Tunable genetic devices through simultaneous control of transcription and translation

Tuning Ultrasensitivity in Genetic Logic Gates Using Antisense RNA Feedback

Inverting genetic logic gates fueled by transcriptional repression is an established building block in genetic circuit design. Often, the gates' dose-response curves require large changes in dose to transition between logic ON and OFF states, potentially leading to logically indeterminate intermediate states when gates are connected. Additionally, leakage in the OFF state is a general concern, especially at the output stages of a circuit. This study explores the potential to improve inverting logic gates through the introduction of an additional sequestration reaction between the input and output chemical species of the gate. As a mechanism of study, we employ antisense RNAs (asRNAs) expressed alongside the mRNA (mRNA) of the logic gate within single transcripts. These asRNAs target mRNAs of adjacent gates and create additional feedback that supports the protein-mediated repression of the gates. Numerical and symbolic analysis indicates that the sequestration steepens the gate's dose-response curve, reduces leakage, and can potentially be used to adjust the location of logic transition. To leverage these effects, we demonstrate how design parameters can be tuned to obtain desired dose-response curves and outline how arbitrary combinational circuits can be assembled using the improved gates. Finally, we also discuss an implementation using split transcripts.

Developing Quorum Sensing-Based Collaborative Dynamic Control System in Halomonas TD01

Dynamic control exhibits increasing significance in microbial cell factory engineering by precisely manipulating gene expression over time and levels. However, the practical uses of most dynamic control tools still remain challenging because of poor scale-up robustness, especially for non-model chassis. Herein, a quorum sensing (QS)-based collaborative dynamic control system is constructed in Halomonas TD by regrouping two orthogonal quorum-sensing modules into two cell types, namely cell-A harboring cinR-luxI and cell-B harboring luxR-cinI together with sfGFP driven by Pcin and Plux promoters, respectively. Effective gene expression control with over 15-time dynamic foldchange is achieved by mixing cells A and B at different ratios and time points in a lab-scale fed-batch study. Besides, dynamic inhibitory and amplified control is further developed by cascading CRISPRi/dCas9 system and MmP1 RNA polymerase, respectively, yielding up to 80% repression efficiency and 30-time amplification foldchange under high cell density fermentation. Moreover, 500 mg L-1 indigo and 4.7 g L-1 superoxide dismutase (SOD) are obtained by engineered Halomonas using QS-based control tools in the fed-batch study, showing 1.5- and 1.0-fold higher, respectively, than the yields by recombinants induced by IPTG. This study exemplifies a standardized and streamlined inducer-free dynamic control pattern for metabolic engineering with promising robustness in scale-up fermentation contexts.

Engineering probiotic Escherichia coli for inflammation-responsive indoleacetic acid production using RiboJ-enhanced genetic circuits

BACKGROUND

As our understanding of gut microbiota's metabolic impacts on health grows, the interest in engineered probiotics has intensified. This study aimed to engineer the probiotic Escherichia coli Nissle 1917 (EcN) to produce indoleacetic acid (IAA) in response to gut inflammatory biomarkers thiosulfate and nitrate.

RESULTS

Genetic circuits were developed to initiate IAA synthesis upon detecting inflammatory signals, optimizing a heterologous IAA biosynthetic pathway, and incorporating a RiboJ insulator to enhance IAA production. The engineered EcN strains demonstrated increased IAA production in the presence of thiosulfate and nitrate. An IAA-responsive genetic circuit using the IacR transcription factor from Pseudomonas putida 1290 was also developed for real-time IAA monitoring.

CONCLUSIONS

Given IAA's role in reducing gastrointestinal inflammation, further refinement of this strain could lead to effective, in situ IAA-based therapies. This proof-of-concept advances the field of live biotherapeutic products and offers a promising approach for targeted therapy in inflammatory bowel diseases.

