropanol production. This approach succeeded to improve the isopropanol production titer and yield while ensuring sufficient cell growth. However, excess accumulation of pyruvate, the precursor for acetyl-CoA synthesis, was also observed. In this study, for efficient conversation of pyruvate to acetyl-CoA (pyruvate oxidation), we designed a synthetic metabolic bypass composed of poxB and acs with the MTS for acetyl-CoA supply from the excess pyruvate. When this designed bypass was expressed at the appropriate expression level associated with the conditional metabolic flux redirection, pyruvate accumulation was prevented, and the isopropanol production titer and yield were improved. Final isopropanol production titer of strain harboring MTS with the synthetic metabolic bypass improved 4.4-fold compared with strain without metabolic flux regulation, and it was 1.3-fold higher than that of strain harboring the conventional MTS alone. Additionally, glucose consumption was also improved 1.7-fold compared with strain without metabolic flux regulation. On the other hand, introduction of the synthetic metabolic bypass alone showed no improvement in isopropanol production and glucose consumption. These results showed that the improvement in bio-production process caused by synergy between the MTS and the synthetic metabolic bypass.

Rapid generation of hypomorphic mutations

Hypomorphic mutations are a valuable tool for both genetic analysis of gene function and for synthetic biology applications. However, current methods to generate hypomorphic mutations are limited to a specific organism, change gene expression unpredictably, or depend on changes in spatial-temporal expression of the targeted gene. Here we present a simple and predictable method to generate hypomorphic mutations in model organisms by targeting translation elongation. Adding consecutive adenosine nucleotides, so-called polyA tracks, to the gene coding sequence of interest will decrease translation elongation efficiency, and in all tested cell cultures and model organisms, this decreases mRNA stability and protein expression. We show that protein expression is adjustable independent of promoter strength and can be further modulated by changing sequence features of the polyA tracks. These characteristics make this method highly predictable and tractable for generation of programmable allelic series with a range of expression levels.

Model-guided combinatorial optimization of complex synthetic gene networks

Constructing gene circuits that satisfy quantitative performance criteria has been a long-standing challenge in synthetic biology. Here, we show a strategy for optimizing a complex three-gene circuit, a novel proportional miRNA biosensor, using predictive modeling to initiate a search in the phase space of sensor genetic composition. We generate a library of sensor circuits using diverse genetic building blocks in order to access favorable parameter combinations and uncover specific genetic compositions with greatly improved dynamic range. The combination of high-throughput screening data and the data obtained from detailed mechanistic interrogation of a small number of sensors was used to validate the model. The validated model facilitated further experimentation, including biosensor reprogramming and biosensor integration into larger networks, enabling in principle arbitrary logic with miRNA inputs using normal form circuits. The study reveals how model-guided generation of genetic diversity followed by screening and model validation can be successfully applied to optimize performance of complex gene networks without extensive prior knowledge.

Synthetic biology platform technologies for antimicrobial applications

The growing prevalence of antibiotic resistance calls for new approaches in the development of antimicrobial therapeutics. Likewise, improved diagnostic measures are essential in guiding the application of targeted therapies and preventing the evolution of therapeutic resistance. Discovery platforms are also needed to form new treatment strategies and identify novel antimicrobial agents. By applying engineering principles to molecular biology, synthetic biologists have developed platforms that improve upon, supplement, and will perhaps supplant traditional broad-spectrum antibiotics. Efforts in engineering bacteriophages and synthetic probiotics demonstrate targeted antimicrobial approaches that can be fine-tuned using synthetic biology-derived principles. Further, the development of paper-based, cell-free expression systems holds promise in promoting the clinical translation of molecular biology tools for diagnostic purposes. In this review, we highlight emerging synthetic biology platform technologies that are geared toward the generation of new antimicrobial therapies, diagnostics, and discovery channels.

Design of a bistable switch to control cellular uptake

Bistable switches are widely used in synthetic biology to trigger cellular functions in response to environmental signals. All bistable switches developed so far, however, control the expression of target genes without access to other layers of the cellular machinery. Here, we propose a bistable switch to control the rate at which cells take up a metabolite from the environment. An uptake switch provides a new interface to command metabolic activity from the extracellular space and has great potential as a building block in more complex circuits that coordinate pathway activity across cell cultures, allocate metabolic tasks among different strains or require cell-to-cell communication with metabolic signals. Inspired by uptake systems found in nature, we propose to couple metabolite import and utilization with a genetic circuit under feedback regulation. Using mathematical models and analysis, we determined the circuit architectures that produce bistability and obtained their design space for bistability in terms of experimentally tuneable parameters. We found an activation-repression architecture to be the most robust switch because it displays bistability for the largest range of design parameters and requires little fine-tuning of the promoters' response curves. Our analytic results are based on on-off approximations of promoter activity and are in excellent qualitative agreement with simulations of more realistic models. With further analysis and simulation, we established conditions to maximize the parameter design space and to produce bimodal phenotypes via hysteresis and cell-to-cell variability. Our results highlight how mathematical analysis can drive the discovery of new circuits for synthetic biology, as the proposed circuit has all the hallmarks of a toggle switch and stands as a promising design to control metabolic phenotypes across cell cultures.

