Genomic mining of prokaryotic repressors for orthogonal logic gates

Quantum-inspired logic for advanced Transcriptional Programming

The tenets of intelligent biological systems are (i) scalable decision-making, (ii) inheritable memory, and (iii) communication. This study aims to increase the complexity of decision-making operations beyond standard Boolean logic, while minimizing the metabolic burden imposed on the chassis cell. To this end, we present a new platform technology for constructing genetic circuits with multiple OUTPUT gene control using fewer INPUTs relative to conventional genetic circuits. Inspired by principles from quantum computing, we engineered synthetic bidirectional promoters, regulated by synthetic transcription factors, to construct 1-INPUT, 2-OUTPUT logical operations-i.e. biological QUBIT and PAULI-X logic gates-designed as compressed genetic circuits. We then layered said gates to engineer additional quantum-inspired logical operations of increasing complexity-e.g. FEYNMAN and TOFFOLI gates. In addition, we engineered a 2-INPUT, 4-OUTPUT quantum operation to showcase the capacity to utilize the entire permutation INPUT space. Finally, we developed a recombinase-based memory operation to remap the truth table between two disparate logic gates-i.e. converting a QUBIT operation to an antithetical PAULI-X operation in situ. This study introduces a novel and versatile synthetic biology toolkit, which expands the biocomputing capacity of Transcriptional Programming via the development of compressed and scalable multi-INPUT/OUTPUT logical operations.

Programmable trans-splicing riboregulators for complex cellular logic computation

Synthetic genetic circuits program the cellular input-output relationships to execute customized functions. However, efforts to scale up these circuits have been hampered by the limited number of reliable regulatory mechanisms with high programmability, performance, predictability and orthogonality. Here we report a class of split-intron-enabled trans-splicing riboregulators (SENTRs) based on de novo designed external guide sequences. SENTR libraries provide low leakage expression, wide dynamic range, high predictability with machine learning and low crosstalk at multiple component levels. SENTRs can sense RNA targets, process signals by logic computation and transduce them into various outputs, either mRNAs or noncoding RNAs. We subsequently demonstrate that digital logic operation with up to six inputs can be implemented using multiple orthogonal SENTRs to regulate a single gene simultaneously and coupling SENTRs with split intein-mediated protein trans-splicing. SENTR represents a powerful and versatile regulatory tool at the post-transcriptional level in Escherichia coli, suggesting broad biotechnological applications.

Why cellular computations challenge our design principles

Biological systems inherently perform computations, inspiring synthetic biologists to engineer biological systems capable of executing predefined computational functions for diverse applications. Typically, this involves applying principles from the design of conventional silicon-based computers to create novel biological systems, such as genetic Boolean gates and circuits. However, the natural evolution of biological computation has not adhered to these principles, and this distinction warrants careful consideration. Here, we explore several concepts connecting computational theory, living cells, and computers, which may offer insights into the development of increasingly sophisticated biological computations. While conventional computers approach theoretical limits, solving nearly all problems that are computationally solvable, biological computers have the opportunity to outperform them in specific niches and problem domains. Crucially, biocomputation does not necessarily need to scale to rival or replicate the capabilities of electronic computation. Rather, efforts to re-engineer biology must recognise that life has evolved and optimised itself to solve specific problems using its own principles. Consequently, intelligently designed cellular computations will diverge from traditional computing in both implementation and application.

A direct experimental test of Ohno's hypothesis

Gene duplication drives evolution by providing raw material for proteins with novel functions. An influential hypothesis by Ohno (1970) posits that gene duplication helps genes tolerate new mutations and thus facilitates the evolution of new phenotypes. Competing hypotheses argue that deleterious mutations will usually inactivate gene duplicates too rapidly for Ohno's hypothesis to work. We experimentally tested Ohno's hypothesis by evolving one or exactly two copies of a gene encoding a fluorescent protein in Escherichia coli through several rounds of mutation and selection. We analyzed the genotypic and phenotypic evolutionary dynamics of the evolving populations through high-throughput DNA sequencing, biochemical assays, and engineering of selected variants. In support of Ohno's hypothesis, populations carrying two gene copies displayed higher mutational robustness than those carrying a single gene copy. Consequently, the double-copy populations experienced relaxed purifying selection, evolved higher phenotypic and genetic diversity, carried more mutations and accumulated combinations of key beneficial mutations earlier. However, their phenotypic evolution was not accelerated, possibly because one gene copy rapidly became inactivated by deleterious mutations. Our work provides an experimental platform to test models of evolution by gene duplication, and it supports alternatives to Ohno's hypothesis that point to the importance of gene dosage.

Partitioning of a 2-bit hash function across 66 communicating cells

Powerful distributed computing can be achieved by communicating cells that individually perform simple operations. Here, we report design software to divide a large genetic circuit across cells as well as the genetic parts to implement the subcircuits in their genomes. These tools were demonstrated using a 2-bit version of the MD5 hashing algorithm, which is an early predecessor to the cryptographic functions underlying cryptocurrency. One iteration requires 110 logic gates, which were partitioned across 66 Escherichia coli strains, requiring the introduction of a total of 1.1 Mb of recombinant DNA into their genomes. The strains were individually experimentally verified to integrate their assigned input signals, process this information correctly and propagate the result to the cell in the next layer. This work demonstrates the potential to obtain programable control of multicellular biological processes.

