Synthetic analog computation in living cells

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.

SemiSynBio: A new era for neuromorphic computing

Neuromorphic computing has the potential to achieve the requirements of the next-generation artificial intelligence (AI) systems, due to its advantages of adaptive learning and parallel computing. Meanwhile, biocomputing has seen ongoing development with the rise of synthetic biology, becoming the driving force for new generation semiconductor synthetic biology (SemiSynBio) technologies. DNA-based biomolecules could potentially perform the functions of Boolean operators as logic gates and be used to construct artificial neural networks (ANNs), providing the possibility of executing neuromorphic computing at the molecular level. Herein, we briefly outline the principles of neuromorphic computing, describe the advances in DNA computing with a focus on synthetic neuromorphic computing, and summarize the major challenges and prospects for synthetic neuromorphic computing. We believe that constructing such synthetic neuromorphic circuits will be an important step toward realizing neuromorphic computing, which would be of widespread use in biocomputing, DNA storage, information security, and national defense.

Chemical reservoir computation in a self-organizing reaction network

Chemical reaction networks, such as those found in metabolism and signalling pathways, enable cells to process information from their environment1,2. Current approaches to molecular information processing and computation typically pursue digital computation models and require extensive molecular-level engineering3. Despite considerable advances, these approaches have not reached the level of information processing capabilities seen in living systems. Here we report on the discovery and implementation of a chemical reservoir computer based on the formose reaction4. We demonstrate how this complex, self-organizing chemical reaction network can perform several nonlinear classification tasks in parallel, predict the dynamics of other complex systems and achieve time-series forecasting. This in chemico information processing system provides proof of principle for the emergent computational capabilities of complex chemical reaction networks, paving the way for a new class of biomimetic information processing systems.

Coupling Exponential to Linear Amplification for Endpoint Quantitative Analysis

Exponential DNA amplification techniques are fundamental in ultrasensitive molecular diagnostics. These systems offer a wide dynamic range, but the quantification requires real-time monitoring of the amplification reaction. Linear amplification schemes, despite their limited sensitivity, can achieve quantitative measurement from a single end-point readout, suitable for low-cost, point-of-care, or massive testing. Reconciling the sensitivity of exponential amplification with the simplicity of end-point readout would thus break through a major design dilemma and open a route to a new generation of massively scalable quantitative bioassays. Here a hybrid nucleic acid-based circuit design is introduced to compute a logarithmic function, therefore providing a wide dynamic range based on a single end-point measurement. CELIA (Coupling Exponential amplification reaction to LInear Amplification) exploits a versatile biochemical circuit architecture to couple a tunable linear amplification stage - optionally embedding an inverter function - downstream of an exponential module in a one-pot format. Applied to the detection of microRNAs, CELIA provides a limit of detection in the femtomolar range and a dynamic range of six decades. This isothermal approach bypasses thermocyclers without compromising sensitivity, thereby opening the way to applications in various diagnostic assays, and providing a simplified, cost-efficient, and high throughput solution for quantitative nucleic acid analysis.

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.

A domain swapping strategy to create modular transcriptional regulators for novel topology in genetic network

Transcriptional regulators generate connections between biological signals and genetic outputs. They are used robustly for sensing input signals in building genetic circuits. However, each regulator can only generate a fixed connection, which generates constraints in linking multiple signals for more complex processes. Recent studies discovered that a domain swapping strategy can be applied to various regulator families to create modular regulators for new signal-output connections, significantly broadening possibilities in circuit design. Here we review the development of this emerging strategy, the use of resulting modular regulators for creating novel genetic response behaviors, and current limitations and solutions for further advancing the design of modular regulators.

Design of a Biohybrid Materials Circuit with Binary Decoder Functionality

Synthetic biology applies concepts from electrical engineering and information processing to endow cells with computational functionality. Transferring the underlying molecular components into materials and wiring them according to topologies inspired by electronic circuit boards has yielded materials systems that perform selected computational operations. However, the limited functionality of available building blocks is restricting the implementation of advanced information-processing circuits into materials. Here, a set of protease-based biohybrid modules the bioactivity of which can either be induced or inhibited is engineered. Guided by a quantitative mathematical model and following a design-build-test-learn (DBTL) cycle, the modules are wired according to circuit topologies inspired by electronic signal decoders, a fundamental motif in information processing. A 2-input/4-output binary decoder for the detection of two small molecules in a material framework that can perform regulated outputs in form of distinct protease activities is designed. The here demonstrated smart material system is strongly modular and can be used for biomolecular information processing for example in advanced biosensing or drug delivery applications.

