Dynamic cybergenetic control of bacterial co-culture composition via optogenetic feedback

Strategies and tools to construct stable and efficient artificial coculture systems as biosynthetic platforms for biomass conversion

Inspired by the natural symbiotic relationships between diverse microbial members, researchers recently focused on modifying microbial chassis to create artificial coculture systems using synthetic biology tools. An increasing number of scientists are now exploring these systems as innovative biosynthetic platforms for biomass conversion. While significant advancements have been achieved, challenges remain in maintaining the stability and productivity of these systems. Sustaining an optimal population ratio over a long time period and balancing anabolism and catabolism during cultivation have proven difficult. Key issues, such as competitive or antagonistic relationships between microbial members, as well as metabolic imbalances and maladaptation, are critical factors affecting the stability and productivity of artificial coculture systems. In this article, we critically review current strategies and methods for improving the stability and productivity of these systems, with a focus on recent progress in biomass conversion. We also provide insights into future research directions, laying the groundwork for further development of artificial coculture biosynthetic platforms.

Optimizing HMG-CoA Synthase Expression for Enhanced Limonene Production in Escherichia coli through Temporal Transcription Modulation Using Optogenetics

Overexpression of a single enzyme in a multigene heterologous pathway may be out of balance with the other enzymes in the pathway, leading to accumulated toxic intermediates, imbalanced carbon flux, reduced productivity of the pathway, or an inhibited growth phenotype. Therefore, optimal, balanced, and synchronized expression levels of enzymes in a particular metabolic pathway is critical to maximize production of desired compounds while maintaining cell fitness in a growing culture. Furthermore, the optimal intracellular concentration of an enzyme is determined by the expression strength, specific timing/duration, and degradation rate of the enzyme. Here, we modulated the intracellular concentration of a key enzyme, namely HMG-CoA synthase (HMGS), in the heterologous mevalonate pathway by tuning its expression level and period of transcription to enhance limonene production in Escherichia coli. Facilitated by the tuned blue-light inducible BLADE/pBad system, we observed that limonene production was highest (160 mg/L) with an intermediate transcription level of HMGS from moderate light illumination (41 au, 150 s ON/150 s OFF) throughout the growth. Owing to the easy penetration and removal of blue-light illumination from the growing culture which is hard to obtain using conventional chemical-based induction, we further explored different induction patterns of HMGS under strong light illumination (2047 au, 300 s ON) for different durations along the growth phases. We identified a specific timing of HMGS expression in the log phase (3-9 h) that led to optimal limonene production (200 mg/L). This is further supported by a mathematical model that predicts several periods of blue-light illumination (3-9 h, 0-9 h, 3-12 h, 0-12 h) to achieve an optimal expression level of HMGS that maximizes limonene production and maintains cell fitness. Compared to moderate and prolonged transcription (41 au, 150 s ON/150 s OFF, 0-73 h), strong but time-limited transcription (2047 au, 300 s ON, 3-9 h) of HMGS could maintain its optimal intracellular concentration and further increased limonene production up to 92% (250 mg/L) in the longer incubation (up to 73 h) without impacting cell fitness. This work has provided new insight into the "right amount" and "just-in-time" expression of a critical metabolite enzyme in the upper module of the mevalonate pathway using optogenetics. This study would complement previous findings in modulating HMGS expression and potentially be applicable to heterologous production of other terpenoids in E. coli.

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.

Engineering Microbial Consortia as Living Materials: Advances and Prospectives

The field of Engineered Living Materials (ELMs) integrates engineered living organisms into natural biomaterials to achieve diverse objectives. Multiorganism consortia, prevalent in both naturally occurring and synthetic microbial cultures, exhibit complex functionalities and interrelationships, extending the scope of what can be achieved with individual engineered bacterial strains. However, the ELMs comprising microbial consortia are still in the developmental stage. In this Review, we introduce two strategies for designing ELMs constituted of microbial consortia: a top-down strategy, which involves characterizing microbial interactions and mimicking and reconstructing natural ecosystems, and a bottom-up strategy, which entails the rational design of synthetic consortia and their assembly with material substrates to achieve user-defined functions. Next, we summarize technologies from synthetic biology that facilitate the efficient engineering of microbial consortia for performing tasks more complex than those that can be done with single bacterial strains. Finally, we discuss essential challenges and future perspectives for microbial consortia-based ELMs.

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.