Biological Switches: Past and Future Milestones of Transcription Factor-Based Biosensors

Since the description of the lac operon in 1961 by Jacob and Monod, transcriptional regulation in prokaryotes has been studied extensively and has led to the development of transcription factor-based biosensors. Due to the broad variety of detectable small molecules and their various applications across biotechnology, biosensor research and development have increased exponentially over the past decades. Throughout this period, key milestones in fundamental knowledge, synthetic biology, analytical tools, and computational learning have led to an immense expansion of the biosensor repertoire and its application portfolio. Over the years, biosensor engineering became a more multidisciplinary discipline, combining high-throughput analytical tools, DNA randomization strategies, forward engineering, and advanced protein engineering workflows. Despite these advances, many obstacles remain to fully unlock the potential of biosensor technology. This review analyzes the timeline of key milestones on fundamental research (1960s to 2000s) and engineering strategies (2000s onward), on both the DNA and protein level of biosensors. Moreover, insights into the future perspectives, remaining hurdles, and unexplored opportunities of this promising field are discussed.

Predictive genetic circuit design for phenotype reprogramming in plants

Plants, with intricate molecular networks for environmental adaptation, offer groundbreaking potential for reprogramming with predictive genetic circuits. However, realizing this goal is challenging due to the long cultivation cycle of plants, as well as the lack of reproducible, quantitative methods and well-characterized genetic parts. Here, we establish a rapid (~10 days), quantitative, and predictive framework in plants. A group of orthogonal sensors, modular synthetic promoters, and NOT gates are constructed and quantitatively characterized. A predictive model is developed to predict the designed circuits' behavior accurately. Our versatile and robust framework, validated by constructing 21 two-input circuits with high prediction accuracy (R2 = 0.81), enables multi-state phenotype control in both Arabidopsis thaliana and Nicotiana benthamiana in response to chemical inducers. Our study achieves predictable design and application of synthetic circuits in plants, offering valuable tools for the rapid engineering of plant traits in biotechnology and agriculture.

Harnessing DNA computing and nanopore decoding for practical applications: from informatics to microRNA-targeting diagnostics

DNA computing represents a subfield of molecular computing with the potential to become a significant area of next-generation computation due to the high programmability inherent in the sequence-dependent molecular behaviour of DNA. Recent studies in DNA computing have extended from mathematical informatics to biomedical applications, with a particular focus on diagnostics that exploit the biocompatibility of DNA molecules. The output of DNA computing devices is encoded in nucleic acid molecules, which must then be decoded into human-recognizable signals for practical applications. Nanopore technology, which utilizes an electrical and label-free decoding approach, provides a unique platform to bridge DNA and electronic computing for practical use. In this tutorial review, we summarise the fundamental knowledge, technologies, and methodologies of DNA computing (logic gates, circuits, neural networks, and non-DNA input circuity). We then focus on nanopore-based decoding, and highlight recent advances in medical diagnostics targeting microRNAs as biomarkers. Finally, we conclude with the potential and challenges for the practical implementation of these techniques. We hope that this tutorial will provide a comprehensive insight and enable the general reader to grasp the fundamental principles and diverse applications of DNA computing and nanopore decoding, and will inspire a wide range of scientists to explore and push the boundaries of these technologies.

Versatile xylose and arabinose genetic switches development for yeasts

Inducible transcription systems are essential tools in genetic engineering, where tight control, strong inducibility and fast response with cost-effective inducers are highly desired. However, existing systems in yeasts are rarely used in large-scale fermentations due to either cost-prohibitive inducers or incompatible performance. Here, we developed powerful xylose and arabinose induction systems in Saccharomyces cerevisiae, utilizing eukaryotic activators XlnR and AraRA from Aspergillus species and bacterial repressors XylR and AraRR. By integrating these signals into a highly-structured synthetic promoter, we created dual-mode systems with strong outputs and minimal leakiness. These systems demonstrated over 4000- and 300-fold regulation with strong activation and rapid response. The dual-mode xylose system was fully activated by xylose-rich agricultural residues like corncob hydrolysate, outperforming existing systems in terms of leakiness, inducibility, dynamic range, induction rate, and growth impact on host. We validated their utility in metabolic engineering with high-titer linalool production and demonstrated the transferability of the XlnR-based xylose induction system to Pichia pastoris, Candida glabrata and Candida albicans. This work provides robust genetic switches for yeasts and a general strategy for integrating activation-repression signals into synthetic promoters to achieve optimal performance.

EvoAI enables extreme compression and reconstruction of the protein sequence space

Designing proteins with improved functions requires a deep understanding of how sequence and function are related, a vast space that is hard to explore. The ability to efficiently compress this space by identifying functionally important features is extremely valuable. Here we establish a method called EvoScan to comprehensively segment and scan the high-fitness sequence space to obtain anchor points that capture its essential features, especially in high dimensions. Our approach is compatible with any biomolecular function that can be coupled to a transcriptional output. We then develop deep learning and large language models to accurately reconstruct the space from these anchors, allowing computational prediction of novel, highly fit sequences without prior homology-derived or structural information. We apply this hybrid experimental-computational method, which we call EvoAI, to a repressor protein and find that only 82 anchors are sufficient to compress the high-fitness sequence space with a compression ratio of 1048. The extreme compressibility of the space informs both applied biomolecular design and understanding of natural evolution.

Dynamic control of the plasmid copy number maintained without antibiotics in Escherichia coli

BACKGROUND

Manipulating the gene expression is the key strategy to optimize the metabolic flux. Not only transcription, translation, and post-translation level control, but also the dynamic plasmid copy number (PCN) control has been studied. The dynamic PCN control systems that have been developed to date are based on the understanding of origin replication mechanisms, which limits their application to specific origins of replication and requires the use of antibiotics for plasmid maintenance. In this study, we developed a dynamic PCN control system for Escherichia coli that is maintained without antibiotics. This is achieved by regulating the transcription level of the translation initiation factor IF-1 (infA), an essential gene encoded on the plasmid, while deleting it from the plasmid-bearing host cell.

