Characterizing bacterial gene circuit dynamics with optically programmed gene expression signals

Light-driven synchronization of optogenetic clocks

Synthetic genetic oscillators can serve as internal clocks within engineered cells to program periodic expression. However, cell-to-cell variability introduces a dispersion in the characteristics of these clocks that drives the population to complete desynchronization. Here, we introduce the optorepressilator, an optically controllable genetic clock that combines the repressilator, a three-node synthetic network in E. coli, with an optogenetic module enabling to reset, delay, or advance its phase using optical inputs. We demonstrate that a population of optorepressilators can be synchronized by transient green light exposure or entrained to oscillate indefinitely by a train of short pulses, through a mechanism reminiscent of natural circadian clocks. Furthermore, we investigate the system's response to detuned external stimuli observing multiple regimes of global synchronization. Integrating experiments and mathematical modeling, we show that the entrainment mechanism is robust and can be understood quantitatively from single cell to population level.

Dynamic Gene Expression Mitigates Mutational Escape in Lysis-Driven Bacteria Cancer Therapy

Engineered bacteria have the potential to deliver therapeutic payloads directly to tumors, with synthetic biology enabling precise control over therapeutic release in space and time. However, it remains unclear how to optimize therapeutic bacteria for durable colonization and sustained payload release. Here, we characterize nonpathogenic Escherichia coli expressing the bacterial toxin Perfringolysin O (PFO) and dynamic strategies that optimize therapeutic efficacy. While PFO is known for its potent cancer cell cytotoxicity, we present experimental evidence that expression of PFO causes lysis of bacteria in both batch culture and microfluidic systems, facilitating its efficient release. However, prolonged expression of PFO leads to the emergence of a mutant population that limits therapeutic-releasing bacteria in a PFO expression level-dependent manner. We present sequencing data revealing the mutant takeover and employ molecular dynamics to confirm that the observed mutations inhibit the lysis efficiency of PFO. To analyze this further, we developed a mathematical model describing the evolution of therapeutic-releasing and mutant bacteria populations revealing trade-offs between therapeutic load delivered and fraction of mutants that arise. We demonstrate that a dynamic strategy employing short and repeated inductions of the pfo gene better preserves the original population of therapeutic bacteria by mitigating the effects of mutational escape. Altogether, we demonstrate how dynamic modulation of gene expression can address mutant takeovers giving rise to limitations in engineered bacteria for therapeutic applications.

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.

Light Control in Microbial Systems

Light is a key environmental component influencing many biological processes, particularly in prokaryotes such as archaea and bacteria. Light control techniques have revolutionized precise manipulation at molecular and cellular levels in recent years. Bacteria, with adaptability and genetic tractability, are promising candidates for light control studies. This review investigates the mechanisms underlying light activation in bacteria and discusses recent advancements focusing on light control methods and techniques for controlling bacteria. We delve into the mechanisms by which bacteria sense and transduce light signals, including engineered photoreceptors and light-sensitive actuators, and various strategies employed to modulate gene expression, protein function, and bacterial motility. Furthermore, we highlight recent developments in light-integrated methods of controlling microbial responses, such as upconversion nanoparticles and optical tweezers, which can enhance the spatial and temporal control of bacteria and open new horizons for biomedical applications.

Deep model predictive control of gene expression in thousands of single cells

Gene expression is inherently dynamic, due to complex regulation and stochastic biochemical events. However, the effects of these dynamics on cell phenotypes can be difficult to determine. Researchers have historically been limited to passive observations of natural dynamics, which can preclude studies of elusive and noisy cellular events where large amounts of data are required to reveal statistically significant effects. Here, using recent advances in the fields of machine learning and control theory, we train a deep neural network to accurately predict the response of an optogenetic system in Escherichia coli cells. We then use the network in a deep model predictive control framework to impose arbitrary and cell-specific gene expression dynamics on thousands of single cells in real time, applying the framework to generate complex time-varying patterns. We also showcase the framework's ability to link expression patterns to dynamic functional outcomes by controlling expression of the tetA antibiotic resistance gene. This study highlights how deep learning-enabled feedback control can be used to tailor distributions of gene expression dynamics with high accuracy and throughput without expert knowledge of the biological system.

Light-inducible protein degradation in E. coli with the LOVdeg tag

Molecular tools for optogenetic control allow for spatial and temporal regulation of cell behavior. In particular, light-controlled protein degradation is a valuable mechanism of regulation because it can be highly modular, used in tandem with other control mechanisms, and maintain functionality throughout growth phases. Here, we engineered LOVdeg, a tag that can be appended to a protein of interest for inducible degradation in Escherichia coli using blue light. We demonstrate the modularity of LOVdeg by using it to tag a range of proteins, including the LacI repressor, CRISPRa activator, and the AcrB efflux pump. Additionally, we demonstrate the utility of pairing the LOVdeg tag with existing optogenetic tools to enhance performance by developing a combined EL222 and LOVdeg system. Finally, we use the LOVdeg tag in a metabolic engineering application to demonstrate post-translational control of metabolism. Together, our results highlight the modularity and functionality of the LOVdeg tag system and introduce a powerful new tool for bacterial optogenetics.

Toward a modeling, optimization, and predictive control framework for fed-batch metabolic cybergenetics

Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes extracellular process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, and so on. However, these elements are frequently insufficient for achieving optimal process performance or precise product composition. One can use metabolic and genetic engineering methods for optimization at the intracellular level. Nevertheless, those are often of static nature, failing when applied to dynamic processes or if disturbances occur. Furthermore, many bioprocesses are optimized empirically and implemented with little-to-no feedback control to counteract disturbances. The concept of cybergenetics has opened new possibilities to optimize bioprocesses by enabling online modulation of the gene expression of metabolism-relevant proteins via external inputs (e.g., light intensity in optogenetics). Here, we fuse cybergenetics with model-based optimization and predictive control for optimizing dynamic bioprocesses. To do so, we propose to use dynamic constraint-based models that integrate the dynamics of metabolic reactions, resource allocation, and inducible gene expression. We formulate a model-based optimal control problem to find the optimal process inputs. Furthermore, we propose using model predictive control to address uncertainties via online feedback. We focus on fed-batch processes, where the substrate feeding rate is an additional optimization variable. As a simulation example, we show the optogenetic control of the ATPase enzyme complex for dynamic modulation of enforced ATP wasting to adjust product yield and productivity.