Multicellular PID control for robust regulation of biological processes

This article presents the first implementation of a proportional-integral-derivative (PID) biomolecular controller within a consortium of different cell populations, aimed at robust regulation of biological processes. By leveraging the modularity and cooperative dynamics of multiple engineered cell populations, we develop a comprehensive in silico analysis of the performance and robustness of P, PD, PI and PID control architectures. Our theoretical findings, validated through in silico experiments using the BSim agent-based simulation platform for bacterial populations, demonstrate the robustness and effectiveness of our multicellular PID control strategy. This innovative approach addresses critical limitations in current control methods, offering significant potential for applications in metabolic engineering, therapeutic contexts and industrial biotechnology. Future work will focus on experimental validation in vivo and further refinement of the control models.

Tailoring microbial fitness through computational steering and CRISPRi-driven robustness regulation

The widespread application of genetically modified microorganisms (GMMs) across diverse sectors underscores the pressing need for robust strategies to mitigate the risks associated with their potential uncontrolled escape. This study merges computational modeling with CRISPR interference (CRISPRi) to refine GMM metabolic robustness. Utilizing ensemble modeling, we achieved high-throughput in silico screening for enzymatic targets susceptible to expression alterations. Translating these insights, we developed functional CRISPRi, boosting fitness control via multiplexed gene knockdown. Our method, enhanced by an insulator-improved gRNA structure and an off-switch circuit controlling a compact Cas12m, resulted in rationally engineered strains with escape frequencies below National Institutes of Health standards. The effectiveness of this approach was confirmed under various conditions, showcasing its ability for secure GMM management. This research underscores the resilience of microbial metabolism, strategically modifying key nodes to halt growth without provoking significant resistance, thereby enabling more reliable and precise GMM control. A record of this paper's transparent peer review process is included in the supplemental information.

Cells transit through a quiescent-like state to convert to neurons at high rates

While transcription factors (TFs) provide essential cues for directing and redirecting cell fate, TFs alone are insufficient to drive cells to adopt alternative fates. Rather, transcription factors rely on receptive cell states to induce novel identities. Cell state emerges from and is shaped by cellular history and the activity of diverse processes. Here, we define the cellular and molecular properties of a highly receptive state amenable to transcription factor-mediated direct conversion from fibroblasts to induced motor neurons. Using a well-defined model of direct conversion to a post-mitotic fate, we identify the highly proliferative, receptive state that transiently emerges during conversion. Through examining chromatin accessibility, histone marks, and nuclear features, we find that cells reprogram from a state characterized by global reductions in nuclear size and transcriptional activity. Supported by globally increased levels of H3K27me3, cells enter a quiescent-like state of reduced RNA metabolism and elevated expression of REST and p27, markers of quiescent neural stem cells. From this transient state, cells convert to neurons at high rates. Inhibition of Ezh2, the catalytic subunit of PRC2 that deposits H3K27me3, abolishes conversion. Our work offers a roadmap to identify global changes in cellular processes that define cells with different conversion potentials that may generalize to other cell-fate transitions.

Highlights

Proliferation drives cells to a compact nuclear state that is receptive to TF-mediated conversion.Increased receptivity to TFs corresponds to reduced nuclear volumes.Reprogrammable cells display global, genome-wide increases in H3K27me3.High levels of H3K27me3 support cells' transits through a state of altered RNA metabolism.Inhibition of Ezh2 increases nuclear size, reduces the expression of the quiescence marker p27.Acute inhibition of Ezh2 abolishes motor neuron conversion.

One Sentence Summary

Cells transit through a quiescent-like state characterized by global reductions in nuclear size and transcriptional activity to convert to neurons at high rates.

Directed evolution of highly sensitive and stringent choline-induced gene expression controllers

Gene expression controllers are useful tools for microbial production of recombinant proteins and valued bio-based chemicals. Despite its usefulness, they have rarely been applied to the practical industrial bioprocess, due to the lack of systems that meets the three requirements: low cost, safety, and tight control, to the inducer molecules. Previously, we have developed the high-spec gene induction system controlled by safe and cheap inducer choline. However, the system requires relatively high concentration (~100 mM) of choline to fully induce the gene under control. In this work, we attempted to drastically improve the sensitivity of this induction system to further reduce the induction costs. To this end, we devised a simple circuit which couples gene induction system with positive-feedback loop (P-loop) of choline importer protein BetT. After the tuning of translation level of BetT (strength of the P-loop) and deletion of endogenous betI (noise sources), highly active yet stringent control of gene expression was achieved using about 100 times less amount of inducer molecules. The choline induction system developed in this study has the lowest basal expression, the lowest choline needed to be activated, and the highest amplitude of induction as the highest available promoter such as those known as PT5 system. With this system, one can tightly control the expression level of genes of interest with negligible cost for inducer molecule, which has been the bottleneck for the application to the large-scale industrial processes.