RESULTS

When validated using GFP as a reporter protein, our system demonstrated a 22-fold dynamic range in PCN within the CloDF13 origin. The system was employed to determine the optimal copy number of the plasmid carrying the cad gene, which converts an intermediate of the tricarboxylic acid cycle (TCA cycle) to itaconic acid. By optimizing the PCN, we could achieve an itaconic acid titer of 3 g/L, which is 5.3-fold higher than the control strain.

CONCLUSIONS

Our system offers a strategy to identify the optimal expression level of genes that have a competitive relationship with metabolic pathways crucial for the growth of the host organism. This approach can potentially be applied to other bacterial hosts by substituting the sensing module or the essential gene.

Fundamentals and Exceptions of the LysR-type Transcriptional Regulators

LysR-type transcriptional regulators (LTTRs) are emerging as a promising group of macromolecules for the field of biosensors. As the largest family of bacterial transcription factors, the LTTRs represent a vast and mostly untapped repertoire of sensor proteins. To fully harness these regulators for transcription factor-based biosensor development, it is crucial to understand their underlying mechanisms and functionalities. In the first part, this Review discusses the established model and features of LTTRs. As dual-function regulators, these inducible transcription factors exude precise control over their regulatory targets. In the second part of this Review, an overview is given of the exceptions to the "classic" LTTR model. While a general regulatory mechanism has helped elucidate the intricate regulation performed by LTTRs, it is essential to recognize the variations within the family. By combining this knowledge, characterization of new regulators can be done more efficiently and accurately, accelerating the expansion of transcriptional sensors for biosensor development. Unlocking the pool of LTTRs would significantly expand the currently limited range of detectable molecules and regulatory functions available for the implementation of novel synthetic genetic circuitry.

Transcriptional regulation of living materials via extracellular electron transfer

Engineered living materials combine the advantages of biological and synthetic systems by leveraging genetic and metabolic programming to control material-wide properties. Here, we demonstrate that extracellular electron transfer (EET), a microbial respiration process, can serve as a tunable bridge between live cell metabolism and synthetic material properties. In this system, EET flux from Shewanella oneidensis to a copper catalyst controls hydrogel cross-linking via two distinct chemistries to form living synthetic polymer networks. We first demonstrate that synthetic biology-inspired design rules derived from fluorescence parameterization can be applied toward EET-based regulation of polymer network mechanics. We then program transcriptional Boolean logic gates to govern EET gene expression, which enables design of computational polymer networks that mechanically respond to combinations of molecular inputs. Finally, we control fibroblast morphology using EET as a bridge for programmed material properties. Our results demonstrate how rational genetic circuit design can emulate physiological behavior in engineered living materials.

Probing the orthogonality and robustness of the mammalian RNA-binding protein Musashi-1 in Escherichia coli

RNA recognition motifs (RRMs) are widespread RNA-binding protein domains in eukaryotes, which represent promising synthetic biology tools due to their compact structure and efficient activity. Yet, their use in prokaryotes is limited and their functionality poorly characterized. Recently, we repurposed a mammalian Musashi protein containing two RRMs as a translation regulator in Escherichia coli. Here, employing high-throughput RNA sequencing, we explored the impact of Musashi expression on the transcriptomic and translatomic profiles of E. coli, revealing certain metabolic interference, induction of post-transcriptional regulatory processes, and spurious protein-RNA interactions. Engineered Musashi protein mutants displayed compromised regulatory activity, emphasizing the importance of both RRMs for specific and sensitive RNA binding. We found that a mutation known to impede allosteric regulation led to similar translation control activity. Evolutionary experiments disclosed a loss of function of the synthetic circuit in about 40 generations, with the gene coding for the Musashi protein showing a stability comparable to other heterologous genes. Overall, this work expands our understanding of RRMs for post-transcriptional regulation in prokaryotes and highlight their potential for biotechnological and biomedical applications.

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.

A hybrid RNA-protein biosensor for high-throughput screening of adenosylcobalamin biosynthesis

Genetically encoded circuits have been successfully utilized to assess and characterize target variants with desirable traits from large mutant libraries. Adenosylcobalamin is an essential coenzyme that is required in many intracellular physiological reactions and is widely used in the pharmaceutical and food industries. High-throughput screening techniques capable of detecting adenosylcobalamin productivity and selecting superior adenosylcobalamin biosynthesis strains are critical for the creation of an effective microbial cell factory for the production of adenosylcobalamin at an industrial level. In this study, we developed an RNA-protein hybrid biosensor whose input part was an endogenous RNA riboswitch to specifically respond to adenosylcobalamin, the inverter part was an orthogonal transcriptional repressor to obtain signal inversion, and the output part was a fluorescent protein to be easily detected. The hybrid biosensor could specifically and positively correlate adenosylcobalamin concentrations to green fluorescent protein expression levels in vivo. This study also improved the operating concentration and dynamic range of the hybrid biosensor by systematic optimization. An individual cell harboring the hybrid biosensor presented over 20-fold higher fluorescence intensity than the negative control. Then, using such a biosensor combined with fluorescence-activated cell sorting, we established a high-throughput screening platform for screening adenosylcobalamin overproducers. This study demonstrates that this platform has significant potential to quickly isolate high-productive strains to meet industrial demand and that the framework is acceptable for various metabolites.