Engineering Ca2+-Dependent DNA Polymerase Activity

Advancements in synthetic biology have provided new opportunities in biosensing, with applications ranging from genetic programming to diagnostics. Next generation biosensors aim to expand the number of accessible environments for measurements, increase the number of measurable phenomena, and improve the quality of the measurement. To this end, an emerging area in the field has been the integration of DNA as an information storage medium within biosensor outputs, leveraging nucleic acids to record the biosensor state over time. However, slow signal transduction steps, due to the time scales of transcription and translation, bottleneck many sensing-DNA recording approaches. DNA polymerases (DNAPs) have been proposed as a solution to the signal transduction problem by operating as both the sensor and responder, but there is presently a lack of DNAPs with functional sensitivity to many desirable target ligands. Here, we engineer components of the Pol δ replicative polymerase complex of Saccharomyces cerevisiae to sense and respond to Ca2+, a metal cofactor relevant to numerous biological phenomena. Through domain insertion and binding site grafting to Pol δ subunits, we demonstrate functional allosteric sensitivity to Ca2+. Together, this work provides an important foundation for future efforts in the development of DNAP-based biosensors.

Light inducible protein degradation in E. coli with the LOVdeg tag

Molecular tools for optogenetic control allow for spatial and temporal regulation of cell behavior. In particular, light controlled protein degradation is a valuable mechanism of regulation because it can be highly modular, used in tandem with other control mechanisms, and maintain functionality throughout growth phases. Here, we engineered LOVdeg, a tag that can be appended to a protein of interest for inducible degradation in Escherichia coli using blue light. We demonstrate the modularity of LOVdeg by using it to tag a range of proteins, including the LacI repressor, CRISPRa activator, and the AcrB efflux pump. Additionally, we demonstrate the utility of pairing the LOVdeg tag with existing optogenetic tools to enhance performance by developing a combined EL222 and LOVdeg system. Finally, we use the LOVdeg tag in a metabolic engineering application to demonstrate post-translational control of metabolism. Together, our results highlight the modularity and functionality of the LOVdeg tag system, and introduce a powerful new tool for bacterial optogenetics.

Algorithmic Programming of Sequential Logic and Genetic Circuits for Recording Biochemical Concentration in a Probiotic Bacterium

Through the implementation of designable genetic circuits, engineered probiotic microorganisms could be used as noninvasive diagnostic tools for the gastrointestinal tract. For these living cells to report detected biomarkers or signals after exiting the gut, the genetic circuits must be able to record these signals by using genetically encoded memory. Complex memory register circuits could enable multiplex interrogation of biomarkers and signals. A theory-based approach to create genetic circuits containing memory, known as sequential logic circuits, was previously established for a model laboratory strain of Escherichia coli, yet how circuit component performance varies for nonmodel and clinically relevant bacterial strains is poorly understood. Here, we develop a scalable computational approach to design robust sequential logic circuits in probiotic strain Escherichia coli Nissle 1917 (EcN). In this work, we used TetR-family transcriptional repressors to build genetic logic gates that can be composed into sequential logic circuits, along with a set of engineered sensors relevant for use in the gut environment. Using standard methods, 16 genetic NOT gates and nine sensors were experimentally characterized in EcN. These data were used to design and predict the performance of circuit designs. We present a set of genetic circuits encoding both combinational logic and sequential logic and show that the circuit outputs are in close agreement with our quantitative predictions from the design algorithm. Furthermore, we demonstrate an analog-like concentration recording circuit that detects and reports three input concentration ranges of a biochemical signal using sequential logic.

Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes

Engineering biology relies on the accurate prediction of cell responses. However, making these predictions is challenging for a variety of reasons, including the stochasticity of biochemical reactions, variability between cells, and incomplete information about underlying biological processes. Machine learning methods, which can model diverse input-output relationships without requiring a priori mechanistic knowledge, are an ideal tool for this task. For example, such approaches can be used to predict gene expression dynamics given time-series data of past expression history. To explore this application, we computationally simulated single-cell responses, incorporating different sources of noise and alternative genetic circuit designs. We showed that deep neural networks trained on these simulated data were able to correctly infer the underlying dynamics of a cell response even in the presence of measurement noise and stochasticity in the biochemical reactions. The training set size and the amount of past data provided as inputs both affected prediction quality, with cascaded genetic circuits that introduce delays requiring more past data. We also tested prediction performance on a bistable auto-activation circuit, finding that our initial method for predicting a single trajectory was fundamentally ill-suited for multimodal dynamics. To address this, we updated the network architecture to predict the entire distribution of future states, showing it could accurately predict bimodal expression distributions. Overall, these methods can be readily applied to the diverse prediction tasks necessary to predict and control a variety of biological circuits, a key aspect of many synthetic biology applications.

Lustro: High-throughput optogenetic experiments enabled by automation and a yeast optogenetic toolkit

Optogenetic systems use genetically-encoded light-sensitive proteins to control cellular processes. This provides the potential to orthogonally control cells with light, however these systems require many design-build-test cycles to achieve a functional design and multiple illumination variables need to be laboriously tuned for optimal stimulation. We combine laboratory automation and a modular cloning scheme to enable high-throughput construction and characterization of optogenetic split transcription factors in Saccharomyces cerevisiae . We expand the yeast optogenetic toolkit to include variants of the cryptochromes and Enhanced Magnets, incorporate these light-sensitive dimerizers into split transcription factors, and automate illumination and measurement of cultures in a 96-well microplate format for high-throughput characterization. We use this approach to rationally design and test an optimized Enhanced Magnet transcription factor with improved light-sensitive gene expression. This approach is generalizable to high-throughput characterization of optogenetic systems across a range of biological systems and applications.

Conformational-switch biosensors as novel tools to support continuous, real-time molecular monitoring in lab-on-a-chip devices

Recent years have seen continued expansion of the functionality of lab on a chip (LOC) devices. Indeed LOCs now provide scientists and developers with useful and versatile platforms across a myriad of chemical and biological applications. The field still fails, however, to integrate an often important element of bench-top analytics: real-time molecular measurements that can be used to "guide" a chemical response. Here we describe the analytical techniques that could provide LOCs with such real-time molecular monitoring capabilities. It appears to us that, among the approaches that are general (i.e., that are independent of the reactive or optical properties of their targets), sensing strategies relying on binding-induced conformational change of bioreceptors are most likely to succeed in such applications.