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.

Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology

Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.

Open-endedness in synthetic biology: A route to continual innovation for biological design

Design in synthetic biology is typically goal oriented, aiming to repurpose or optimize existing biological functions, augmenting biology with new-to-nature capabilities, or creating life-like systems from scratch. While the field has seen many advances, bottlenecks in the complexity of the systems built are emerging and designs that function in the lab often fail when used in real-world contexts. Here, we propose an open-ended approach to biological design, with the novelty of designed biology being at least as important as how well it fulfils its goal. Rather than solely focusing on optimization toward a single best design, designing with novelty in mind may allow us to move beyond the diminishing returns we see in performance for most engineered biology. Research from the artificial life community has demonstrated that embracing novelty can automatically generate innovative and unexpected solutions to challenging problems beyond local optima. Synthetic biology offers the ideal playground to explore more creative approaches to biological design.

Customizing cellular signal processing by synthetic multi-level regulatory circuits

As synthetic biology permeates society, the signal processing circuits in engineered living systems must be customized to meet practical demands. Towards this mission, novel regulatory mechanisms and genetic circuits with unprecedented complexity have been implemented over the past decade. These regulatory mechanisms, such as transcription and translation control, could be integrated into hybrid circuits termed "multi-level circuits". The multi-level circuit design will tremendously benefit the current genetic circuit design paradigm, from modifying basic circuit dynamics to facilitating real-world applications, unleashing our capabilities to customize cellular signal processing and address global challenges through synthetic biology.

Design of synthetic bacterial biosensors

Novel whole-cell bacterial biosensor designs require an emphasis on moving toward field deployment. Many current sensors are characterized under specified laboratory conditions, which frequently do not represent actual deployment conditions. To this end, recent developments such as toolkits for probing new host chassis that are more robust to environments of interest, have paved the way for improved designs. Strategies for rational tuning of genetic components or tools such as genetic amplifiers or designs that allow post hoc tuning are essential in optimizing existing biosensors for practical application. Furthermore, recent work has seen a rise in directed evolution techniques, which can be immensely valuable in both tuning existing sensors and developing sensors for new analytes that lack characterized sensors. Combined with advancements in bioinformatics and capabilities in rewiring two-component systems, many new sensors can be established, broadening biosensor use cases. Last, recent work in CRISPR-based dynamic regulation and memory mechanisms, as well as kill-switches for biosafety and innovative output integration concepts, represents promising steps toward designing bacterial biosensors for deployment in dynamic and heterogeneous conditions.

Tailored Synthetic sRNAs Dynamically Tune Multilayer Genetic Circuits

Predictable and controllable tuning of genetic circuits to regulate gene expression, including modulation of existing circuits or constructs without the need for redesign or rebuilding, is a persistent challenge in synthetic biology. Here, we propose rationally designed new small RNAs (sRNAs) that dynamically modulate gene expression of genetic circuits with a broad range (high, medium, and low) of repression. We designed multiple multilayer genetic circuits in which the variable effector element is a transcription factor (TF) controlling downstream the production of a reporter protein. The sRNAs target TFs instead of a reporter gene, and harnessing the intrinsic RNA-interference pathway in E. coli allowed for a wide range of expression modulation of the reporter protein, including the most difficult to achieve dynamic switch to an OFF state. The synthetic sRNAs are expressed independently of the circuit(s), thus allowing for repression without modifying the circuit itself. Our work provides a frame for achieving independent modulation of gene expression and dynamic and modular control of the multilayer genetic circuits by only including an independent control circuit expressing synthetic sRNAs, without altering the structure of existing genetic circuits.