In situ targeted base editing of bacteria in the mouse gut

Microbiome research is now demonstrating a growing number of bacterial strains and genes that affect our health1. Although CRISPR-derived tools have shown great success in editing disease-driving genes in human cells2, we currently lack the tools to achieve comparable success for bacterial targets in situ. Here we engineer a phage-derived particle to deliver a base editor and modify Escherichia coli colonizing the mouse gut. Editing of a β-lactamase gene in a model E. coli strain resulted in a median editing efficiency of 93% of the target bacterial population with a single dose. Edited bacteria were stably maintained in the mouse gut for at least 42 days following treatment. This was achieved using a non-replicative DNA vector, preventing maintenance and dissemination of the payload. We then leveraged this approach to edit several genes of therapeutic relevance in E. coli and Klebsiella pneumoniae strains in vitro and demonstrate in situ editing of a gene involved in the production of curli in a pathogenic E. coli strain. Our work demonstrates the feasibility of modifying bacteria directly in the gut, offering a new avenue to investigate the function of bacterial genes and opening the door to the design of new microbiome-targeted therapies.

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.

Precise and scalable self-organization in mammalian pseudo-embryos

Gene expression is inherently noisy, posing a challenge to understanding how precise and reproducible patterns of gene expression emerge in mammals. Here we investigate this phenomenon using gastruloids, a three-dimensional in vitro model for early mammalian development. Our study reveals intrinsic reproducibility in the self-organization of gastruloids, encompassing growth dynamics and gene expression patterns. We observe a remarkable degree of control over gene expression along the main body axis, with pattern boundaries positioned with single-cell precision. Furthermore, as gastruloids grow, both their physical proportions and gene expression patterns scale proportionally with system size. Notably, these properties emerge spontaneously in self-organizing cell aggregates, distinct from many in vivo systems constrained by fixed boundary conditions. Our findings shed light on the intricacies of developmental precision, reproducibility and size scaling within a mammalian system, suggesting that these phenomena might constitute fundamental features of multicellularity.

Living Synthelectronics: A New Era for Bioelectronics Powered by Synthetic Biology

Bioelectronics, which converges biology and electronics, has attracted great attention due to their vital applications in human-machine interfaces. While traditional bioelectronic devices utilize nonliving organic and/or inorganic materials to achieve flexibility and stretchability, a biological mismatch is often encountered because human tissues are characterized not only by softness and stretchability but also by biodynamic and adaptive properties. Recently, a notable paradigm shift has emerged in bioelectronics, where living cells, and even viruses, modified via gene editing within synthetic biology, are used as core components in a new hybrid electronics paradigm. These devices are defined as "living synthelectronics," and they offer enhanced potential for interfacing with human tissues at informational and substance exchange levels. In this Perspective, the recent advances in living synthelectronics are summarized. First, opportunities brought to electronics by synthetic biology are briefly introduced. Then, strategic approaches to designing and making electronic devices using living cells/viruses as the building blocks, sensing components, or power sources are reviewed. Finally, the challenges faced by living synthelectronics are raised. It is believed that this paradigm shift will significantly contribute to the real integration of bioelectronics with human tissues.

Red-Light-Induced Genetic System for Control of Extracellular Electron Transfer

Optogenetics is a powerful tool for spatiotemporal control of gene expression. Several light-inducible gene regulators have been developed to function in bacteria, and these regulatory circuits have been ported to new host strains. Here, we developed and adapted a red-light-inducible transcription factor for Shewanella oneidensis. This regulatory circuit is based on the iLight optogenetic system, which controls gene expression using red light. A thermodynamic model and promoter engineering were used to adapt this system to achieve differential gene expression in light and dark conditions within a S. oneidensis host strain. We further improved the iLight optogenetic system by adding a repressor to invert the genetic circuit and activate gene expression under red light illumination. The inverted iLight genetic circuit was used to control extracellular electron transfer within S. oneidensis. The ability to use both red- and blue-light-induced optogenetic circuits simultaneously was also demonstrated. Our work expands the synthetic biology capabilities in S. oneidensis, which could facilitate future advances in applications with electrogenic bacteria.

Engineering is evolution: a perspective on design processes to engineer biology

Careful consideration of how we approach design is crucial to all areas of biotechnology. However, choosing or developing an effective design methodology is not always easy as biology, unlike most areas of engineering, is able to adapt and evolve. Here, we put forward that design and evolution follow a similar cyclic process and therefore all design methods, including traditional design, directed evolution, and even random trial and error, exist within an evolutionary design spectrum. This contrasts with conventional views that often place these methods at odds and provides a valuable framework for unifying engineering approaches for challenging biological design problems.

Systematic Engineering of Genistein Biosynthetic Pathway through Genetic Regulators and Combinatorial Enzyme Screening

Microbial production of genistein, an isoflavonoid primarily found in soybeans, is gaining prominence in the food industry due to its significant nutritional and health benefits. However, challenges arise in redesigning strains due to intricate regulatory nodes between cell growth and genistein production and in systematically exploring core enzymes involving genistein biosynthesis. To address this, this study devised a strategy that simultaneously and precisely rewires flux at both acetyl-CoA and malonyl-CoA nodes toward genistein synthesis. In particular, naringenin, the primary precursor of genistein, was accumulated 2.6 times more than the unoptimized strain through transcriptional repressor-based genetic regulators. Building upon this, a combination of isoflavone synthase and cytochrome P450 reductase with the remarkable conversion of naringenin to genistein was screened from enzyme homologue libraries. The integrated metabolic engineering strategy yields the highest reported production (98 mg/L of genistein) to date, providing a framework for the biosynthesis of diverse flavonoids, including genistein.

Genetic Parts and Enabling Tools for Biocircuit Design

Synthetic biology aims to engineer biological systems for customized tasks through the bottom-up assembly of fundamental building blocks, which requires high-quality libraries of reliable, modular, and standardized genetic parts. To establish sets of parts that work well together, synthetic biologists created standardized part libraries in which every component is analyzed in the same metrics and context. Here we present a state-of-the-art review of the currently available part libraries for designing biocircuits and their gene expression regulation paradigms at transcriptional, translational, and post-translational levels in Escherichia coli. We discuss the necessary facets to integrate these parts into complex devices and systems along with the current efforts to catalogue and standardize measurement data. To better display the range of available parts and to facilitate part selection in synthetic biology workflows, we established biopartsDB, a curated database of well-characterized and useful genetic part and device libraries with detailed quantitative data validated by the published literature.