A hybrid transistor with transcriptionally controlled computation and plasticity

Organic electrochemical transistors (OECTs) are ideal devices for translating biological signals into electrical readouts and have applications in bioelectronics, biosensing, and neuromorphic computing. Despite their potential, developing programmable and modular methods for living systems to interface with OECTs has proven challenging. Here we describe hybrid OECTs containing the model electroactive bacterium Shewanella oneidensis that enable the transduction of biological computations to electrical responses. Specifically, we fabricated planar p-type OECTs and demonstrated that channel de-doping is driven by extracellular electron transfer (EET) from S. oneidensis. Leveraging this mechanistic understanding and our ability to control EET flux via transcriptional regulation, we used plasmid-based Boolean logic gates to translate biological computation into current changes within the OECT. Finally, we demonstrated EET-driven changes to OECT synaptic plasticity. This work enables fundamental EET studies and OECT-based biosensing and biocomputing systems with genetically controllable and modular design elements.

Synthetic Homoserine Lactone Sensors for Gram-Positive Bacillus subtilis Using LuxR-Type Regulators

A universal biochemical signal for bacterial cell-cell communication could facilitate programming dynamic responses in diverse bacterial consortia. However, the classical quorum sensing paradigm is that Gram-negative and Gram-positive bacteria generally communicate via homoserine lactones (HSLs) or oligopeptide molecular signals, respectively, to elicit population responses. Here, we create synthetic HSL sensors for Gram-positive Bacillus subtilis 168 using allosteric LuxR-type regulators (RpaR, LuxR, RhlR, and CinR) and synthetic promoters. Promoters were combinatorially designed from different sequence elements (-35, -16, -10, and transcriptional start regions). We quantified the effects of these combinatorial promoters on sensor activity and determined how regulator expression affects its activation, achieving up to 293-fold activation. Using the statistical design of experiments, we identified significant effects of promoter regions and pairwise interactions on sensor activity, which helped to understand the sequence-function relationships for synthetic promoter design. We present the first known set of functional HSL sensors (≥20-fold dynamic range) in B. subtilis for four different HSL chemical signals: p-coumaroyl-HSL, 3-oxohexanoyl-HSL, n-butyryl-HSL, and n-(3-hydroxytetradecanoyl)-HSL. This set of synthetic HSL sensors for a Gram-positive bacterium can pave the way for designable interspecies communication within microbial consortia.

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.

Computation harvesting from nature dynamics for predicting wind speed and direction

Natural phenomena generate complex dynamics because of nonlinear interactions among their components. The dynamics can be exploited as a kind of computational resource. For example, in the framework of natural computation, various natural phenomena such as quantum mechanics and cellular dynamics are used to realize general purpose calculations or logical operations. In recent years, simple collection of such nature dynamics has become possible in a sensor-rich society. For example, images of plant movement that have been captured indirectly by a surveillance camera can be regarded as sensor outputs reflecting the state of the wind striking the plant. Herein, based on ideas of physical reservoir computing, we present a methodology for wind speed and direction estimation from naturally occurring sensors in movies. Then we demonstrate its effectiveness through experimentation. Specifically using the proposed methodology, we investigate the computational capability of the nature dynamics, revealing its high robustness and generalization performance for computation.

Israel and the global synthetic biology ecosystem

The field of synthetic biology emerged a few decades ago, following some key works of researchers in the USA, Europe, and the Far East. It reached Israel through academia and a few years later it finally got the attention of industry, venture capitals, and government authorities, especially the Israeli Innovation Authority, hoping to encourage entrepreneurs to establish startups in this field. Here we provide an overview of the activity of the field of synthetic biology in Israel, including historical notes, current strategy, prospects and developments, and further insight that are relevant to any stakeholders in the synthetic biology field.

A Hybrid Transistor with Transcriptionally Controlled Computation and Plasticity

Organic electrochemical transistors (OECTs) are ideal devices for translating biological signals into electrical readouts and have applications in bioelectronics, biosensing, and neuromorphic computing. Despite their potential, developing programmable and modular methods for living systems to interface with OECTs has proven challenging. Here we describe hybrid OECTs containing the model electroactive bacterium Shewanella oneidensis that enable the transduction of biological computations to electrical responses. Specifically, we fabricated planar p-type OECTs and demonstrated that channel de-doping is driven by extracellular electron transfer (EET) from S. oneidensis. Leveraging this mechanistic understanding and our ability to control EET flux via transcriptional regulation, we used plasmid-based Boolean logic gates to translate biological computation into current changes within the OECT. Finally, we demonstrated EET-driven changes to OECT synaptic plasticity. This work enables fundamental EET studies and OECT-based biosensing and biocomputing systems with genetically controllable and modular design elements.