Optogenetic control of beta-carotene bioproduction in yeast across multiple lab-scales

Optogenetics arises as a valuable tool to precisely control genetic circuits in microbial cell factories. Light control holds the promise of optimizing bioproduction methods and maximizing yields, but its implementation at different steps of the strain development process and at different culture scales remains challenging. In this study, we aim to control beta-carotene bioproduction using optogenetics in Saccharomyces cerevisiae and investigate how its performance translates across culture scales. We built four lab-scale illumination devices, each handling different culture volumes, and each having specific illumination characteristics and cultivating conditions. We evaluated optogenetic activation and beta-carotene production across devices and optimized them both independently. Then, we combined optogenetic induction and beta-carotene production to make a light-inducible beta-carotene producer strain. This was achieved by placing the transcription of the bifunctional lycopene cyclase/phytoene synthase CrtYB under the control of the pC120 optogenetic promoter regulated by the EL222-VP16 light-activated transcription factor, while other carotenogenic enzymes (CrtI, CrtE, tHMG) were expressed constitutively. We show that illumination, culture volume and shaking impact differently optogenetic activation and beta-carotene production across devices. This enabled us to determine the best culture conditions to maximize light-induced beta-carotene production in each of the devices. Our study exemplifies the stakes of scaling up optogenetics in devices of different lab scales and sheds light on the interplays and potential conflicts between optogenetic control and metabolic pathway efficiency. As a general principle, we propose that it is important to first optimize both components of the system independently, before combining them into optogenetic producing strains to avoid extensive troubleshooting. We anticipate that our results can help designing both strains and devices that could eventually lead to larger scale systems in an effort to bring optogenetics to the industrial scale.

NLoed: A Python Package for Nonlinear Optimal Experimental Design in Systems Biology

Modeling in systems and synthetic biology relies on accurate parameter estimates and predictions. Accurate model calibration relies, in turn, on data and on how well suited the available data are to a particular modeling task. Optimal experimental design (OED) techniques can be used to identify experiments and data collection procedures that will most efficiently contribute to a given modeling objective. However, implementation of OED is limited by currently available software tools that are not well suited for the diversity of nonlinear models and non-normal data commonly encountered in biological research. Moreover, existing OED tools do not make use of the state-of-the-art numerical tools, resulting in inefficient computation. Here, we present the NLoed software package and demonstrate its use with in vivo data from an optogenetic system in Escherichia coli. NLoed is an open-source Python library providing convenient access to OED methods, with particular emphasis on experimental design for systems biology research. NLoed supports a wide variety of nonlinear, multi-input/output, and dynamic models and facilitates modeling and design of experiments over a wide variety of data types. To support OED investigations, the NLoed package implements maximum likelihood fitting and diagnostic tools, providing a comprehensive modeling workflow. NLoed offers an accessible, modular, and flexible OED tool set suited to the wide variety of experimental scenarios encountered in systems biology research. We demonstrate NLoed's capabilities by applying it to experimental design for characterization of a bacterial optogenetic system.

Optogenetic Control of Bacterial Expression by Red Light

In optogenetics, as in nature, sensory photoreceptors serve to control cellular processes by light. Bacteriophytochrome (BphP) photoreceptors sense red and far-red light via a biliverdin chromophore and, in response, cycle between the spectroscopically, structurally, and functionally distinct Pr and Pfr states. BphPs commonly belong to two-component systems that control the phosphorylation of cognate response regulators and downstream gene expression through histidine kinase modules. We recently demonstrated that the paradigm BphP from Deinococcus radiodurans exclusively acts as a phosphatase but that its photosensory module can control the histidine kinase activity of homologous receptors. Here, we apply this insight to reprogram two widely used setups for bacterial gene expression from blue-light to red-light control. The resultant pREDusk and pREDawn systems allow gene expression to be regulated down and up, respectively, uniformly under red light by 100-fold or more. Both setups are realized as portable, single plasmids that encode all necessary components including the biliverdin-producing machinery. The triggering by red light affords high spatial resolution down to the single-cell level. As pREDusk and pREDawn respond sensitively to red light, they support multiplexing with optogenetic systems sensitive to other light colors. Owing to the superior tissue penetration of red light, the pREDawn system can be triggered at therapeutically safe light intensities through material layers, replicating the optical properties of the skin and skull. Given these advantages, pREDusk and pREDawn enable red-light-regulated expression for diverse use cases in bacteria.

Light-regulated gene expression in Bacteria: Fundamentals, advances, and perspectives

Numerous photoreceptors and genetic circuits emerged over the past two decades and now enable the light-dependent i.e., optogenetic, regulation of gene expression in bacteria. Prompted by light cues in the near-ultraviolet to near-infrared region of the electromagnetic spectrum, gene expression can be up- or downregulated stringently, reversibly, non-invasively, and with precision in space and time. Here, we survey the underlying principles, available options, and prominent examples of optogenetically regulated gene expression in bacteria. While transcription initiation and elongation remain most important for optogenetic intervention, other processes e.g., translation and downstream events, were also rendered light-dependent. The optogenetic control of bacterial expression predominantly employs but three fundamental strategies: light-sensitive two-component systems, oligomerization reactions, and second-messenger signaling. Certain optogenetic circuits moved beyond the proof-of-principle and stood the test of practice. They enable unprecedented applications in three major areas. First, light-dependent expression underpins novel concepts and strategies for enhanced yields in microbial production processes. Second, light-responsive bacteria can be optogenetically stimulated while residing within the bodies of animals, thus prompting the secretion of compounds that grant health benefits to the animal host. Third, optogenetics allows the generation of precisely structured, novel biomaterials. These applications jointly testify to the maturity of the optogenetic approach and serve as blueprints bound to inspire and template innovative use cases of light-regulated gene expression in bacteria. Researchers pursuing these lines can choose from an ever-growing, versatile, and efficient toolkit of optogenetic circuits.

An adaptive tracking illumination system for optogenetic control of single bacterial cells

Single-cell behaviors are essential during early-stage biofilm formation. In this study, we aimed to evaluate whether single-cell behaviors could be precisely and continuously manipulated by optogenetics. We thus established adaptive tracking illumination (ATI), a novel illumination method to precisely manipulate the gene expression and bacterial behavior of Pseudomonas aeruginosa on the surface at the single-cell level by using the combination of a high-throughput bacterial tracking algorithm, optogenetic manipulation, and adaptive microscopy. ATI enables precise gene expression control by manipulating the optogenetic module gene expression and type IV pili (TFP)-mediated motility and microcolony formation during biofilm formation through bis-(3'-5')-cyclic dimeric guanosine monophosphate (c-di-GMP) level modifications in single cells. Moreover, we showed that the spatial organization of single cells in mature biofilms could be controlled using ATI. Therefore, this novel method we established might markedly answer various questions or resolve problems in microbiology. KEY POINTS: • High-resolution spatial and continuous optogenetic control of individual bacteria. • Phenotype-specific optogenetic control of individual bacteria. • Capacity to control biologically relevant processes in engineered single cells.