EvoAI enables extreme compression and reconstruction of the protein sequence space

Designing proteins with improved functions requires a deep understanding of how sequence and function are related, a vast space that is hard to explore. The ability to efficiently compress this space by identifying functionally important features is extremely valuable. Here, we first establish a method called EvoScan to comprehensively segment and scan the high-fitness sequence space to obtain anchor points that capture its essential features, especially in high dimensions. Our approach is compatible with any biomolecular function that can be coupled to a transcriptional output. We then develop deep learning and large language models to accurately reconstruct the space from these anchors, allowing computational prediction of novel, highly fit sequences without prior homology-derived or structural information. We apply this hybrid experimental-computational method, which we call EvoAI, to a repressor protein and find that only 82 anchors are sufficient to compress the high-fitness sequence space with a compression ratio of 1048. The extreme compressibility of the space informs both applied biomolecular design and understanding of natural evolution.

Choreographing root architecture and rhizosphere interactions through synthetic biology

Climate change is driving extreme changes to the environment, posing substantial threats to global food security and bioenergy. Given the direct role of plant roots in mediating plant-environment interactions, engineering the form and function of root systems and their associated microbiota may mitigate these effects. Synthetic genetic circuits have enabled sophisticated control of gene expression in microbial systems for years and a surge of advances has heralded the extension of this approach to multicellular plant species. Targeting these tools to affect root structure, exudation, and microbe activity on root surfaces provide multiple strategies for the advancement of climate-ready crops.

Snowprint: a predictive tool for genetic biosensor discovery

Bioengineers increasingly rely on ligand-inducible transcription regulators for chemical-responsive control of gene expression, yet the number of regulators available is limited. Novel regulators can be mined from genomes, but an inadequate understanding of their DNA specificity complicates genetic design. Here we present Snowprint, a simple yet powerful bioinformatic tool for predicting regulator:operator interactions. Benchmarking results demonstrate that Snowprint predictions are significantly similar for >45% of experimentally validated regulator:operator pairs from organisms across nine phyla and for regulators that span five distinct structural families. We then use Snowprint to design promoters for 33 previously uncharacterized regulators sourced from diverse phylogenies, of which 28 are shown to influence gene expression and 24 produce a >20-fold dynamic range. A panel of the newly repurposed regulators are then screened for response to biomanufacturing-relevant compounds, yielding new sensors for a polyketide (olivetolic acid), terpene (geraniol), steroid (ursodiol), and alkaloid (tetrahydropapaverine) with induction ratios up to 10.7-fold. Snowprint represents a unique, protein-agnostic tool that greatly facilitates the discovery of ligand-inducible transcriptional regulators for bioengineering applications. A web-accessible version of Snowprint is available at https://snowprint.groov.bio .

Genetic Circuits for Feedback Control of Gamma-Aminobutyric Acid Biosynthesis in Probiotic Escherichia coli Nissle 1917

Engineered microorganisms such as the probiotic strain Escherichia coli Nissle 1917 (EcN) offer a strategy to sense and modulate the concentration of metabolites or therapeutics in the gastrointestinal tract. Here, we present an approach to regulate the production of the depression-associated metabolite gamma-aminobutyric acid (GABA) in EcN using genetic circuits that implement negative feedback. We engineered EcN to produce GABA by overexpressing glutamate decarboxylase and applied an intracellular GABA biosensor to identify growth conditions that improve GABA biosynthesis. We next employed characterized genetically encoded NOT gates to construct genetic circuits with layered feedback to control the rate of GABA biosynthesis and the concentration of GABA produced. Looking ahead, this approach may be utilized to design feedback control of microbial metabolite biosynthesis to achieve designable smart microbes that act as living therapeutics.

Customizing cellular signal processing by synthetic multi-level regulatory circuits

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

Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico

With advances in high-throughput, large-scale in vivo measurement and genome modification techniques at the single-nucleotide level, there is an increasing demand for the development of new technologies for the flexible design and control of cellular systems. Computer-aided design is a powerful tool to design new cells. Whole-cell modeling aims to integrate various cellular subsystems, determine their interactions and cooperative mechanisms, and predict comprehensive cellular behaviors by computational simulations on a genome-wide scale. It has been applied to prokaryotes, yeasts, and higher eukaryotic cells, and utilized in a wide range of applications, including production of valuable substances, drug discovery, and controlled differentiation. Whole-cell modeling, consisting of several thousand elements with diverse scales and properties, requires innovative model construction, simulation, and analysis techniques. Furthermore, whole-cell modeling has been extended to multiple scales, including high-resolution modeling at the single-nucleotide and single-amino acid levels and multicellular modeling of tissues and organs. This review presents an overview of the current state of whole-cell modeling, discusses the novel computational and experimental technologies driving it, and introduces further developments toward multihierarchical modeling on a whole-genome scale.

Computational design of sequence-specific DNA-binding proteins

Sequence-specific DNA-binding proteins (DBPs) play critical roles in biology and biotechnology, and there has been considerable interest in the engineering of DBPs with new or altered specificities for genome editing and other applications. While there has been some success in reprogramming naturally occurring DBPs using selection methods, the computational design of new DBPs that recognize arbitrary target sites remains an outstanding challenge. We describe a computational method for the design of small DBPs that recognize specific target sequences through interactions with bases in the major groove, and employ this method in conjunction with experimental screening to generate binders for 5 distinct DNA targets. These binders exhibit specificity closely matching the computational models for the target DNA sequences at as many as 6 base positions and affinities as low as 30-100 nM. The crystal structure of a designed DBP-target site complex is in close agreement with the design model, highlighting the accuracy of the design method. The designed DBPs function in both Escherichia coli and mammalian cells to repress and activate transcription of neighboring genes. Our method is a substantial step towards a general route to small and hence readily deliverable sequence-specific DBPs for gene regulation and editing.