Multi-input Drug-Controlled Switches of Mammalian Gene Expression Based on Engineered Nuclear Hormone Receptors

Protein-based switches that respond to different inputs to regulate cellular outputs, such as gene expression, are central to synthetic biology. For increased controllability, multi-input switches that integrate several cooperating and competing signals for the regulation of a shared output are of particular interest. The nuclear hormone receptor (NHR) superfamily offers promising starting points for engineering multi-input-controlled responses to clinically approved drugs. Starting from the VgEcR/RXR pair, we demonstrate that novel (multi)drug regulation can be achieved by exchange of the ecdysone receptor (EcR) ligand binding domain (LBD) for other human NHR-derived LBDs. For responses activated to saturation by an agonist for the first LBD, we show that outputs can be boosted by an agonist targeting the second LBD. In combination with an antagonist, output levels are tunable by up to three simultaneously present small-molecule drugs. Such high-level control validates NHRs as a versatile, engineerable platform for programming multidrug-controlled responses.

Homo cerevisiae-Leveraging Yeast for Investigating Protein-Protein Interactions and Their Role in Human Disease

Understanding how genetic variation affects phenotypes represents a major challenge, particularly in the context of human disease. Although numerous disease-associated genes have been identified, the clinical significance of most human variants remains unknown. Despite unparalleled advances in genomics, functional assays often lack sufficient throughput, hindering efficient variant functionalization. There is a critical need for the development of more potent, high-throughput methods for characterizing human genetic variants. Here, we review how yeast helps tackle this challenge, both as a valuable model organism and as an experimental tool for investigating the molecular basis of phenotypic perturbation upon genetic variation. In systems biology, yeast has played a pivotal role as a highly scalable platform which has allowed us to gain extensive genetic and molecular knowledge, including the construction of comprehensive interactome maps at the proteome scale for various organisms. By leveraging interactome networks, one can view biology from a systems perspective, unravel the molecular mechanisms underlying genetic diseases, and identify therapeutic targets. The use of yeast to assess the molecular impacts of genetic variants, including those associated with viral interactions, cancer, and rare and complex diseases, has the potential to bridge the gap between genotype and phenotype, opening the door for precision medicine approaches and therapeutic development.

A Theoretical Framework for Implementable Nucleic Acids Feedback Systems

Chemical reaction networks can be utilised as basic components for nucleic acid feedback control systems' design for Synthetic Biology application. DNA hybridisation and programmed strand-displacement reactions are effective primitives for implementation. However, the experimental validation and scale-up of nucleic acid control systems are still considerably falling behind their theoretical designs. To aid with the progress heading into experimental implementations, we provide here chemical reaction networks that represent two fundamental classes of linear controllers: integral and static negative state feedback. We reduced the complexity of the networks by finding designs with fewer reactions and chemical species, to take account of the limits of current experimental capabilities and mitigate issues pertaining to crosstalk and leakage, along with toehold sequence design. The supplied control circuits are quintessential candidates for the first experimental validations of nucleic acid controllers, since they have a number of parameters, species, and reactions small enough for viable experimentation with current technical capabilities, but still represent challenging feedback control systems. They are also well suited to further theoretical analysis to verify results on the stability, performance, and robustness of this important new class of control systems.

Drug Cocktail Formulation via Circuit Design

Electronic circuits intuitively visualize and quantitatively simulate biological systems with nonlinear differential equations that exhibit complicated dynamics. Drug cocktail therapies are a powerful tool against diseases that exhibit such dynamics. We show that just six key states, which are represented in a feedback circuit, enable drug-cocktail formulation: 1) healthy cell number; 2) infected cell number; 3) extracellular pathogen number; 4) intracellular pathogenic molecule number; 5) innate immune system strength; and 6) adaptive immune system strength. To enable drug cocktail formulation, the model represents the effects of the drugs in the circuit. For example, a nonlinear feedback circuit model fits measured clinical data, represents cytokine storm and adaptive autoimmune behavior, and accounts for age, sex, and variant effects for SARS-CoV-2 with few free parameters. The latter circuit model provided three quantitative insights on the optimal timing and dosage of drug components in a cocktail: 1) antipathogenic drugs should be given early in the infection, but immunosuppressant timing involves a tradeoff between controlling pathogen load and mitigating inflammation; 2) both within and across-class combinations of drugs have synergistic effects; 3) if they are administered sufficiently early in the infection, anti-pathogenic drugs are more effective at mitigating autoimmune behavior than immunosuppressant drugs.