Platforms for Optogenetic Stimulation and Feedback Control

Harnessing the potential of optogenetics in biology requires methodologies from different disciplines ranging from biology, to mechatronics engineering, to control engineering. Light stimulation of a synthetic optogenetic construct in a given biological species can only be achieved via a suitable light stimulation platform. Emerging optogenetic applications entail a consistent, reproducible, and regulated delivery of light adapted to the application requirement. In this review, we explore the evolution of light-induction hardware-software platforms from simple illumination set-ups to sophisticated microscopy, microtiter plate and bioreactor designs, and discuss their respective advantages and disadvantages. Here, we examine design approaches followed in performing optogenetic experiments spanning different cell types and culture volumes, with induction capabilities ranging from single cell stimulation to entire cell culture illumination. The development of automated measurement and stimulation schemes on these platforms has enabled researchers to implement various in silico feedback control strategies to achieve computer-controlled living systems—a theme we briefly discuss in the last part of this review.

Synthetic microbiology applications powered by light

Synthetic biology is a field of research in which molecular parts (mostly nucleic acids and proteins) are de novo created or modified and then used either alone or in combination to achieve new functions that can help solve the problems of our modern society. In synthetic microbiology, microbes are employed rather than other organisms or cell-free systems. Optogenetics, a relatively recently established technology that relies on the use of genetically encoded photosensitive proteins to control biological processes with high spatiotemporal precision, offers the possibility to empower synthetic (micro)biology applications due to the many positive features that light has as an external trigger. In this review, we describe recent synthetic microbiology applications that made use of optogenetics after briefly introducing the molecular mechanism behind some of the most employed optogenetic tools. We highlight the power and versatility of this technique, which opens up new horizons for both research and industry.

Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level

Understanding and characterising biochemical processes inside single cells requires experimental platforms that allow one to perturb and observe the dynamics of such processes as well as computational methods to build and parameterise models from the collected data. Recent progress with experimental platforms and optogenetics has made it possible to expose each cell in an experiment to an individualised input and automatically record cellular responses over days with fine time resolution. However, methods to infer parameters of stochastic kinetic models from single-cell longitudinal data have generally been developed under the assumption that experimental data is sparse and that responses of cells to at most a few different input perturbations can be observed. Here, we investigate and compare different approaches for calculating parameter likelihoods of single-cell longitudinal data based on approximations of the chemical master equation (CME) with a particular focus on coupling the linear noise approximation (LNA) or moment closure methods to a Kalman filter. We show that, as long as cells are measured sufficiently frequently, coupling the LNA to a Kalman filter allows one to accurately approximate likelihoods and to infer model parameters from data even in cases where the LNA provides poor approximations of the CME. Furthermore, the computational cost of filtering-based iterative likelihood evaluation scales advantageously in the number of measurement times and different input perturbations and is thus ideally suited for data obtained from modern experimental platforms. To demonstrate the practical usefulness of these results, we perform an experiment in which single cells, equipped with an optogenetic gene expression system, are exposed to various different light-input sequences and measured at several hundred time points and use parameter inference based on iterative likelihood evaluation to parameterise a stochastic model of the system.

Optogenetics Illuminates Applications in Microbial Engineering

Optogenetics has been used in a variety of microbial engineering applications, such as chemical and protein production, studies of cell physiology, and engineered microbe-host interactions. These diverse applications benefit from the precise spatiotemporal control that light affords, as well as its tunability, reversibility, and orthogonality. This combination of unique capabilities has enabled a surge of studies in recent years investigating complex biological systems with completely new approaches. We briefly describe the optogenetic tools that have been developed for microbial engineering, emphasizing the scientific advancements that they have enabled. In particular, we focus on the unique benefits and applications of implementing optogenetic control, from bacterial therapeutics to cybergenetics. Finally, we discuss future research directions, with special attention given to the development of orthogonal multichromatic controls. With an abundance of advantages offered by optogenetics, the future is bright in microbial engineering. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Bifunctional optogenetic switch for improving shikimic acid production in E. coli

BACKGROUND

Biomass formation and product synthesis decoupling have been proven to be promising to increase the titer of desired value add products. Optogenetics provides a potential strategy to develop light-induced circuits that conditionally control metabolic flux redistribution for enhanced microbial production. However, the limited number of light-sensitive proteins available to date hinders the progress of light-controlled tools.

RESULTS

To address these issues, two optogenetic systems (TPRS and TPAS) were constructed by reprogramming the widely used repressor TetR and protease TEVp to expand the current optogenetic toolkit. By merging the two systems, a bifunctional optogenetic switch was constructed to enable orthogonally regulated gene transcription and protein accumulation. Application of this bifunctional switch to decouple biomass formation and shikimic acid biosynthesis allowed 35 g/L of shikimic acid production in a minimal medium from glucose, representing the highest titer reported to date by E. coli without the addition of any chemical inducers and expensive aromatic amino acids. This titer was further boosted to 76 g/L when using rich medium fermentation.

CONCLUSION

The cost effective and light-controlled switch reported here provides important insights into environmentally friendly tools for metabolic pathway regulation and should be applicable to the production of other value-add chemicals.

Regulation of the activity of bacterial histidine kinase PleC by the scaffolding protein PodJ

Scaffolding proteins can customize the response of signaling networks to support cell development and behaviors. PleC is a bifunctional histidine kinase whose signaling activity coordinates asymmetric cell division to yield a motile swarmer cell and a stalked cell in the gram-negative bacterium Caulobacter crescentus. Past studies have shown that PleC’s switch in activity from kinase to phosphatase correlates with a change in its subcellular localization pattern from diffuse to localized at the new cell pole. Here we investigated how the bacterial scaffolding protein PodJ regulates the subcellular positioning and activity of PleC. We reconstituted the PleC-PodJ signaling complex through both heterologous expressions in Escherichia coli and in vitro studies. In vitro, PodJ phase separates as a biomolecular condensate that recruits PleC and inhibits its kinase activity. We also constructed an in vivo PleC-CcaS chimeric histidine kinase reporter assay and demonstrated using this method that PodJ leverages its intrinsically disordered region to bind to PleC’s PAS sensory domain and regulate PleC-CcaS signaling. Regulation of the PleC-CcaS was most robust when PodJ was concentrated at the cell poles and was dependent on the allosteric coupling between PleC-CcaS’s PAS sensory domain and its downstream histidine kinase domain. In conclusion, our in vitro biochemical studies suggest that PodJ phase separation may be coupled to changes in PleC enzymatic function. We propose that this coupling of phase separation and allosteric regulation may be a generalizable phenomenon among enzymes associated with biomolecular condensates.