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.

Natural Microbial Reactor-Based Sensing Platform for Highly Sensitive Detection of Inorganic Arsenic in Rice Grains

Rice is a major dietary source of inorganic arsenic (iAs), a highly toxic arsenical that accumulates in rice and poses health risks to rice-based populations. However, the availability of detection methods for iAs in rice grains is limited. In this study, we developed a novel approach utilizing a natural bacterial biosensor, Escherichia coli AW3110 (pBB-ArarsR-mCherry), in conjunction with amylase hydrolysis for efficient extraction, enabling high-throughput and quantitative detection of iAs in rice grains. The biosensor exhibits high specificity for arsenic and distinguishes between arsenite [As(III)] and arsenate [As(V)] by modulating the concentration of PO43- in the detection system. We determined the iAs concentrations in 19 rice grain samples with varying total As concentrations and compared our method with the standard technique of microwave digestion coupled with HPLC-ICP-MS. Both methods exhibited comparable results, without no significant bias in the concentrations of As(III) and As(V). The whole-cell biosensor demonstrated excellent reproducibility and a high signal-to-noise ratio, achieving a limit of detection of 16 μg kg-1 [As(III)] and 29 μg kg-1 [As(V)]. These values are considerably lower than the maximum allowable level (100 μg kg-1) for infant rice supplements established by the European Union. Our straightforward sensing strategy presents a promising tool for detecting iAs in other food samples.

Synthesis of Gamma-Butyrolactone Hormones Enables Understanding of Natural Product Induction

Bacteria produce natural products (NPs) via biosynthetic gene clusters. Unfortunately, many biosynthetic gene clusters are silent under traditional laboratory conditions. To access novel NPs, a better understanding of their regulation is needed. γ-Butyrolactones, including the A-factor and Streptomyces coelicolor butanolides, SCBs, are a major class of Streptomyces' hormones. Study of these hormones has been limited due to challenges in accessing them in stereochemically pure forms. Herein, we describe an efficient route to (R)-paraconyl alcohol, a key intermediate for these molecules, as well as a biocatalytic method to access the exocyclic hydroxyl group that differentiates A-factor-type from SCB-type hormones. Utilizing these methods, a library of hormones have been synthesized and tested in a green fluorescent protein reporter assay for their ability to relieve repression by the repressor ScbR. This allowed the most quantitative structure-activity relationship of γ-butyrolactones and a cognate repressor to date. Bioinformatics analysis strongly suggests that many other repressors of NP biosynthesis likely bind similar molecules. This efficient, diversifiable synthesis will enable further investigation of the regulation of NP biosynthesis.

Redesign of an Escherichia coli Nissle treatment for phenylketonuria using insulated genomic landing pads and genetic circuits to reduce burden

To build therapeutic strains, Escherichia coli Nissle (EcN) have been engineered to express antibiotics, toxin-degrading enzymes, immunoregulators, and anti-cancer chemotherapies. For efficacy, the recombinant genes need to be highly expressed, but this imposes a burden on the cell, and plasmids are difficult to maintain in the body. To address these problems, we have developed landing pads in the EcN genome and genetic circuits to control therapeutic gene expression. These tools were applied to EcN SYNB1618, undergoing clinical trials as a phenylketonuria treatment. The pathway for converting phenylalanine to trans-cinnamic acid was moved to a landing pad under the control of a circuit that keeps the pathway off during storage. The resulting strain (EcN SYN8784) achieved higher activity than EcN SYNB1618, reaching levels near when the pathway is carried on a plasmid. This work demonstrates a simple system for engineering EcN that aids quantitative strain design for therapeutics.

Genetic circuits for feedback control of gamma-aminobutyric acid biosynthesis in probiotic Escherichia coli Nissle 1917

Engineered microorganisms such as the probiotic strain Escherichia coli Nissle 1917 (EcN) offer a strategy to sense and modulate the concentration of metabolites or therapeutics in the gastrointestinal tract. Here, we present an approach to regulate production of the depression-associated metabolite gamma-aminobutyric acid (GABA) in EcN using genetic circuits that implement negative feedback. We engineered EcN to produce GABA by overexpressing glutamate decarboxylase (GadB) from E. coli and applied an intracellular GABA biosensor to identify growth conditions that improve GABA biosynthesis. We next employed characterized genetically-encoded NOT gates to construct genetic circuits with layered feedback to control the rate of GABA biosynthesis and the concentration of GABA produced. Looking ahead, this approach may be utilized to design feedback control of microbial metabolite biosynthesis to achieve designable smart microbes that act as living therapeutics.

Design of Four Small-Molecule-Inducible Systems in the Yeast Chromosome, Applied to Optimize Terpene Biosynthesis

The optimization of cellular functions often requires the balancing of gene expression, but the physical construction and screening of alternative designs are costly and time-consuming. Here, we construct a strain of Saccharomyces cerevisiae that contains a "sensor array" containing bacterial regulators that respond to four small-molecule inducers (vanillic acid, xylose, aTc, IPTG). Four promoters can be independently controlled with low background and a 40- to 5000-fold dynamic range. These systems can be used to study the impact of changing the level and timing of gene expression without requiring the construction of multiple strains. We apply this approach to the optimization of a four-gene heterologous pathway to the terpene linalool, which is a flavor and precursor to energetic materials. Using this approach, we identify bottlenecks in the metabolic pathway. This work can aid the rapid automated strain development of yeasts for the bio-manufacturing of diverse products, including chemicals, materials, fuels, and food ingredients.