Trumpet is an operating system for simple and robust cell-free biocomputing

Biological computation is becoming a viable and fast-growing alternative to traditional electronic computing. Here we present a biocomputing technology called Trumpet: Transcriptional RNA Universal Multi-Purpose GatE PlaTform. Trumpet combines the simplicity and robustness of the simplest in vitro biocomputing methods, adding signal amplification and programmability, while avoiding common shortcomings of live cell-based biocomputing solutions. We have demonstrated the use of Trumpet to build all universal Boolean logic gates. We have also built a web-based platform for designing Trumpet gates and created a primitive processor by networking several gates as a proof-of-principle for future development. The Trumpet offers a change of paradigm in biocomputing, providing an efficient and easily programmable biological logic gate operating system.

A microfluidic optimal experimental design platform for forward design of cell-free genetic networks

Cell-free protein synthesis has been widely used as a “breadboard” for design of synthetic genetic networks. However, due to a severe lack of modularity, forward engineering of genetic networks remains challenging. Here, we demonstrate how a combination of optimal experimental design and microfluidics allows us to devise dynamic cell-free gene expression experiments providing maximum information content for subsequent non-linear model identification. Importantly, we reveal that applying this methodology to a library of genetic circuits, that share common elements, further increases the information content of the data resulting in higher accuracy of model parameters. To show modularity of model parameters, we design a pulse decoder and bistable switch, and predict their behaviour both qualitatively and quantitatively. Finally, we update the parameter database and indicate that network topology affects parameter estimation accuracy. Utilizing our methodology provides us with more accurate model parameters, a necessity for forward engineering of complex genetic networks.

Characterization of cell-free genetic networks is inherently difficult. Here the authors use optimal experimental design and microfluidics to improve characterization, demonstrating modularity and predictability of parts in applied test cases.

A synthetic synthesis to explore animal evolution and development

Identifying the general principles by which genotypes are converted into phenotypes remains a challenge in the post-genomic era. We still lack a predictive understanding of how genes shape interactions among cells and tissues in response to signalling and environmental cues, and hence how regulatory networks generate the phenotypic variation required for adaptive evolution. Here, we discuss how techniques borrowed from synthetic biology may facilitate a systematic exploration of evolvability across biological scales. Synthetic approaches permit controlled manipulation of both endogenous and fully engineered systems, providing a flexible platform for investigating causal mechanisms in vivo. Combining synthetic approaches with multi-level phenotyping (phenomics) will supply a detailed, quantitative characterization of how internal and external stimuli shape the morphology and behaviour of living organisms. We advocate integrating high-throughput experimental data with mathematical and computational techniques from a variety of disciplines in order to pursue a comprehensive theory of evolution.

This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’.

Designing Biological Circuits: From Principles to Applications

Genetic circuit design is a well-studied problem in synthetic biology. Ever since the first genetic circuits─the repressilator and the toggle switch─were designed and implemented, many advances have been made in this area of research. The current review systematically organizes a number of key works in this domain by employing the versatile framework of generalized morphological analysis. Literature in the area has been mapped on the basis of (a) the design methodologies used, ranging from brute-force searches to control-theoretic approaches, (b) the modeling techniques employed, (c) various circuit functionalities implemented, (d) key design characteristics, and (e) the strategies used for the robust design of genetic circuits. We conclude our review with an outlook on multiple exciting areas for future research, based on the systematic assessment of key research gaps that have been readily unravelled by our analysis framework.