The Red Edge: Bilin-Binding Photoreceptors as Optogenetic Tools and Fluorescence Reporters

This review adds the bilin-binding phytochromes to the Chemical Reviews thematic issue "Optogenetics and Photopharmacology". The work is structured into two parts. We first outline the photochemistry of the covalently bound tetrapyrrole chromophore and summarize relevant spectroscopic, kinetic, biochemical, and physiological properties of the different families of phytochromes. Based on this knowledge, we then describe the engineering of phytochromes to further improve these chromoproteins as photoswitches and review their employment in an ever-growing number of different optogenetic applications. Most applications rely on the light-controlled complex formation between the plant photoreceptor PhyB and phytochrome-interacting factors (PIFs) or C-terminal light-regulated domains with enzymatic functions present in many bacterial and algal phytochromes. Phytochrome-based optogenetic tools are currently implemented in bacteria, yeast, plants, and animals to achieve light control of a wide range of biological activities. These cover the regulation of gene expression, protein transport into cell organelles, and the recruitment of phytochrome- or PIF-tagged proteins to membranes and other cellular compartments. This compilation illustrates the intrinsic advantages of phytochromes compared to other photoreceptor classes, e.g., their bidirectional dual-wavelength control enabling instant ON and OFF regulation. In particular, the long wavelength range of absorption and fluorescence within the "transparent window" makes phytochromes attractive for complex applications requiring deep tissue penetration or dual-wavelength control in combination with blue and UV light-sensing photoreceptors. In addition to the wide variability of applications employing natural and engineered phytochromes, we also discuss recent progress in the development of bilin-based fluorescent proteins.

Functional Verification of Novel ELMO1 Variants by Live Imaging in Zebrafish

ELMO1 (Engulfment and Cell Motility1) is a gene involved in regulating cell motility through the ELMO1-DOCK2-RAC complex. Contrary to DOCK2 (Dedicator of Cytokinesis 2) deficiency, which has been reported to be associated with immunodeficiency diseases, variants of ELMO1 have been associated with autoimmune diseases, such as diabetes and rheumatoid arthritis (RA). To explore the function of ELMO1 in immune cells and to verify the functions of novel ELMO1 variants in vivo, we established a zebrafish elmo1 mutant model. Live imaging revealed that, similar to mammals, the motility of neutrophils and T-cells was largely attenuated in zebrafish mutants. Consequently, the response of neutrophils to injury or bacterial infection was significantly reduced in the mutants. Furthermore, the reduced mobility of neutrophils could be rescued by the expression of constitutively activated Rac proteins, suggesting that zebrafish elmo1 mutant functions via a conserved mechanism. With this mutant, three novel human ELMO1 variants were transiently and specifically expressed in zebrafish neutrophils. Two variants, p.E90K (c.268G>A) and p.D194G (c.581A>G), could efficiently recover the motility defect of neutrophils in the elmo1 mutant; however, the p.R354X (c.1060C>T) variant failed to rescue the mutant. Based on those results, we identified that zebrafish elmo1 plays conserved roles in cell motility, similar to higher vertebrates. Using the transient-expression assay, zebrafish elmo1 mutants could serve as an effective model for human variant verification in vivo.

Bacterial two-component systems as sensors for synthetic biology applications

Two-component systems (TCSs) are a ubiquitous family of signal transduction pathways that enable bacteria to sense and respond to diverse physical, chemical, and biological stimuli outside and inside the cell. Synthetic biologists have begun to repurpose TCSs for applications in optogenetics, materials science, gut microbiome engineering, and soil nutrient biosensing, among others. New engineering methods including genetic refactoring, DNA-binding domain swapping, detection threshold tuning, and phosphorylation cross-talk insulation are being used to increase the reliability of TCS sensor performance and tailor TCS signaling properties to the requirements of specific applications. There is now potential to combine these methods with large-scale gene synthesis and laboratory screening to discover the inputs sensed by many uncharacterized TCSs and develop a large new family of genetically-encoded sensors that respond to an unrivaled breadth of stimuli.

Optogenetics in bacteria - applications and opportunities

Optogenetics holds the promise of controlling biological processes with superb temporal and spatial resolution at minimal perturbation. Although many of the light-reactive proteins used in optogenetic systems are derived from prokaryotes, applications were largely limited to eukaryotes for a long time. In recent years, however, an increasing number of microbiologists use optogenetics as a powerful new tool to study and control key aspects of bacterial biology in a fast and often reversible manner. After a brief discussion of optogenetic principles, this review provides an overview of the rapidly growing number of optogenetic applications in bacteria, with a particular focus on studies venturing beyond transcriptional control. To guide future experiments, we highlight helpful tools, provide considerations for successful application of optogenetics in bacterial systems, and identify particular opportunities and challenges that arise when applying these approaches in bacteria.

Redesign of ultrasensitive and robust RecA gene circuit to sense DNA damage

SOS box of the recA promoter, PVRecA from Vibrio natriegens was characterized, cloned and expressed in a probiotic strain E. coli Nissle 1917. This promoter was then rationally engineered according to predicted interactions between LexA repressor and PVRecA . The redesigned PVRecA-AT promoter showed a sensitive and robust response to DNA damage induced by UV and genotoxic compounds. Rational design of PVRecA coupled to an amplification gene circuit increased circuit output amplitude 4.3-fold in response to a DNA damaging compound mitomycin C. A TetR-based negative feedback loop was added to the PVRecA-AT amplifier to achieve a robust SOS system, resistant to environmental fluctuations in parameters including pH, temperature, oxygen and nutrient conditions. We found that E. coli Nissle 1917 with optimized PVRecA-AT adapted to UV exposure and increased SOS response 128-fold over 40 h cultivation in turbidostat mini-reactor. We also showed the potential of this PVRecA-AT system as an optogenetic actuator, which can be controlled spatially through UV radiation. We demonstrated that the optimized SOS responding gene circuits were able to detect carcinogenic biomarker molecules with clinically relevant concentrations. The ultrasensitive SOS gene circuits in probiotic E. coli Nissle 1917 would be potentially useful for bacterial diagnosis.