Induction of bacterial expression at the mRNA level by light

Vital organismal processes, including development, differentiation and adaptation, involve altered gene expression. Although expression is frequently controlled at the transcriptional stage, various regulation mechanisms operate at downstream levels. Here, we leverage the photoreceptor NmPAL to optogenetically induce RNA refolding and the translation of bacterial mRNAs. Blue-light-triggered NmPAL binding disrupts a cis-repressed mRNA state, thereby relieves obstruction of translation initiation, and upregulates gene expression. Iterative probing and optimization of the circuit, dubbed riboptoregulator, enhanced induction to 30-fold. Given action at the mRNA level, the riboptoregulator can differentially regulate individual structural genes within polycistronic operons. Moreover, it is orthogonal to and can be wed with other gene-regulatory circuits for nuanced and more stringent gene-expression control. We thus advance the pAurora2 circuit that combines transcriptional and translational mechanisms to optogenetically increase bacterial gene expression by >1000-fold. The riboptoregulator strategy stands to upgrade numerous regulatory circuits and widely applies to expression control in microbial biotechnology, synthetic biology and materials science.

Development of specialized devices for microbial experimental evolution

Experimental evolution of microbial cells provides valuable information on evolutionary dynamics, such as mutations that contribute to fitness gain under given selection pressures. Although experimental evolution is a promising tool in evolutionary biology and bioengineering, long-term culture experiments under multiple environmental conditions often impose an excessive workload on researchers. Therefore, the development of automated systems significantly contributes to the advancement of experimental evolutionary research. This review presents several specialized devices designed for experimental evolution studies, such as an automated system for high-throughput culture experiments, a culture device that generate a temperature gradient, and an automated ultraviolet (UV) irradiation culture device. The ongoing development of such specialized devices is poised to continually expand new frontiers in experimental evolution research.

Synthetic microbial ecology: engineering habitats for modular consortia

Microbiomes, the complex networks of micro-organisms and the molecules through which they interact, play a crucial role in health and ecology. Over at least the past two decades, engineering biology has made significant progress, impacting the bio-based industry, health, and environmental sectors; but has only recently begun to explore the engineering of microbial ecosystems. The creation of synthetic microbial communities presents opportunities to help us understand the dynamics of wild ecosystems, learn how to manipulate and interact with existing microbiomes for therapeutic and other purposes, and to create entirely new microbial communities capable of undertaking tasks for industrial biology. Here, we describe how synthetic ecosystems can be constructed and controlled, focusing on how the available methods and interaction mechanisms facilitate the regulation of community composition and output. While experimental decisions are dictated by intended applications, the vast number of tools available suggests great opportunity for researchers to develop a diverse array of novel microbial ecosystems.

Inteins: A Swiss army knife for synthetic biology

Inteins are proteins found in nature that execute protein splicing. Among them, split inteins stand out for their versatility and adaptability, presenting creative solutions for addressing intricate challenges in various biological applications. Their exquisite attributes, including compactness, reliability, orthogonality, low toxicity, and irreversibility, make them of interest to various fields including synthetic biology, biotechnology and biomedicine. In this review, we delve into the inherent challenges of using inteins, present approaches for overcoming these challenges, and detail their reliable use for specific cellular tasks. We will discuss the use of conditional inteins in areas like cancer therapy, drug screening, patterning, infection treatment, diagnostics and biocontainment. Additionally, we will underscore the potential of inteins in executing basic logical operations with practical implications. We conclude by showcasing their potential in crafting complex genetic circuits for performing computations and feedback control that achieves robust perfect adaptation.

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.

Microbiome modeling: a beginner's guide

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Lighting the way: recent developments and applications in molecular optogenetics

Molecular optogenetics utilizes genetically encoded, light-responsive protein switches to control the function of molecular processes. Over the last two years, there have been notable advances in the development of novel optogenetic switches, their utilization in elucidating intricate signaling pathways, and their progress toward practical applications in biotechnological processes, material sciences, and therapeutic applications. In this review, we discuss these areas, offer insights into recent developments, and contemplate future directions.

Optogenetic Control of Bacterial Cell-Cell Adhesion Dynamics: Unraveling the Influence on Biofilm Architecture and Functionality

The transition of bacteria from an individualistic to a biofilm lifestyle profoundly alters their biology. During biofilm development, the bacterial cell-cell adhesions are a major determinant of initial microcolonies, which serve as kernels for the subsequent microscopic and mesoscopic structure of the biofilm, and determine the resulting functionality. In this study, the significance of bacterial cell-cell adhesion dynamics on bacterial aggregation and biofilm maturation is elucidated. Using photoswitchable adhesins between bacteria, modifying the dynamics of bacterial cell-cell adhesions with periodic dark-light cycles is systematic. Dynamic cell-cell adhesions with liquid-like behavior improve bacterial aggregation and produce more compact microcolonies than static adhesions with solid-like behavior in both experiments and individual-based simulations. Consequently, dynamic cell-cell adhesions give rise to earlier quorum sensing activation, better intermixing of different bacterial populations, improved biofilm maturation, changes in the growth of cocultures, and higher yields in fermentation. The here presented approach of tuning bacterial cell-cell adhesion dynamics opens the door for regulating the structure and function of biofilms and cocultures with potential biotechnological applications.