Systems Biology Approaches for the Improvement of Oncolytic Virus-Based Immunotherapies

Oncolytic virus (OV)-based immunotherapy is mainly dependent on establishing an efficient cell-mediated antitumor immunity. OV-mediated antitumor immunity elicits a renewed antitumor reactivity, stimulating a T-cell response against tumor-associated antigens (TAAs) and recruiting natural killer cells within the tumor microenvironment (TME). Despite the fact that OVs are unspecific cancer vaccine platforms, to further enhance antitumor immunity, it is crucial to identify the potentially immunogenic T-cell restricted TAAs, the main key orchestrators in evoking a specific and durable cytotoxic T-cell response. Today, innovative approaches derived from systems biology are exploited to improve target discovery in several types of cancer and to identify the MHC-I and II restricted peptide repertoire recognized by T-cells. Using specific computation pipelines, it is possible to select the best tumor peptide candidates that can be efficiently vectorized and delivered by numerous OV-based platforms, in order to reinforce anticancer immune responses. Beyond the identification of TAAs, system biology can also support the engineering of OVs with improved oncotropism to reduce toxicity and maintain a sufficient portion of the wild-type virus virulence. Finally, these technologies can also pave the way towards a more rational design of armed OVs where a transgene of interest can be delivered to TME to develop an intratumoral gene therapy to enhance specific immune stimuli.

Multi-input drug-controlled switches of mammalian gene expression based on engineered nuclear hormone receptors

Protein-based switches that respond to different inputs to regulate cellular outputs, such as gene expression, are central to synthetic biology. For increased controllability, multi-input switches that integrate several cooperating and competing signals for the regulation of a shared output are of particular interest. The nuclear hormone receptor (NHR) superfamily offers promising starting points for engineering multi-input-controlled responses to clinically approved drugs. Starting from the VgEcR/RXR pair, we demonstrate that novel (multi-)drug regulation can be achieved by exchange of the ecdysone receptor (EcR) ligand binding domain (LBD) for other human NHR-derived LBDs. For responses activated to saturation by an agonist for the first LBD, we show that outputs can be boosted by an agonist targeting the second LBD. In combination with an antagonist, output levels are tunable by up to three simultaneously present small-molecule drugs. Such high-level control validates NHRs as a versatile, engineerable platform for programming multi-drug-controlled responses.

The Topological Characteristics of Biological Ratio-Sensing Networks

Ratio sensing is a fundamental biological function observed in signal transduction and decision making. In the synthetic biology context, ratio sensing presents one of the elementary functions for cellular multi-signal computation. To investigate the mechanism of the ratio-sensing behavior, we explored the topological characteristics of biological ratio-sensing networks. With exhaustive enumeration of three-node enzymatic and transcriptional regulatory networks, we found that robust ratio sensing was highly dependent on network structure rather than network complexity. Specifically, a set of seven minimal core topological structures and four motifs were deduced to be capable of robust ratio sensing. Further investigations on the evolutionary space of robust ratio-sensing networks revealed highly clustered domains surrounding the core motifs which suggested their evolutionary plausibility. Our study revealed the network topological design principles of ratio-sensing behavior and provided a design scheme for constructing regulatory circuits with ratio-sensing behavior in synthetic biology.

Self-organized computation in the far-from-equilibrium cell

Recent progress in our understanding of the physics of self-organization in active matter has pointed to the possibility of spontaneous collective behaviors that effectively compute things about the patterns in the surrounding patterned environment. Here, we describe this progress and speculate about its implications for our understanding of the internal organization of the living cell.

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.

A computational design of a programmable biological processor

Basic synthetic information processing structures, such as logic gates, oscillators and flip-flops, have already been implemented in living organisms. Current implementations of these structures have yet to be extended to more complex processing structures that would constitute a biological computer. We make a step forward towards the construction of a biological computer. We describe a model-based computational design of a biological processor that uses transcription and translation resources of the host cell to perform its operations. The proposed processor is composed of an instruction memory containing a biological program, a program counter that is used to address this memory, and a biological oscillator that triggers the execution of the next instruction in the memory. We additionally describe the implementation of a biological compiler that compiles a sequence of human-readable instructions into ordinary differential equation-based models, which can be used to simulate and analyse the dynamics of the processor. The proposed implementation presents the first programmable biological processor that exploits cellular resources to execute the specified instructions. We demonstrate the application of the described processor on a set of simple yet scalable biological programs. Biological descriptions of these programs can be produced manually or automatically using the provided compiler.