A light tunable differentiation system for the creation and control of consortia in yeast

Artificial microbial consortia seek to leverage division-of-labour to optimize function and possess immense potential for bioproduction. Co-culturing approaches, the preferred mode of generating a consortium, remain limited in their ability to give rise to stable consortia having finely tuned compositions. Here, we present an artificial differentiation system in budding yeast capable of generating stable microbial consortia with custom functionalities from a single strain at user-defined composition in space and in time based on optogenetically-driven genetic rewiring. Owing to fast, reproducible, and light-tunable dynamics, our system enables dynamic control of consortia composition in continuous cultures for extended periods. We further demonstrate that our system can be extended in a straightforward manner to give rise to consortia with multiple subpopulations. Our artificial differentiation strategy establishes a novel paradigm for the creation of complex microbial consortia that are simple to implement, precisely controllable, and versatile to use.

Reactor control system in bacterial co-culture based on fluorescent proteins using an Arduino-based home-made device

BACKGROUND

Co-culture, fermentation with more than two microbial strains, is a potential flexible method for optimizing the metabolic conversion process in bio-production. However, maintaining an ideal population throughout the fermentation process remains a challenge.

METHODS AND RESULTS

In this study, we developed a proportional control system for controlling the population ratio of Escherichia coli strains to a set value during continuous co-culture. Two E. coli strains were distinguished by expressing different fluorescent proteins, and their population ratio was determined by culture fluorescence. Furthermore, different types of amino acid auxotrophs were provided to each strain, and among these, growth was controlled by the amino acid concentrations in the feed medium. An Arduino-based device was developed using light-emitting diodes and cadmium sulfide light sensors for the in-line monitoring of culture fluorescence. Two E. coli strains of methionine auxotroph green fluorescent protein (GFP) expressing (met-GFP) strain and arginine auxotroph red fluorescent protein (RFP) expressing (arg-RFP) strain were co-cultured using a jar-fermenter. The amounts of methionine and arginine in the feed medium were altered to guide the population ratio to a set value. During the continuous culture, the population ratio between the met-GFP and arg-RFP strains was successfully maintained at approximately the setpoint values.

CONCLUSION

This study demonstrated the development of a home-made device for controlling the reactor of E. coli based on fluorescent proteins using inexpensive parts.

Cell-Free Total Biosynthesis of Plant Terpene Natural Products using an Orthogonal Cofactor Regeneration System

Here we report the one-pot, cell-free enzymatic synthesis of the plant monoterpene nepetalactol starting from the readily available geraniol. A pair of orthogonal cofactor regeneration systems permitted NAD⁺-dependent geraniol oxidation followed by NADPH-dependent reductive cyclization without isolation of intermediates. The orthogonal cofactor regeneration system maintained a high ratio of NAD⁺ to NADH and a low ratio of NADP⁺ to NADPH. The overall reaction contains four biosynthetic enzymes, including a soluble P450; and five accessory and cofactor regeneration enzymes. Furthermore, addition of a NAD⁺-dependent dehydrogenase to the one-pot mixture led to ~1 g/L of nepetalactone, the active cat- attractant in catnip.

SARS-CoV-2 Detection with De Novo-Designed Synthetic Riboregulators

SARS-CoV-2 is a human pathogen and the main cause of COVID-19 disease, announced as a global pandemic by the World Health Organization. COVID-19 is characterized by severe conditions, and early diagnosis can make dramatic changes for both personal and public health. Low-cost, easy-to-use diagnostic capabilities can have a very critical role in controlling the transmission of the disease. Here, we are reporting a state-of-the-art diagnostic tool developed with an in vitro synthetic biology approach by employing engineered de novo riboregulators. Our design coupled with a home-made point-of-care device can detect and report the presence of SARS-CoV-2-specific genes. The presence of SARS-CoV-2-related genes triggers the translation of sfGFP mRNAs, resulting in a green fluorescence output. The approach proposed here has the potential of being a game changer in SARS-CoV-2 diagnostics by providing an easy-to-run, low-cost diagnostic capability.

A Biosensor for Detection of Indole Metabolites

Microbially produced indole metabolites serve as a diverse family of interspecies and interkingdom signaling molecules in the context of human health, crop production, and antibiotic resistance. We mined the protein database for sensors of indole metabolites and developed a biosensor for indole-3-aldehyde (I3A). Microbially produced I3A has been associated with reducing inflammation in diseases such as ulcerative colitis by stimulating the aryl hydrocarbon receptor pathway. We engineered an E. coli strain embedded with a single plasmid carrying a chimeric two-component system that detects I3A. Our I3A receptor characterization confirmed binding site residues that contribute to the sensor's I3A detection range of 0.1-10 μM. This new I3A biosensor opens the door to sensing indole metabolites produced at various host-microbe interfaces and provides new parts for synthetic biology applications.

Electronic signals are electrogenetically relayed to control cell growth and co-culture composition

There is much to be gained by enabling electronic interrogation and control of biological function. While the benefits of bioelectronics that rely on potential-driven ionic flows are well known (electrocardiograms, defibrillators, neural prostheses, etc) there are relatively few advances targeting nonionic molecular networks, including genetic circuits. Redox activities combine connectivity to electronics with the potential for specific genetic control in cells. Here, electrode-generated hydrogen peroxide is used to actuate an electrogenetic "relay" cell population, which interprets the redox cue and synthesizes a bacterial signaling molecule (quorum sensing autoinducer AI-1) that, in turn, signals increased growth rate in a second population. The dramatically increased growth rate of the second population is enabled by expression of a phosphotransferase system protein, HPr, which is important for glucose transport. The potential to electronically modulate cell growth via direct genetic control will enable new opportunities in the treatment of disease and manufacture of biological therapeutics and other molecules.

Engineered Illumination Devices for Optogenetic Control of Cellular Signaling Dynamics

Spatially and temporally varying patterns of morphogen signals during development drive cell fate specification at the proper location and time. However, current in vitro methods typically do not allow for precise, dynamic spatiotemporal control of morphogen signaling and are thus insufficient to readily study how morphogen dynamics affect cell behavior. Here, we show that optogenetic Wnt/β-catenin pathway activation can be controlled at user-defined intensities, temporal sequences, and spatial patterns using engineered illumination devices for optogenetic photostimulation and light activation at variable amplitudes (LAVA). By patterning human embryonic stem cell (hESC) cultures with varying light intensities, LAVA devices enabled dose-responsive control of optoWnt activation and Brachyury expression. Furthermore, time-varying and spatially localized patterns of light revealed tissue patterning that models the embryonic presentation of Wnt signals in vitro. LAVA devices thus provide a low-cost, user-friendly method for high-throughput and spatiotemporal optogenetic control of cell signaling for applications in developmental and cell biology.