CRISPR Interference Modules as Low-Burden Logic Inverters in Synthetic Circuits

CRISPR and CRISPRi systems have revolutionized our biological engineering capabilities by enabling the editing and regulation of virtually any gene, via customization of single guide RNA (sgRNA) sequences. CRISPRi modules can work as programmable logic inverters, in which the dCas9-sgRNA complex represses a target transcriptional unit. They have been successfully used in bacterial synthetic biology to engineer information processing tasks, as an alternative to the traditionally adopted transcriptional regulators. In this work, we investigated and modulated the transfer function of several model systems with specific focus on the cell load caused by the CRISPRi logic inverters. First, an optimal expression cassette for dCas9 was rationally designed to meet the low-burden high-repression trade-off. Then, a circuit collection was studied at varying levels of dCas9 and sgRNAs targeting three different promoters from the popular tet, lac and lux systems, placed at different DNA copy numbers. The CRISPRi NOT gates showed low-burden properties that were exploited to fix a high resource-consuming circuit previously exhibiting a non-functional input-output characteristic, and were also adopted to upgrade a transcriptional regulator-based NOT gate into a 2-input NOR gate. The obtained data demonstrate that CRISPRi-based modules can effectively act as low-burden components in different synthetic circuits for information processing.

Design and Assembly of Multilevel Transcriptional and Translational Regulators for Stringent Control of Gene Expression

Precise control of gene expression is crucial when reprogramming the behavior of living cells. However, common inducible systems often lack the ability to stringently control gene expression due to the use of a single type of regulator that can be susceptible to unavoidable biomolecular fluctuations. In contrast, multilevel controllers (MLCs) employ several forms of regulation simultaneously to overcome this issue, ensuring a reduced basal expression while minimally affecting the maximum induced expression level that can be achieved. Here, we show how our publicly available genetic toolkit can be used to simplify the assembly of MLCs for the stringent control of gene expression. We demonstrate how new compatible parts can be designed and explain the rapid end-to-end assembly procedure.

Kinetically guided, ratiometric tuning of fatty acid biosynthesis

Cellular metabolism is a nonlinear reaction network in which dynamic shifts in enzyme concentration help regulate the flux of carbon to different products. Despite the apparent simplicity of these biochemical adjustments, their influence on metabolite biosynthesis tends to be context-dependent, difficult to predict, and challenging to exploit in metabolic engineering. This study combines a detailed kinetic model with a systematic set of in vitro and in vivo analyses to explore the use of enzyme concentration as a control parameter in fatty acid synthesis, an essential metabolic process with important applications in oleochemical production. Compositional analyses of a modeled and experimentally reconstituted fatty acid synthase (FAS) from Escherichia coli indicate that the concentration ratio of two native enzymes-a promiscuous thioesterase and a ketoacyl synthase-can tune the average length of fatty acids, an important design objective of engineered pathways. The influence of this ratio is sensitive to the concentrations of other FAS components, which can narrow or expand the range of accessible chain lengths. Inside the cell, simple changes in enzyme concentration can enhance product-specific titers by as much as 125-fold and elicit shifts in overall product profiles that rival those of thioesterase mutants. This work develops a kinetically guided approach for using ratiometric adjustments in enzyme concentration to control the product profiles of FAS systems and, broadly, provides a detailed framework for understanding how coordinated shifts in enzyme concentration can afford tight control over the outputs of nonlinear metabolic pathways.

Attenuator LRR - a regulatory tool for modulating gene expression in Gram-positive bacteria

With the rapid development of synthetic biology in recent years, particular attention has been paid to RNA devices, especially riboswitches, because of their significant and diverse regulatory roles in prokaryotic and eukaryotic cells. Due to the limited performance and context-dependence of riboswitches, only a few of them (such as theophylline, tetracycline and ciprofloxacin riboswitches) have been utilized as regulatory tools in biotechnology. In the present study, we demonstrated that a ribosome-dependent ribo-regulator, LRR, discovered in our previous work, exhibits an attractive regulatory performance. Specifically, it offers a 60-fold change in expression in the presence of retapamulin and a low level of leaky expression of about 1-2% without antibiotics. Moreover, LRR can be combined with different promoters and performs well in Bacillus thuringiensis, B. cereus, B. amyloliquefaciens, and B. subtilis. Additionally, LRR also functions in the Gram-negative bacterium Escherichia coli. Furthermore, we demonstrate its ability to control melanin metabolism in B. thuringiensis BMB171. Our results show that LRR can be applied to regulate gene expression, construct genetic circuits and tune metabolic pathways, and has great potential for many applications in synthetic biology.