Hierarchical Assembly of Flexible Biopolymer Polyphosphate-Manganese into Nanosheets

Polyphosphate (polyP) is one of the most compact inorganic polyanionic biopolymers that participates in various physiological processes. However, the development of polyP-based nanomaterials is still in its infancy. Here, biocompatible polyphosphate-manganese nanosheets are designed and synthesized by a hierarchical assembly strategy. The thickness and the lateral size of the resulting polyP-Mn nanosheets (PMNSs) are 5 nm and 120-130 nm, respectively. Molecular dynamics simulations suggested that the polyP-hexadecyl trimethyl ammonium bromide flat structure possesses a strong aggregating capacity and serves as the template for the 2D assembly of polyP-Mn. The PMNSs can activate the inflammatory response of macrophages resulting in the recovery of innate immunological functions to inhibit tumor proliferation. This work has initiated a new direction in constructing layered polyP-based nanomaterials and provides guidance for biocompatible and biodegradable biopolymer-based materials in the regulation of innate responses.

Rapid modeling of experimental molecular kinetics with simple electronic circuits instead of with complex differential equations

Kinetic modeling has relied on using a tedious number of mathematical equations to describe molecular kinetics in interacting reactions. The long list of differential equations with associated abstract variables and parameters inevitably hinders readers’ easy understanding of the models. However, the mathematical equations describing the kinetics of biochemical reactions can be exactly mapped to the dynamics of voltages and currents in simple electronic circuits wherein voltages represent molecular concentrations and currents represent molecular fluxes. For example, we theoretically derive and experimentally verify accurate circuit models for Michaelis-Menten kinetics. Then, we show that such circuit models can be scaled via simple wiring among circuit motifs to represent more and arbitrarily complex reactions. Hence, we can directly map reaction networks to equivalent circuit schematics in a rapid, quantitatively accurate, and intuitive fashion without needing mathematical equations. We verify experimentally that these circuit models are quantitatively accurate. Examples include 1) different mechanisms of competitive, noncompetitive, uncompetitive, and mixed enzyme inhibition, important for understanding pharmacokinetics; 2) product-feedback inhibition, common in biochemistry; 3) reversible reactions; 4) multi-substrate enzymatic reactions, both important in many metabolic pathways; and 5) translation and transcription dynamics in a cell-free system, which brings insight into the functioning of all gene-protein networks. We envision that circuit modeling and simulation could become a powerful scientific communication language and tool for quantitative studies of kinetics in biology and related fields.

Synthetic neuromorphic computing in living cells

Computational properties of neuronal networks have been applied to computing systems using simplified models comprising repeated connected nodes, e.g., perceptrons, with decision-making capabilities and flexible weighted links. Analogously to their revolutionary impact on computing, neuro-inspired models can transform synthetic gene circuit design in a manner that is reliable, efficient in resource utilization, and readily reconfigurable for different tasks. To this end, we introduce the perceptgene, a perceptron that computes in the logarithmic domain, which enables efficient implementation of artificial neural networks in Escherichia coli cells. We successfully modify perceptgene parameters to create devices that encode a minimum, maximum, and average of analog inputs. With these devices, we create multi-layer perceptgene circuits that compute a soft majority function, perform an analog-to-digital conversion, and implement a ternary switch. We also create a programmable perceptgene circuit whose computation can be modified from OR to AND logic using small molecule induction. Finally, we show that our approach enables circuit optimization via artificial intelligence algorithms.

Explaining pre-emptive acclimation by linking information to plant phenotype

We review mechanisms for pre-emptive acclimation in plants and propose a conceptual model linking developmental and evolutionary ecology with the acquisition of information through sensing of cues and signals. The idea is that plants acquire much of the information in the environment not from individual cues and signals but instead from their joint multivariate properties such as correlations. If molecular signalling has evolved to extract such information, the joint multivariate properties of the environment must be encoded in the genome, epigenome, and phenome. We contend that multivariate complexity explains why extrapolating from experiments done in artificial contexts into natural or agricultural systems almost never works for characters under complex environmental regulation: biased relationships among the state variables in both time and space create a mismatch between the evolutionary history reflected in the genotype and the artificial growing conditions in which the phenotype is expressed. Our model can generate testable hypotheses bridging levels of organization. We describe the model and its theoretical bases, and discuss its implications. We illustrate the hypotheses that can be derived from the model in two cases of pre-emptive acclimation based on correlations in the environment: the shade avoidance response and acclimation to drought.