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.

Dynamical Modeling of Optogenetic Circuits in Yeast for Metabolic Engineering Applications

Dynamic control of engineered microbes using light via optogenetics has been demonstrated as an effective strategy for improving the yield of biofuels, chemicals, and other products. An advantage of using light to manipulate microbial metabolism is the relative simplicity of interfacing biological and computer systems, thereby enabling in silico control of the microbe. Using this strategy for control and optimization of product yield requires an understanding of how the microbe responds in real-time to the light inputs. Toward this end, we present mechanistic models of a set of yeast optogenetic circuits. We show how these models can predict short- and long-time response to varying light inputs and how they are amenable to use with model predictive control (the industry standard among advanced control algorithms). These models reveal dynamics characterized by time-scale separation of different circuit components that affect the steady and transient levels of the protein under control of the circuit. Ultimately, this work will help enable real-time control and optimization tools for improving yield and consistency in the production of biofuels and chemicals using microbial fermentations.

Real-Time Optogenetics System for Controlling Gene Expression Using a Model-Based Design

Optimization of engineered biological systems requires precise control over the rates and timing of gene expression. Optogenetics is used to dynamically control gene expression as an alternative to conventional chemical-based methods since it provides a more convenient interface between digital control software and microbial culture. Here, we describe the construction of a real-time optogenetics platform, which performs closed-loop control over the CcaR-CcaS two-plasmid system in Escherichia coli. We showed the first model-based design approach by constructing a nonlinear representation of the CcaR-CcaS system, tuned the model through open-loop experimentation to capture the experimental behavior, and applied the model in silico to inform the necessary changes to build a closed-loop optogenetic control system. Our system periodically induces and represses the CcaR-CcaS system while recording optical density and fluorescence using image processing techniques. We highlight the facile nature of constructing our system and how our model-based design approach will potentially be used to model other systems requiring closed-loop optogenetic control.

Synthetic Biological Approaches for Optogenetics and Tools for Transcriptional Light-Control in Bacteria

Light has become established as a tool not only to visualize and investigate but also to steer biological systems. This review starts by discussing the unique features that make light such an effective control input in biology. It then gives an overview of how light-control came to progress, starting with photoactivatable compounds and leading up to current genetic implementations using optogenetic approaches. The review then zooms in on optogenetics, focusing on photosensitive proteins, which form the basis for optogenetic engineering using synthetic biological approaches. As the regulation of transcription provides a highly versatile means for steering diverse biological functions, the focus of this review then shifts to transcriptional light regulators, which are presented in the biotechnologically highly relevant model organism Escherichia coli.

Design and Characterization of Rapid Optogenetic Circuits for Dynamic Control in Yeast Metabolic Engineering

The use of optogenetics in metabolic engineering for light-controlled microbial chemical production raises the prospect of utilizing control and optimization techniques routinely deployed in traditional chemical manufacturing. However, such mechanisms require well-characterized, customizable tools that respond fast enough to be used as real-time inputs during fermentations. Here, we present OptoINVRT7, a new rapid optogenetic inverter circuit to control gene expression in Saccharomyces cerevisiae. The circuit induces gene expression in only 0.6 h after switching cells from light to darkness, which is at least 6 times faster than previous OptoINVRT optogenetic circuits used for chemical production. In addition, we introduce an engineered inducible GAL1 promoter (P_GAL1-S), which is stronger than any constitutive or inducible promoter commonly used in yeast. Combining OptoINVRT7 with P_GAL1-S achieves strong and light-tunable levels of gene expression with as much as 132.9 ± 22.6-fold induction in darkness. The high performance of this new optogenetic circuit in controlling metabolic enzymes boosts production of lactic acid and isobutanol by more than 50% and 15%, respectively. The strength and controllability of OptoINVRT7 and P_GAL1-S open the door to applying process control tools to engineered metabolisms to improve robustness and yields in microbial fermentations for chemical production.

Optogenetic control of gut bacterial metabolism to promote longevity

Gut microbial metabolism is associated with host longevity. However, because it requires direct manipulation of microbial metabolism in situ, establishing a causal link between these two processes remains challenging. We demonstrate an optogenetic method to control gene expression and metabolite production from bacteria residing in the host gut. We genetically engineer an Escherichia coli strain that secretes colanic acid (CA) under the quantitative control of light. Using this optogenetically-controlled strain to induce CA production directly in the Caenorhabditis elegans gut, we reveal the local effect of CA in protecting intestinal mitochondria from stress-induced hyper-fragmentation. We also demonstrate that the lifespan-extending effect of this strain is positively correlated with the intensity of green light, indicating a dose-dependent CA benefit on the host. Thus, optogenetics can be used to achieve quantitative and temporal control of gut bacterial metabolism in order to reveal its local and systemic effects on host health and aging.

The Promise of Optogenetics for Bioproduction: Dynamic Control Strategies and Scale-Up Instruments

Progress in metabolic engineering and synthetic and systems biology has made bioproduction an increasingly attractive and competitive strategy for synthesizing biomolecules, recombinant proteins and biofuels from renewable feedstocks. Yet, due to poor productivity, it remains difficult to make a bioproduction process economically viable at large scale. Achieving dynamic control of cellular processes could lead to even better yields by balancing the two characteristic phases of bioproduction, namely, growth versus production, which lie at the heart of a trade-off that substantially impacts productivity. The versatility and controllability offered by light will be a key element in attaining the level of control desired. The popularity of light-mediated control is increasing, with an expanding repertoire of optogenetic systems for novel applications, and many optogenetic devices have been designed to test optogenetic strains at various culture scales for bioproduction objectives. In this review, we aim to highlight the most important advances in this direction. We discuss how optogenetics is currently applied to control metabolism in the context of bioproduction, describe the optogenetic instruments and devices used at the laboratory scale for strain development, and explore how current industrial-scale bioproduction processes could be adapted for optogenetics or could benefit from existing photobioreactor designs. We then draw attention to the steps that must be undertaken to further optimize the control of biological systems in order to take full advantage of the potential offered by microbial factories.