Performance Prediction of Fundamental Transcriptional Programs

Transcriptional programming leverages systems of engineered transcription factors to impart decision-making (e.g., Boolean logic) in chassis cells. The number of components used to construct said decision-making systems is rapidly increasing, making an exhaustive experimental evaluation of iterations of biological circuits impractical. Accordingly, we posited that a predictive tool is needed to guide and accelerate the design of transcriptional programs. The work described here involves the development and experimental characterization of a large collection of network-capable single-INPUT logical operations─i.e., engineered BUFFER (repressor) and engineered NOT (antirepressor) logical operations. Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates). In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates). These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit. Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.

Genomically mined acoustic reporter genes for real-time in vivo monitoring of tumors and tumor-homing bacteria

Ultrasound allows imaging at a much greater depth than optical methods, but existing genetically encoded acoustic reporters for in vivo cellular imaging have been limited by poor sensitivity, specificity and in vivo expression. Here we describe two acoustic reporter genes (ARGs)-one for use in bacteria and one for use in mammalian cells-identified through a phylogenetic screen of candidate gas vesicle gene clusters from diverse bacteria and archaea that provide stronger ultrasound contrast, produce non-linear signals distinguishable from background tissue and have stable long-term expression. Compared to their first-generation counterparts, these improved bacterial and mammalian ARGs produce 9-fold and 38-fold stronger non-linear contrast, respectively. Using these new ARGs, we non-invasively imaged in situ tumor colonization and gene expression in tumor-homing therapeutic bacteria, tracked the progression of tumor gene expression and growth in a mouse model of breast cancer, and performed gene-expression-guided needle biopsies of a genetically mosaic tumor, demonstrating non-invasive access to dynamic biological processes at centimeter depth.

Riboswitch-guided chalcone synthase engineering and metabolic flux optimization for enhanced production of flavonoids

Flavonoids are a group of secondary metabolites from plants that have received attention as high value-added pharmacological substances. Recently, a robust and efficient bioprocess using recombinant microbes has emerged as a promising approach to supply flavonoids. In the flavonoid biosynthetic pathway, the rate of chalcone synthesis, the first committed step, is a major bottleneck. However, chalcone synthase (CHS) engineering was difficult because of high-level conservation and the absence of effective screening tools, which are limited to overexpression or homolog-based combinatorial strategies. Furthermore, it is necessary to precisely regulate the metabolic flux for the optimum availability of malonyl-CoA, a substrate of chalcone synthesis. In this study, we engineered CHS and optimized malonyl-CoA availability to establish a platform strain for naringenin production, a key molecular scaffold for various flavonoids. First, we engineered CHS through synthetic riboswitch-based high-throughput screening of rationally designed mutant libraries. Consequently, the catalytic efficiency (k_cat/K_m) of the optimized CHS enzyme was 62% higher than that of the wild-type enzyme. In addition to CHS engineering, we designed genetic circuits using transcriptional repressors to fine-tune the malonyl-CoA availability. The best mutant with synergistic effects of the engineered CHS and the optimized genetic circuit produced 98.71 mg/L naringenin (12.57 mg naringenin/g glycerol), which is the highest naringenin concentration and yield from glycerol in similar culture conditions reported to date, a 2.5-fold increase compared to the parental strain. Overall, this study provides an effective strategy for efficient production of flavonoids.

Small molecule-inducible gene regulatory systems in mammalian cells: progress and design principles

Small molecule-inducible gene circuits are some of the most important tools in biology because they provide a convenient way to exert precise regulation of biological systems. These systems typically are designed to govern gene activation, repression, or disruption at multiple levels, such as through genome modification, transcription, translation, or post-translational regulation of protein activity. Due to their importance, many new systems have been created in the past few years to address different needs or afford orthogonality. They can be broadly characterized based on the inducer used, the mode of regulation, and the effector protein enabling the regulation. Furthermore, each synthetic circuit has varying performance metrics and design considerations. Here, we provide a concise comparison of recently developed tools and recommend standardized metrics for evaluating their performance and potential as biological interrogators or therapeutics.

Inducible plasmid copy number control for synthetic biology in commonly used E. coli strains

The ability to externally control gene expression has been paradigm shifting for all areas of biological research, especially for synthetic biology. Such control typically occurs at the transcriptional and translational level, while technologies enabling control at the DNA copy level are limited by either (i) relying on a handful of plasmids with fixed and arbitrary copy numbers; or (ii) require multiple plasmids for replication control; or (iii) are restricted to specialized strains. To overcome these limitations, we present TULIP (TUnable Ligand Inducible Plasmid): a self-contained plasmid with inducible copy number control, designed for portability across various Escherichia coli strains commonly used for cloning, protein expression, and metabolic engineering. Using TULIP, we demonstrate through multiple application examples that flexible plasmid copy number control accelerates the design and optimization of gene circuits, enables efficient probing of metabolic burden, and facilitates the prototyping and recycling of modules in different genetic contexts.