Dual control system - A novel scaffolding architecture of an inducible regulatory device for the precise regulation of gene expression

Here, we present a novel scaffolding architecture of an inducible regulatory device. This dual control system is completely silent in the off stage and is coupled to the regulation of gene expression at both the transcriptional and translational levels. This system also functions as an AND gate. We demonstrated the effectiveness of the cumate-riboswitch dual control system for the control of pamamycin production in Streptomyces albus. Placing the cre recombinase gene under the control of this system permitted the construction of synthetic devices with non-volatile memory that sense the signal and respond by altering DNA at the chromosomal level, thereby producing changes that are heritable. In addition, we present a library of synthetic inducible promoters based on the previously described cumate switch. With only one inducer and different promoters, we demonstrate that simultaneous modulation of the expression of several genes to different levels in various operons is possible. Because all modules of the AND gates are functional in bacteria other than Streptomyces, we anticipate that these regulatory devices can be used to control gene expression in other Actinobacteria. The features described in this study make these systems promising tools for metabolic engineering and biotechnology in Actinobacteria.

Molecular Buffers Permit Sensitivity Tuning and Inversion of Riboswitch Signals

Predictable integration of foreign biological signals and parts remains a key challenge in the systematic engineering of synthetic cellular actuations, and general methods to improve signal transduction and sensitivity are needed. To address this problem we modeled and built a molecular signal buffer network in Saccharomyces cerevisiae inspired by chemical pH buffer systems. The molecular buffer system context-insulates a riboswitch enabling synthetic control of colony formation and modular signal manipulations. The riboswitch signal is relayed to a transcriptional activation domain of a split transcription factor, while interacting DNA-binding domains mediate the transduction of signal and form an interacting molecular buffer. The molecular buffer system enables modular signal inversion through integration with repressor modules. Further, tuning of input sensitivity was achieved through perturbation of the buffer pair ratio guided by a mathematical model. Such buffered signal tuning networks will be useful for domestication of RNA-based sensors enabling tunable outputs and library-wide selections for drug discovery and metabolic engineering.

Synthetic biology to access and expand nature's chemical diversity

Bacterial genomes encode the biosynthetic potential to produce hundreds of thousands of complex molecules with diverse applications, from medicine to agriculture and materials. Accessing these natural products promises to reinvigorate drug discovery pipelines and provide novel routes to synthesize complex chemicals. The pathways leading to the production of these molecules often comprise dozens of genes spanning large areas of the genome and are controlled by complex regulatory networks with some of the most interesting molecules being produced by non-model organisms. In this Review, we discuss how advances in synthetic biology--including novel DNA construction technologies, the use of genetic parts for the precise control of expression and for synthetic regulatory circuits--and multiplexed genome engineering can be used to optimize the design and synthesis of pathways that produce natural products.

Polymorphic tandem repeats within gene promoters act as modifiers of gene expression and DNA methylation in humans

Despite representing an important source of genetic variation, tandem repeats (TRs) remain poorly studied due to technical difficulties. We hypothesized that TRs can operate as expression (eQTLs) and methylation (mQTLs) quantitative trait loci. To test this we analyzed the effect of variation at 4849 promoter-associated TRs, genotyped in 120 individuals, on neighboring gene expression and DNA methylation. Polymorphic promoter TRs were associated with increased variance in local gene expression and DNA methylation, suggesting functional consequences related to TR variation. We identified >100 TRs associated with expression/methylation levels of adjacent genes. These potential eQTL/mQTL TRs were enriched for overlaps with transcription factor binding and DNaseI hypersensitivity sites, providing a rationale for their effects. Moreover, we showed that most TR variants are poorly tagged by nearby single nucleotide polymorphisms (SNPs) markers, indicating that many functional TR variants are not effectively assayed by SNP-based approaches. Our study assigns biological significance to TR variations in the human genome, and suggests that a significant fraction of TR variations exert functional effects via alterations of local gene expression or epigenetics. We conclude that targeted studies that focus on genotyping TR variants are required to fully ascertain functional variation in the genome.

Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case

Background

Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized.

Results

We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior.

Conclusion

The proposed multi-objective optimization design framework is able to provide effective guidelines to tune biological parameters so as to achieve a desired circuit behavior. Moreover, it is easy to analyze the impact of the context on the synthetic device to be designed. That is, one can analyze how the presence of a downstream load influences the performance of the designed circuit, and take it into account.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0269-0) contains supplementary material, which is available to authorized users.