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.

Nematic bits and universal logic gates

Liquid crystals (LCs) can host robust topological defect structures that essentially determine their optical and elastic properties. Although recent experimental progress enables precise control over nematic LC defects, their practical potential for information storage and processing has yet to be explored. Here, we introduce the concept of nematic bits (nbits) by exploiting a quaternionic mapping from LC defects to the Poincaré-Bloch sphere. Through theory and simulations, we demonstrate how single-nbit operations can be implemented using electric fields, to construct LC analogs of Pauli, Hadamard, and other elementary logic gates. Using nematoelastic interactions, we show how four-nbit configurations can realize universal classical NOR and NAND gates. Last, we demonstrate the implementation of generalized logical functions that take values on the Poincaré-Bloch sphere. These results open a route toward the implementation of classical digital and nonclassical continuous computation strategies in topological soft matter systems.

Bacterial degrons in synthetic circuits

Bacterial proteases are a promising post-translational regulation strategy in synthetic circuits because they recognize specific amino acid degradation tags (degrons) that can be fine-tuned to modulate the degradation levels of tagged proteins. For this reason, recent efforts have been made in the search for new degrons. Here we review the up-to-date applications of degradation tags for circuit engineering in bacteria. In particular, we pay special attention to the effects of degradation bottlenecks in synthetic oscillators and introduce mathematical approaches to study queueing that enable the quantitative modelling of proteolytic queues.

Low-cost anti-mycobacterial drug discovery using engineered E. coli

Whole-cell screening for Mycobacterium tuberculosis (Mtb) inhibitors is complicated by the pathogen's slow growth and biocontainment requirements. Here we present a synthetic biology framework for assaying Mtb drug targets in engineered E. coli. We construct Target Essential Surrogate E. coli (TESEC) in which an essential metabolic enzyme is deleted and replaced with an Mtb-derived functional analog, linking bacterial growth to the activity of the target enzyme. High throughput screening of a TESEC model for Mtb alanine racemase (Alr) revealed benazepril as a targeted inhibitor, a result validated in whole-cell Mtb. In vitro biochemical assays indicated a noncompetitive mechanism unlike that of clinical Alr inhibitors. We establish the scalability of TESEC for drug discovery by characterizing TESEC strains for four additional targets.

Reducing metabolic burden in the PACEmid evolver system by remastering high-copy phagemid vectors

Orthogonal or non-cross-reacting transcription factors are used in synthetic biology as components of genetic circuits. Brödel et al. (2016) engineered 12 such cIλ transcription factor variants using a directed evolution 'PACEmid' system. The variants operate as dual activator/repressors and expand gene circuit construction possibilities. However, the high-copy phagemid vectors carrying the cIλ variants imposed high metabolic burden upon cells. Here, the authors 'remaster' the phagemid backbones to relieve their burden substantially, exhibited by a recovery in Escherichia coli growth. The remastered phagemids' ability to function within the PACEmid evolver system is maintained, as is the cIλ transcription factors' activity within these vectors. The low-burden phagemid versions are more suitable for use in PACEmid experiments and synthetic gene circuits; the authors have, therefore, replaced the original high-burden phagemids on the Addgene repository. The authors' work emphasises the importance of understanding metabolic burden and incorporating it into design steps in future synthetic biology ventures.

Synthetic nonlinear computation for genetic circuit design

Computation frameworks have been studied in synthetic biology to achieve biosignals integration and processing, for biosensing and therapeutics applications. Biological systems exhibit nonlinearity across scales from the molecular level, to biochemical network and intercellular systems. At the molecular level, cooperative bindings contribute to nonlinear molecular signal processing in a way similar to weight variables. At the intracellular network level, feedback and feedforward regulations result in cell behaviors such as multistability and adaptation. When biochemical networks are distributed in different cell groups, intercelluar networks can generate population dynamics. Here, we review works that highlight nonlinear computations in synthetic biology. We group the works according to the scale of implementations, from the cis-transcription level, to biochemical circuit level and cellular networks.