Light-Dependent Control of Bacterial Expression at the mRNA Level

Sensory photoreceptors mediate numerous light-dependent adaptations across organisms. In optogenetics, photoreceptors achieve the reversible, non-invasive, and spatiotemporally precise control by light of gene expression and other cellular processes. The light-oxygen-voltage receptor PAL binds to small RNA aptamers with sequence specificity upon blue-light illumination. By embedding the responsive aptamer in the ribosome-binding sequence of genes of interest, their expression can be downregulated by light. We developed the pCrepusculo and pAurora optogenetic systems that are based on PAL and allow to down- and upregulate, respectively, bacterial gene expression using blue light. Both systems are realized as compact, single plasmids that exhibit stringent blue-light responses with low basal activity and up to several 10-fold dynamic range. As PAL exerts light-dependent control at the RNA level, it can be combined with other optogenetic circuits that control transcription initiation. By integrating regulatory mechanisms operating at the DNA and mRNA levels, optogenetic circuits with emergent properties can thus be devised. As a case in point, the pEnumbra setup permits to upregulate gene expression under moderate blue light whereas strong blue light shuts off expression again. Beyond providing novel signal-responsive expression systems for diverse applications in biotechnology and synthetic biology, our work also illustrates how the light-dependent PAL-aptamer interaction can be harnessed for the control and interrogation of RNA-based processes.

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.

High-yield ectoine production in engineered Corynebacterium glutamicum by fine metabolic regulation via plug-in repressor library

Ectoine is a high-value protective and stabilizing agent with different applications in biopharmaceuticals, biotechnology, and fine chemicals. Here, efficient production of ectoine in Corynebacterium glutamicum was achieved by combination of metabolic engineering and plug-in repressor library strategy. First, the ectBAC cluster from Pseudomonas stutzeri was introduced into strain K02, and the titer of the obtained strain was 2.12 g/L. Metabolic engineering was then performed for further optimization, including removal of competing pathways (pck and ldh knockout), deletion of glycolysis repressor (sugR knockout), and enhancement of precursor supply (overexpression of Ecasd and CglysC^S301Y). Next, two repressor libraries were designed for targeted flux control to improve ectoine production. Finally, strain CB5L6 produced 45.52 g/L ectoine and had the highest yield in C. glutamicum. For the first time, plug-in repressor library was employed to engineer C. glutamicum for metabolites production, which will provide a guideline for the construction of microbial cell factories.

Yeast transcriptional device libraries enable precise synthesis of value-added chemicals from methanol

Natural methylotrophs are attractive methanol utilization hosts, but lack flexible expression tools. In this study, we developed yeast transcriptional device libraries for precise synthesis of value-added chemicals from methanol. We synthesized transcriptional devices by fusing bacterial DNA-binding proteins (DBPs) with yeast transactivation domains, and linking bacterial binding sequences (BSs) with the yeast core promoter. Three DBP–BS pairs showed good activity when working with transactivation domains and the core promoter of P_AOX1 in the methylotrophic yeast, Pichia pastoris. Fine-tuning of the tandem BSs, spacers and differentiated input promoters further enabled a constitutive transcriptional device library (cTRDL) composed of 126 transcriptional devices with an expression strength of 16–520% and an inducible TRDL (iTRDL) composed of 162 methanol-inducible transcriptional devices with an expression strength of 30–500%, compared with P_AOX1. Selected devices from iTRDL were adapted to the dihydromonacolin L biosynthetic pathway by orthogonal experimental design, reaching 5.5-fold the production from the P_AOX1-driven pathway. The full factorial design of the selected devices from the cTRDL was adapted to the downstream pathway of dihydromonacolin L to monacolin J. Monacolin J production from methanol reached 3.0-fold the production from the P_AOX1-driven pathway. Our engineered toolsets ensured multilevel pathway control of chemical synthesis in methylotrophic yeasts.

Using fungible biosensors to evolve improved alkaloid biosyntheses

A key bottleneck in the microbial production of therapeutic plant metabolites is identifying enzymes that can improve yield. The facile identification of genetically encoded biosensors can overcome this limitation and become part of a general method for engineering scaled production. We have developed a combined screening and selection approach that quickly refines the affinities and specificities of generalist transcription factors; using RamR as a starting point, we evolve highly specific (>100-fold preference) and sensitive (half-maximum effective concentration (EC50) < 30 μM) biosensors for the alkaloids tetrahydropapaverine, papaverine, glaucine, rotundine and noscapine. High-resolution structures reveal multiple evolutionary avenues for the malleable effector-binding site and the creation of new pockets for different chemical moieties. These sensors further enabled the evolution of a streamlined pathway for tetrahydropapaverine, a precursor to four modern pharmaceuticals, collapsing multiple methylation steps into a single evolved enzyme. Our methods for evolving biosensors enable the rapid engineering of pathways for therapeutic alkaloids.

Genetic Circuit Design in Rhizobacteria

Genetically engineered plants hold enormous promise for tackling global food security and agricultural sustainability challenges. However, construction of plant-based genetic circuitry is constrained by a lack of well-characterized genetic parts and circuit design rules. In contrast, advances in bacterial synthetic biology have yielded a wealth of sensors, actuators, and other tools that can be used to build bacterial circuitry. As root-colonizing bacteria (rhizobacteria) exert substantial influence over plant health and growth, genetic circuit design in these microorganisms can be used to indirectly engineer plants and accelerate the design-build-test-learn cycle. Here, we outline genetic parts and best practices for designing rhizobacterial circuits, with an emphasis on sensors, actuators, and chassis species that can be used to monitor/control rhizosphere and plant processes.

Synthetic genetic circuits as a means of reprogramming plant roots

The shape of a plant's root system influences its ability to reach essential nutrients in the soil and to acquire water during drought. Progress in engineering plant roots to optimize water and nutrient acquisition has been limited by our capacity to design and build genetic programs that alter root growth in a predictable manner. We developed a collection of synthetic transcriptional regulators for plants that can be compiled to create genetic circuits. These circuits control gene expression by performing Boolean logic operations and can be used to predictably alter root structure. This work demonstrates the potential of synthetic genetic circuits to control gene expression across tissues and reprogram plant growth.