Automated design and implementation of a NOR gate in Pseudomonas putida

Boolean NOR gates have been widely implemented in Escherichia coli as transcriptional regulatory devices for building complex genetic circuits. Yet, their portability to other bacterial hosts/chassis is generally hampered by frequent changes in the parameters of the INPUT/OUTPUT response functions brought about by new genetic and biochemical contexts. Here, we have used the circuit design tool CELLO for assembling a NOR gate in the soil bacterium and the metabolic engineering platform Pseudomonas putida with components tailored for E. coli. To this end, we capitalized on the functional parameters of 20 genetic inverters for each host and the resulting compatibility between NOT pairs. Moreover, we added to the gate library three inducible promoters that are specific to P. putida, thus expanding cross-platform assembly options. While the number of potential connectable inverters decreased drastically when moving the library from E. coli to P. putida, the CELLO software was still able to find an effective NOR gate in the new chassis. The automated generation of the corresponding DNA sequence and in vivo experimental verification accredited that some genetic modules initially optimized for E. coli can indeed be reused to deliver NOR logic in P. putida as well. Furthermore, the results highlight the value of creating host-specific collections of well-characterized regulatory inverters for the quick assembly of genetic circuits to meet complex specifications.

Efficiency of the synthetic self-splicing RiboJ ribozyme is robust to cis- and trans-changes in genetic background

The expanding knowledge of the variety of synthetic genetic elements has enabled the construction of new and more efficient genetic circuits and yielded novel insights into molecular mechanisms. However, context dependence, in which interactions between cis- or trans-genetic elements affect the behavior of these elements, can reduce their general applicability or predictability. Genetic insulators, which mitigate unintended context-dependent cis-interactions, have been used to address this issue. One of the most commonly used genetic insulators is a self-splicing ribozyme called RiboJ, which can be used to decouple upstream 5' UTR in mRNA from downstream sequences (e.g., open reading frames). Despite its general use as an insulator, there has been no systematic study quantifying the efficiency of RiboJ splicing or whether this autocatalytic activity is robust to trans- and cis-genetic context. Here, we determine the robustness of RiboJ splicing in the genetic context of six widely divergent E. coli strains. We also check for possible cis-effects by assessing two SNP versions close to the catalytic site of RiboJ. We show that mRNA molecules containing RiboJ are rapidly spliced even during rapid exponential growth and high levels of gene expression, with a mean efficiency of 98%. We also show that neither the cis- nor trans-genetic context has a significant impact on RiboJ activity, suggesting this element is robust to both cis- and trans-genetic changes.

Towards an engineering theory of evolution

Biological technologies are fundamentally unlike any other because biology evolves. Bioengineering therefore requires novel design methodologies with evolution at their core. Knowledge about evolution is currently applied to the design of biosystems ad hoc. Unless we have an engineering theory of evolution, we will neither be able to meet evolution's potential as an engineering tool, nor understand or limit its unintended consequences for our biological designs. Here, we propose the evotype as a helpful concept for engineering the evolutionary potential of biosystems, or other self-adaptive technologies, potentially beyond the realm of biology.

Cheetah: A Computational Toolkit for Cybergenetic Control

Advances in microscopy, microfluidics, and optogenetics enable single-cell monitoring and environmental regulation and offer the means to control cellular phenotypes. The development of such systems is challenging and often results in bespoke setups that hinder reproducibility. To address this, we introduce Cheetah, a flexible computational toolkit that simplifies the integration of real-time microscopy analysis with algorithms for cellular control. Central to the platform is an image segmentation system based on the versatile U-Net convolutional neural network. This is supplemented with functionality to robustly count, characterize, and control cells over time. We demonstrate Cheetah's core capabilities by analyzing long-term bacterial and mammalian cell growth and by dynamically controlling protein expression in mammalian cells. In all cases, Cheetah's segmentation accuracy exceeds that of a commonly used thresholding-based method, allowing for more accurate control signals to be generated. Availability of this easy-to-use platform will make control engineering techniques more accessible and offer new ways to probe and manipulate living cells.