From Analog to Digital Computing: Is Homo sapiens’ Brain on Its Way to Become a Turing Machine?

The abstract basis of modern computation is the formal description of a finite state machine, the Universal Turing Machine, based on manipulation of integers and logic symbols. In this contribution to the discourse on the computer-brain analogy, we discuss the extent to which analog computing, as performed by the mammalian brain, is like and unlike the digital computing of Universal Turing Machines. We begin with ordinary reality being a permanent dialog between continuous and discontinuous worlds. So it is with computing, which can be analog or digital, and is often mixed. The theory behind computers is essentially digital, but efficient simulations of phenomena can be performed by analog devices; indeed, any physical calculation requires implementation in the physical world and is therefore analog to some extent, despite being based on abstract logic and arithmetic. The mammalian brain, comprised of neuronal networks, functions as an analog device and has given rise to artificial neural networks that are implemented as digital algorithms but function as analog models would. Analog constructs compute with the implementation of a variety of feedback and feedforward loops. In contrast, digital algorithms allow the implementation of recursive processes that enable them to generate unparalleled emergent properties. We briefly illustrate how the cortical organization of neurons can integrate signals and make predictions analogically. While we conclude that brains are not digital computers, we speculate on the recent implementation of human writing in the brain as a possible digital path that slowly evolves the brain into a genuine (slow) Turing machine.

DNA Input Classification by a Riboregulator-Based Cell-Free Perceptron

The ability to recognize molecular patterns is essential for the continued survival of biological organisms, allowing them to sense and respond to their immediate environment. The design of synthetic gene-based classifiers has been explored previously; however, prior strategies have focused primarily on DNA strand-displacement reactions. Here, we present a synthetic in vitro transcription and translation (TXTL)-based perceptron consisting of a weighted sum operation (WSO) coupled to a downstream thresholding function. We demonstrate the application of toehold switch riboregulators to construct a TXTL-based WSO circuit that converts DNA inputs into a GFP output, the concentration of which correlates to the input pattern and the corresponding weights. We exploit the modular nature of the WSO circuit by changing the output protein to the Escherichia coli σ28-factor, facilitating the coupling of the WSO output to a downstream reporter network. The subsequent introduction of a σ28 inhibitor enabled thresholding of the WSO output such that the expression of the downstream reporter protein occurs only when the produced σ28 exceeds this threshold. In this manner, we demonstrate a genetically implemented perceptron capable of binary classification, i.e., the expression of a single output protein only when the desired minimum number of inputs is exceeded.

Thermal Input/Concentration Output Systems Processed by Chemical Reactions of Helicene Oligomers

This article describes thermal input/concentration output systems processed by chemical reactions. Various sophisticated thermal inputs can be converted into concentration outputs through the double-helix formation of helicene oligomers exhibiting thermal hysteresis. The inputs include high or low temperature, cooling or heating state, slow or fast cooling state, heating state, and cooling history. The chemical basis for the properties of the chemical reactions includes the reversibility out of chemical equilibrium, sigmoidal relationship and kinetics, bistability involving metastable states, positive feedback by self-catalytic chemical reactions, competitive chemical reactions, and fine tunability for parallel processing. The interfacing of concentration outputs in other systems is considered, and biological cells are considered to have been utilizing such input/output systems processed by chemical reactions.

Futuristic CRISPR-based biosensing in the cloud and internet of things era: an overview

Biosensors-based devices are transforming medical diagnosis of diseases and monitoring of patient signals. The development of smart and automated molecular diagnostic tools equipped with biomedical big data analysis, cloud computing and medical artificial intelligence can be an ideal approach for the detection and monitoring of diseases, precise therapy, and storage of data over the cloud for supportive decisions. This review focused on the use of machine learning approaches for the development of futuristic CRISPR-biosensors based on microchips and the use of Internet of Things for wireless transmission of signals over the cloud for support decision making. The present review also discussed the discovery of CRISPR, its usage as a gene editing tool, and the CRISPR-based biosensors with high sensitivity of Attomolar (10^-18 M), Femtomolar (10^-15 M) and Picomolar (10^-12 M) in comparison to conventional biosensors with sensitivity of nanomolar 10^-9 M and micromolar 10^-3 M. Additionally, the review also outlines limitations and open research issues in the current state of CRISPR-based biosensing applications.