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

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

Dynamic Multiplexed Control and Modeling of Optogenetic Systems Using the High-Throughput Optogenetic Platform, Lustro

The ability to control cellular processes using optogenetics is inducer-limited, with most optogenetic systems responding to blue light. To address this limitation, we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems. Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive split transcription factors in the budding yeast, Saccharomyces cerevisiae. We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control. This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs, with broad implications for biotechnology and bioengineering.

A coarse-grained bacterial cell model for resource-aware analysis and design of synthetic gene circuits

Within a cell, synthetic and native genes compete for expression machinery, influencing cellular process dynamics through resource couplings. Models that simplify competitive resource binding kinetics can guide the design of strategies for countering these couplings. However, in bacteria resource availability and cell growth rate are interlinked, which complicates resource-aware biocircuit design. Capturing this interdependence requires coarse-grained bacterial cell models that balance accurate representation of metabolic regulation against simplicity and interpretability. We propose a coarse-grained E. coli cell model that combines the ease of simplified resource coupling analysis with appreciation of bacterial growth regulation mechanisms and the processes relevant for biocircuit design. Reliably capturing known growth phenomena, it provides a unifying explanation to disparate empirical relations between growth and synthetic gene expression. Considering a biomolecular controller that makes cell-wide ribosome availability robust to perturbations, we showcase our model's usefulness in numerically prototyping biocircuits and deriving analytical relations for design guidance.

Unlocking the potential of optogenetics in microbial applications

Optogenetics is a powerful approach that enables researchers to use light to dynamically manipulate cellular behavior. Since the first published use of optogenetics in synthetic biology, the field has expanded rapidly, yielding a vast array of tools and applications. Despite its immense potential for achieving high spatiotemporal precision, optogenetics has predominantly been employed as a substitute for conventional chemical inducers. In this short review, we discuss key features of microbial optogenetics and highlight applications for understanding biology, cocultures, bioproduction, biomaterials, and therapeutics, in which optogenetics is more fully utilized to realize goals not previously possible by other methods.

Photoactivation of LOV domains with chemiluminescence

Optogenetics has opened new possibilities in the remote control of diverse cellular functions with high spatiotemporal precision using light. However, delivering light to optically non-transparent systems remains a challenge. Here, we describe the photoactivation of light-oxygen-voltage-sensing domains (LOV domains) with in situ generated light from a chemiluminescence reaction between luminol and H2O2. This activation is possible due to the spectral overlap between the blue chemiluminescence emission and the absorption bands of the flavin chromophore in LOV domains. All four LOV domain proteins with diverse backgrounds and structures (iLID, BcLOV4, nMagHigh/pMagHigh, and VVDHigh) were photoactivated by chemiluminescence as demonstrated using a bead aggregation assay. The photoactivation with chemiluminescence required a critical light-output below which the LOV domains reversed back to their dark state with protein characteristic kinetics. Furthermore, spatially confined chemiluminescence produced inside giant unilamellar vesicles (GUVs) was able to photoactivate proteins both on the membrane and in solution, leading to the recruitment of the corresponding proteins to the GUV membrane. Finally, we showed that reactive oxygen species produced by neutrophil like cells can be converted into sufficient chemiluminescence to recruit the photoswitchable protein BcLOV4-mCherry from solution to the cell membrane. The findings highlight the utility of chemiluminescence as an endogenous light source for optogenetic applications, offering new possibilities for studying cellular processes in optically non-transparent systems.

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.

Dynamic Multiplexed Control and Modeling of Optogenetic Systems Using the High-Throughput Optogenetic Platform, Lustro

The ability to control cellular processes using optogenetics is inducer-limited, with most optogenetic systems responding to blue light. To address this limitation we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems. Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive spit transcription factors in the budding yeast, Saccharomyces cerevisiae . We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control. This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs, with broad implications for biotechnology and bioengineering.