Tandem repeat variation in human and great ape populations and its impact on gene expression divergence

Tandem repeats (TRs) are stretches of DNA that are highly variable in length and mutate rapidly. They are thus an important source of genetic variation. This variation is highly informative for population and conservation genetics. It has also been associated with several pathological conditions and with gene expression regulation. However, genome-wide surveys of TR variation in humans and closely related species have been scarce due to technical difficulties derived from short-read technology. Here we explored the genome-wide diversity of TRs in a panel of 83 human and nonhuman great ape genomes, in a total of six different species, and studied their impact on gene expression evolution. We found that population diversity patterns can be efficiently captured with short TRs (repeat unit length, 1-5 bp). We examined the potential evolutionary role of TRs in gene expression differences between humans and primates by using 30,275 larger TRs (repeat unit length, 2-50 bp). Genes that contained TRs in the promoters, in their 3' untranslated region, in introns, and in exons had higher expression divergence than genes without repeats in the regions. Polymorphic small repeats (1-5 bp) had also higher expression divergence compared with genes with fixed or no TRs in the gene promoters. Our findings highlight the potential contribution of TRs to human evolution through gene regulation.

Enhanced Protein Production in Escherichia coli by Optimization of Cloning Scars at the Vector-Coding Sequence Junction

Protein production in Escherichia coli is a fundamental activity for a large fraction of academic, pharmaceutical, and industrial research laboratories. Maximum production is usually sought, as this reduces costs and facilitates downstream purification steps. Frustratingly, many coding sequences are poorly expressed even when they are codon-optimized and expressed from vectors with powerful genetic elements. In this study, we show that poor expression can be caused by certain nucleotide sequences (e.g., cloning scars) at the junction between the vector and the coding sequence. Since these sequences lie between the Shine-Dalgarno sequence and the start codon, they are an integral part of the translation initiation region. To identify the most optimal sequences, we devised a simple and inexpensive PCR-based step that generates sequence variants at the vector-coding sequence junction. These sequence variants modulated expression by up to 1000-fold. FACS-seq analyses indicated that low GC content and relaxed mRNA stability (ΔG) in this region were important, but not the only, determinants for high expression.

Tandem repeat variation in human and great ape populations and its impact on gene expression divergence

Tandem repeats (TRs) are stretches of DNA that are highly variable in length and mutate rapidly. They are thus an important source of genetic variation. This variation is highly informative for population and conservation genetics. It has also been associated with several pathological conditions and with gene expression regulation. However, genome-wide surveys of TR variation in humans and closely related species have been scarce due to technical difficulties derived from short-read technology. Here we explored the genome-wide diversity of TRs in a panel of 83 human and nonhuman great ape genomes, in a total of six different species, and studied their impact on gene expression evolution. We found that population diversity patterns can be efficiently captured with short TRs (repeat unit length, 1–5 bp). We examined the potential evolutionary role of TRs in gene expression differences between humans and primates by using 30,275 larger TRs (repeat unit length, 2–50 bp). Genes that contained TRs in the promoters, in their 3′ untranslated region, in introns, and in exons had higher expression divergence than genes without repeats in the regions. Polymorphic small repeats (1–5 bp) had also higher expression divergence compared with genes with fixed or no TRs in the gene promoters. Our findings highlight the potential contribution of TRs to human evolution through gene regulation.

Engineered biosynthesis of natural products in heterologous hosts

Natural products produced by microorganisms and plants are a major resource of antibacterial and anticancer drugs as well as industrially useful compounds. However, the native producers often suffer from low productivity and titers. Here we summarize the recent applications of heterologous biosynthesis for the production of several important classes of natural products such as terpenoids, flavonoids, alkaloids, and polyketides. In addition, we will discuss the new tools and strategies at multi-scale levels including gene, pathway, genome and community levels for highly efficient heterologous biosynthesis of natural products.

Controlling E. coli Gene Expression Noise

Intracellular protein copy numbers show significant cell-to-cell variability within an isogenic population due to the random nature of biological reactions. Here we show how the variability in copy number can be controlled by perturbing gene expression. Depending on the genetic network and host, different perturbations can be applied to control variability. To understand more fully how noise propagates and behaves in biochemical networks we developed stochastic control analysis (SCA) which is a sensitivity-based analysis framework for the study of noise control. Here we apply SCA to synthetic gene expression systems encoded on plasmids that are transformed into Escherichia coli. We show that (1) dual control of transcription and translation efficiencies provides the most efficient way of noise-versus-mean control. (2) The expressed proteins follow the gamma distribution function as found in chromosomal proteins. (3) One of the major sources of noise, leading to the cell-to-cell variability in protein copy numbers, is related to bursty translation. (4) By taking into account stochastic fluctuations in autofluorescence, the correct scaling relationship between the noise and mean levels of the protein copy numbers was recovered for the case of weak fluorescence signals.

Designing Conservation Relations in Layered Synthetic Biomolecular Networks

In Synthetic Biology, biomolecular networks are designed and constructed to perform specified tasks. Design strategies for these networks tend to center on tuning the parameters of mathematical models to achieve a specified behavior, and implementing these parameters experimentally. This design strategy often assumes a fixed network structure that defines the possible behaviors, which may be too restrictive for our purposes. This paper investigates the extent to which the state space of a synthetic network can also be designed and shaped by parametric tuning. We exploit timescale separation to implement new, nonlinear, tunable conservation relations that hold for all times beyond a fast transient. We demonstrate an application of this design strategy by flexibly constraining the possible behaviors of a gene regulatory network through the design of fast protein interactions.