Harnessing the central dogma for stringent multi-level control of gene expression

Strictly controlled inducible gene expression is crucial when engineering biological systems where even tiny amounts of a protein have a large impact on function or host cell viability. In these cases, leaky protein production must be avoided, but without affecting the achievable range of expression. Here, we demonstrate how the central dogma offers a simple solution to this challenge. By simultaneously regulating transcription and translation, we show how basal expression of an inducible system can be reduced, with little impact on the maximum expression rate. Using this approach, we create several stringent expression systems displaying >1000-fold change in their output after induction and show how multi-level regulation can suppress transcriptional noise and create digital-like switches between ‘on’ and ‘off’ states. These tools will aid those working with toxic genes or requiring precise regulation and propagation of cellular signals, plus illustrate the value of more diverse regulatory designs for synthetic biology.

Inducible gene expression systems should minimise leaky output and offer a large achievable range of expression. Here, the authors regulate transcription and translation together to suppress noise and create digital-like responses, while maintaining a large expression range in vivo and in vitro.

A Modelling Framework Linking Resource-Based Stochastic Translation to the Optimal Design of Synthetic Constructs

Simple Summary

In synthetic biology, it is commonplace to design and insert gene expression constructs into cells for the production of useful proteins. In order to maximise production yield, it is useful to predict the performance of these “engineered cells” in advance of conducting experiments. This is typically a complex task, which in recent years has motivated the use of “whole-cell models” (WCMs) that act as computational tools for predicting different aspects of cell growth. Many useful WCMs exist, however a common problem is their over-simplification of ribosome movement on mRNA transcripts during translation. WCMs typically don’t consider that, for constructs with inefficient (“slow”) codons, ribosomes can stall and form “traffic jams”, thereby becoming unavailable for translation of other proteins. To more accurately address these scenarios, we have built a computational framework that combines whole-cell modelling with a detailed account of ribosome movement on mRNA. We show how our framework can be used to link the modular design of a gene expression construct (via its promoter, ribosome binding site and codon composition) to protein yield during continuous cell culture, with a particular focus on how the optimal design can change over time in the presence or absence of “slow” codons.

Abstract

The effect of gene expression burden on engineered cells has motivated the use of “whole-cell models” (WCMs) that use shared cellular resources to predict how unnatural gene expression affects cell growth. A common problem with many WCMs is their inability to capture translation in sufficient detail to consider the impact of ribosomal queue formation on mRNA transcripts. To address this, we have built a “stochastic cell calculator” (StoCellAtor) that combines a modified TASEP with a stochastic implementation of an existing WCM. We show how our framework can be used to link a synthetic construct’s modular design (promoter, ribosome binding site (RBS) and codon composition) to protein yield during continuous culture, with a particular focus on the effects of low-efficiency codons and their impact on ribosomal queues. Through our analysis, we recover design principles previously established in our work on burden-sensing strategies, namely that changing promoter strength is often a more efficient way to increase protein yield than RBS strength. Importantly, however, we show how these design implications can change depending on both the duration of protein expression, and on the presence of ribosomal queues.

Advances on systems metabolic engineering of Bacillus subtilis as a chassis cell

The Gram-positive model bacterium Bacillus subtilis, has been broadly applied in various fields because of its low pathogenicity and strong protein secretion ability, as well as its well-developed fermentation technology. B. subtilis is considered as an attractive host in the field of metabolic engineering, in particular for protein expression and secretion, so it has been well studied and applied in genetic engineering. In this review, we discussed why B. subtilis is a good chassis cell for metabolic engineering. We also summarized the latest research progress in systematic biology, synthetic biology and evolution-based engineering of B. subtilis, and showed systemic metabolic engineering expedite the harnessing B. subtilis for bioproduction.

Toward Engineering Biosystems With Emergent Collective Functions

Many complex behaviors in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans many length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modeling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modeling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviors offers a means to take synthetic biology beyond single molecules or cells and toward the creation of systems with functions that can only emerge from collectives at multiple scales.