Graphical abstract

Advances in the dynamic control of metabolic pathways in Saccharomyces cerevisiae

The metabolic engineering of Saccharomyces cerevisiae has great potential for enhancing the production of high-value chemicals and recombinant proteins. Recent studies have demonstrated the effectiveness of dynamic regulation as a strategy for optimizing metabolic flux and improving production efficiency. In this review, we provide an overview of recent advancements in the dynamic regulation of S. cerevisiae metabolism. Here, we focused on the successful utilization of transcription factor (TF)-based biosensors within the dynamic regulatory network of S. cerevisiae. These biosensors are responsive to a wide range of endogenous and exogenous signals, including chemical inducers, light, temperature, cell density, intracellular metabolites, and stress. Additionally, we explored the potential of omics tools for the discovery of novel responsive promoters and their roles in fine-tuning metabolic networks. We also provide an outlook on the development trends in this field.

Advances in automated and reactive flow cytometry for synthetic biotechnology

Automated flow cytometry (FC) has been initially considered for bioprocess monitoring and optimization. More recently, new physical and software interfaces have been made available, facilitating the access to this technology for labs and industries. It also comes with new capabilities, such as being able to act on the cultivation conditions based on population data. This approach, known as reactive FC, extended the range of applications of automated FC to bioprocess control and the stabilization of cocultures, but also to the broad field of synthetic and systems biology for the characterization of gene circuits. However, several issues must be addressed before automated and reactive FC can be considered standard and modular technologies.

A hybrid in silico/in-cell controller for microbial bioprocesses with process-model mismatch

Bioprocess optimization using mathematical models is prevalent, yet the discrepancy between model predictions and actual processes, known as process-model mismatch (PMM), remains a significant challenge. This study proposes a novel hybrid control system called the hybrid in silico/in-cell controller (HISICC) to address PMM by combining model-based optimization (in silico feedforward controller) with feedback controllers utilizing synthetic genetic circuits integrated into cells (in-cell feedback controller). We demonstrated the efficacy of HISICC using two engineered Escherichia coli strains, TA1415 and TA2445, previously developed for isopropanol (IPA) production. TA1415 contains a metabolic toggle switch (MTS) to manage the competition between cell growth and IPA production for intracellular acetyl-CoA by responding to external input of isopropyl β-D-1-thiogalactopyranoside (IPTG). TA2445, in addition to the MTS, has a genetic circuit that detects cell density to autonomously activate MTS. The combination of TA2445 with an in silico controller exemplifies HISICC implementation. We constructed mathematical models to optimize IPTG input values for both strains based on the two-compartment model and validated these models using experimental data of the IPA production process. Using these models, we evaluated the robustness of HISICC against PMM by comparing IPA yields with two strains in simulations assuming various magnitudes of PMM in cell growth rates. The results indicate that the in-cell feedback controller in TA2445 effectively compensates for PMM by modifying MTS activation timing. In conclusion, the HISICC system presents a promising solution to the PMM problem in bioprocess engineering, paving the way for more efficient and reliable optimization of microbial bioprocesses.

High-Throughput Optogenetics Experiments in Yeast Using the Automated Platform Lustro

Optogenetics offers precise control over cellular behavior by utilizing genetically encoded light-sensitive proteins. However, optimizing these systems to achieve the desired functionality often requires multiple design-build-test cycles, which can be time-consuming and labor-intensive. To address this challenge, we have developed Lustro, a platform that combines light stimulation with laboratory automation, enabling efficient high-throughput screening and characterization of optogenetic systems. Lustro utilizes an automation workstation equipped with an illumination device, a shaking device, and a plate reader. By employing a robotic arm, Lustro automates the movement of a microwell plate between these devices, allowing for the stimulation of optogenetic strains and the measurement of their response. This protocol provides a step-by-step guide on using Lustro to characterize optogenetic systems for gene expression control in the budding yeast Saccharomyces cerevisiae. The protocol covers the setup of Lustro's components, including the integration of the illumination device with the automation workstation. It also provides detailed instructions for programming the illumination device, plate reader, and robot, ensuring smooth operation and data acquisition throughout the experimental process.

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 the high-throughput characterization of optogenetic systems across a range of biological systems and 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.

Recent advances, opportunities and challenges in cybergenetic identification and control of biomolecular networks

Cybergenetics is a new area of research aimed at developing digital and biological controllers for living systems. Synthetic biologists have begun exploiting cybergenetic tools and platforms to both accelerate the development of mathematical models and develop control strategies for complex biological phenomena. Here, we review the state of the art in cybergenetic identification and control. Our aim is to lower the entry barrier to this field and foster the adoption of methods and technologies that will accelerate the pace at which Synthetic Biology progresses toward 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.