Optogenetic interrogation and control of cell signaling

Signaling networks control the flow of information through biological systems and coordinate the chemical processes that constitute cellular life. Optogenetic actuators - genetically encoded proteins that undergo light-induced changes in activity or conformation - are useful tools for probing signaling networks over time and space. They have permitted detailed dissections of cellular proliferation, differentiation, motility, and death, and enabled the assembly of synthetic systems with applications in areas as diverse as photography, chemical synthesis, and medicine. In this review, we provide a brief introduction to optogenetic systems and describe their application to molecular-level analyses of cell signaling. Our discussion highlights important research achievements and speculates on future opportunities to exploit optogenetic systems in the study and assembly of complex biochemical networks.

In situ characterisation and manipulation of biological systems with Chi.Bio

The precision and repeatability of in vivo biological studies is predicated upon methods for isolating a targeted subsystem from external sources of noise and variability. However, in many experimental frameworks, this is made challenging by nonstatic environments during host cell growth, as well as variability introduced by manual sampling and measurement protocols. To address these challenges, we developed Chi.Bio, a parallelised open-source platform that represents a new experimental paradigm in which all measurement and control actions can be applied to a bulk culture in situ. In addition to continuous-culturing capabilities, it incorporates tunable light outputs, spectrometry, and advanced automation features. We demonstrate its application to studies of cell growth and biofilm formation, automated in silico control of optogenetic systems, and readout of multiple orthogonal fluorescent proteins in situ. By integrating precise measurement and actuation hardware into a single low-cost platform, Chi.Bio facilitates novel experimental methods for synthetic, systems, and evolutionary biology and broadens access to cutting-edge research capabilities.

Multiplexing cell-cell communication

The engineering of advanced multicellular behaviors, such as the programmed growth of biofilms or tissues, requires cells to communicate multiple aspects of physiological information. Unfortunately, few cell-cell communication systems have been developed for synthetic biology. Here, we engineer a genetically encoded channel selector device that enables a single communication system to transmit two separate intercellular conversations. Our design comprises multiplexer and demultiplexer sub-circuits constructed from a total of 12 CRISPRi-based transcriptional logic gates, an acyl homoserine lactone-based communication module, and three inducible promoters that enable small molecule control over the conversations. Experimentally parameterized mathematical models of the sub-components predict the steady state and dynamical performance of the full system. Multiplexed cell-cell communication has applications in synthetic development, metabolic engineering, and other areas requiring the coordination of multiple pathways among a community of cells.

A single-component light sensor system allows highly tunable and direct activation of gene expression in bacterial cells

Light-regulated modules offer unprecedented new ways to control cellular behaviour with precise spatial and temporal resolution. Among a variety of bacterial light-switchable gene expression systems, single-component systems consisting of single transcription factors would be more useful due to the advantages of speed, simplicity, and versatility. In the present study, we developed a single-component light-activated bacterial gene expression system (eLightOn) based on a novel LOV domain from Rhodobacter sphaeroides (RsLOV). The eLightOn system showed significant improvements over the existing single-component bacterial light-activated expression systems, with benefits including a high ON/OFF ratio of >500-fold, a high activation level, fast activation kinetics, and/or good adaptability. Additionally, the induction characteristics, including regulatory windows, activation kinetics and light sensitivities, were highly tunable by altering the expression level of LexRO. We demonstrated the usefulness of the eLightOn system in regulating cell division and swimming by controlling the expression of the FtsZ and CheZ genes, respectively, as well as constructing synthetic Boolean logic gates using light and arabinose as the two inputs. Taken together, our data indicate that the eLightOn system is a robust and highly tunable tool for quantitative and spatiotemporal control of bacterial gene expression.

Heterogeneity coordinates bacterial multi-gene expression in single cells

For a genetically identical microbial population, multi-gene expression in various environments requires effective allocation of limited resources and precise control of heterogeneity among individual cells. However, it is unclear how resource allocation and cell-to-cell variation jointly shape the overall performance. Here we demonstrate a Simpson’s paradox during overexpression of multiple genes: two competing proteins in single cells correlated positively for every induction condition, but the overall correlation was negative. Yet this phenomenon was not observed between two competing mRNAs in single cells. Our analytical framework shows that the phenomenon arises from competition for translational resource, with the correlation modulated by both mRNA and ribosome variability. Thus, heterogeneity plays a key role in single-cell multi-gene expression and provides the population with an evolutionary advantage, as demonstrated in this study.

Microbes perform multitasking for a wide range of purposes, including survival, adaptation, colonization, and evolution. Both modelling and experimental results at the ensemble level reveal trade-offs between different tasks due to resource competition, but it is unclear how single cells allocate limited intracellular resources to perform multitasking, and how does a population coordinate single cell performances during multitasking to maximize population efficiencies. In this study, we address this question by using bacterial multi-gene overexpression as the basic form of multitasking. We discovered and analyzed a statistical phenomenon called Simpson’s paradox, where competing proteins in single cells correlate positively at each constant condition, although the proteins correlate negatively when all conditions are combined. We demonstrate that the phenomenon arises from competition for translational resources, with the correlation modulated by heterogeneity of both mRNA and ribosomes. We further show that heterogeneity coordinates multiple functional modules, conferring an evolutionary advantage on the population. Our work discloses that heterogeneity in the form of Simpson’s paradox is an important phenomenon in coordinating multi-gene expression.

Impact framework: A python package for writing data analysis workflows to interpret microbial physiology

Microorganisms can be genetically engineered to solve a range of challenges in diverse including health, environmental protection and sustainability. The natural complexity of biological systems makes this an iterative cycle, perturbing metabolism and making stepwise progress toward a desired phenotype through four major stages: design, build, test, and data interpretation. This cycle has been accelerated by advances in molecular biology (e.g. robust DNA synthesis and assembly techniques), liquid handling automation and scale-down characterization platforms, generating large heterogeneous data sets. Here, we present an extensible Python package for scientists and engineers working with large biological data sets to interpret, model, and visualize data: the IMPACT (Integrated Microbial Physiology: Analysis, Characterization and Translation) framework. Impact aims to ease the development of Python-based data analysis workflows for a range of stakeholders in the bioengineering process, offering open-source tools for data analysis, physiology characterization and translation to visualization. Using this framework, biologists and engineers can opt for reproducible and extensible programmatic data analysis workflows, mediating a bottleneck limiting the throughput of microbial engineering. The Impact framework is available at https://github.com/lmse/impact.