An optogenetic toolkit for light-inducible antibiotic resistance

Antibiotics are a key control mechanism for synthetic biology and microbiology. Resistance genes are used to select desired cells and regulate bacterial populations, however their use to-date has been largely static. Precise spatiotemporal control of antibiotic resistance could enable a wide variety of applications that require dynamic control of susceptibility and survival. Here, we use light-inducible Cre recombinase to activate expression of drug resistance genes in Escherichia coli. We demonstrate light-activated resistance to four antibiotics: carbenicillin, kanamycin, chloramphenicol, and tetracycline. Cells exposed to blue light survive in the presence of lethal antibiotic concentrations, while those kept in the dark do not. To optimize resistance induction, we vary promoter, ribosome binding site, and enzyme variant strength using chromosome and plasmid-based constructs. We then link inducible resistance to expression of a heterologous fatty acid enzyme to increase production of octanoic acid. These optogenetic resistance tools pave the way for spatiotemporal control of cell survival.

Enhancing chemical and biological diversity by co-cultivation

In natural product research, microbial metabolites have tremendous potential to provide new therapeutic agents since extremely diverse chemical structures can be found in the nearly infinite microbial population. Conventionally, these specialized metabolites are screened by single-strain cultures. However, owing to the lack of biotic and abiotic interactions in monocultures, the growth conditions are significantly different from those encountered in a natural environment and result in less diversity and the frequent re-isolation of known compounds. In the last decade, several methods have been developed to eventually understand the physiological conditions under which cryptic microbial genes are activated in an attempt to stimulate their biosynthesis and elicit the production of hitherto unexpressed chemical diversity. Among those, co-cultivation is one of the most efficient ways to induce silenced pathways, mimicking the competitive microbial environment for the production and holistic regulation of metabolites, and has become a golden methodology for metabolome expansion. It does not require previous knowledge of the signaling mechanism and genome nor any special equipment for cultivation and data interpretation. Several reviews have shown the potential of co-cultivation to produce new biologically active leads. However, only a few studies have detailed experimental, analytical, and microbiological strategies for efficiently inducing bioactive molecules by co-culture. Therefore, we reviewed studies applying co-culture to induce secondary metabolite pathways to provide insights into experimental variables compatible with high-throughput analytical procedures. Mixed-fermentation publications from 1978 to 2022 were assessed regarding types of co-culture set-ups, metabolic induction, and interaction effects.

Reconfiguring the Challenge of Biological Complexity as a Resource for Biodesign

Biological complexity is widely seen as the central, intractable challenge of engineering biology. Yet this challenge has been constructed through the field's dominant metaphors. Alternative ways of thinking-latent in progressive experimental approaches, but rarely articulated as such-could instead position complexity as engineering biology's greatest resource. We outline how assumptions about engineered microorganisms have been built into the field, carried by entrenched metaphors, even as contemporary methods move beyond them. We suggest that alternative metaphors would better align engineering biology's conceptual infrastructure with the field's move away from conventionally engineering-inspired methods toward biology-centric ones. Innovating new conceptual frameworks would also enable better aligning scientific work with higher-level conversations about that work. Such innovation-thinking about how engineering microbes might be more like user-centered design than like programming a computer or building a car-could highlight complexity as a resource to leverage, not a problem to erase or negate.

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.

Cybergenetic control of microbial community composition

The use of bacterial communities in bioproduction instead of monocultures has potential advantages including increased productivity through division of labour, ability to utilise cheaper substrates, and robustness against perturbations. A key challenge in the application of engineered bacterial communities is the ability to reliably control the composition of the community in terms of its constituent species. This is crucial to prevent faster growing species from outcompeting others with a lower relative fitness, and to ensure that all species are present at an optimal ratio during different steps in a biotechnological process. In contrast to purely biological approaches such as synthetic quorum sensing circuits or paired auxotrophies, cybergenetic control techniques - those in which computers interface with living cells-are emerging as an alternative approach with many advantages. The community composition is measured through methods such as fluorescence intensity or flow cytometry, with measured data fed real-time into a computer. A control action is computed using a variety of possible control algorithms and then applied to the system, with actuation taking the form of chemical (e.g., inducers, nutrients) or physical (e.g., optogenetic, mechanical) inputs. Subsequent changes in composition are then measured and the cycle repeated, maintaining or driving the system to a desired state. This review discusses recent and future developments in methods for implementing cybergenetic control systems, contrasts their capabilities with those of traditional biological methods of population control, and discusses future directions and outstanding challenges for the field.