A synthetic distributed genetic multi-bit counter

A design for genetically encoded counters is proposed via repressor-based circuits. An N-bit counter reads sequences of input pulses and displays the total number of pulses, modulo 2N. The design is based on distributed computation with specialized cell types allocated to specific tasks. This allows scalability and bypasses constraints on the maximal number of circuit genes per cell due to toxicity or failures due to resource limitations. The design starts with a single-bit counter. The N-bit counter is then obtained by interconnecting (using diffusible chemicals) a set of N single-bit counters and connector modules. An optimization framework is used to determine appropriate gate parameters and to compute bounds on admissible pulse widths and relaxation (inter-pulse) times, as well as to guide the construction of novel gates. This work can be viewed as a step toward obtaining circuits that are capable of finite automaton computation in analogy to digital central processing units.

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A single-bit counter is designed for a repressor-based genetic circuit

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A scalable multi-bit counter is enabled by distributing the design across cells

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A computational optimization framework is proposed to guide the design

Methods for the Development of Recombinant Microorganisms for the Production of Natural Products

Metabolic engineering strives to develop microbial strains that are capable of producing a target chemical in a biological organism. There are still many challenges to overcome in order to achieve titers, yields, and productivities necessary for industrial production. The use of recombinant microorganisms to meet these needs is the next step for metabolic engineers. In this chapter, we aim to provide insight on both the applications of metabolic engineering for natural product biosynthesis as well as optimization methods.

Competitive dCas9 binding as a mechanism for transcriptional control

Catalytically dead Cas9 (dCas9) is a programmable transcription factor that can be targeted to promoters through the design of small guide RNAs (sgRNAs), where it can function as an activator or repressor. Natural promoters use overlapping binding sites as a mechanism for signal integration, where the binding of one can block, displace, or augment the activity of the other. Here, we implemented this strategy in Escherichia coli using pairs of sgRNAs designed to repress and then derepress transcription through competitive binding. When designed to target a promoter, this led to 27-fold repression and complete derepression. This system was also capable of ratiometric input comparison over two orders of magnitude. Additionally, we used this mechanism for promoter sequence-independent control by adopting it for elongation control, achieving 8-fold repression and 4-fold derepression. This work demonstrates a new genetic control mechanism that could be used to build analog circuit or implement cis-regulatory logic on CRISPRi-targeted native genes.

Synthetic Circuit-Driven Expression of Heterologous Enzymes for Disease Detection

The integration of nanotechnology and synthetic biology could lay the framework for new classes of engineered biosensors that produce amplified readouts of disease states. As a proof-of-concept demonstration of this vision, here we present an engineered gene circuit that, in response to cancer-associated transcriptional deregulation, expresses heterologous enzyme biomarkers whose activity can be measured by nanoparticle sensors that generate amplified detection readouts. Specifically, we designed an AND-gate gene circuit that integrates the activity of two ovarian cancer-specific synthetic promoters to drive the expression of a heterologous protein output, secreted Tobacco Etch Virus (TEV) protease, exclusively from within tumor cells. Nanoparticle probes were engineered to carry a TEV-specific peptide substrate in order to measure the activity of the circuit-generated enzyme to yield amplified detection signals measurable in the urine or blood. We applied our integrated sense-and-respond system in a mouse model of disseminated ovarian cancer, where we demonstrated measurement of circuit-specific TEV protease activity both in vivo using exogenously administered nanoparticle sensors and ex vivo using quenched fluorescent probes. We envision that this work will lay the foundation for how synthetic biology and nanotechnology can be meaningfully integrated to achieve next-generation engineered biosensors.

Quantifying the optimal strategy of population control of quorum sensing network in Escherichia coli

Biological functions of bacteria can be regulated by monitoring their own population density induced by the quorum sensing system. However, quantitative insight into the system’s dynamics and regulatory mechanism remain challenging. Here, we construct a comprehensive mathematical model of the synthetic quorum sensing circuit that controls population density in Escherichia coli. Simulations agree well with experimental results obtained under different ribosome-binding site (RBS) efficiencies. We present a quantitative description of the component dynamics and show how the components respond to isopropyl-β-D-1-thiogalactopyranoside (IPTG) induction. The optimal IPTG-induction range for efficiently controlling population density is quantified. The controllable area of population density by acyl-homoserine lactone (AHL) permeability is quantified as well, indicating that high AHL permeability should be treated with a high dose of IPTG, while low AHL permeability should be induced with low dose for efficiently controlling. Unexpectedly, an oscillatory behavior of the growth curve is observed with proper RBS-binding strengths and the oscillation is greatly restricted by the bacterial death induced by toxic metabolic by-products. Moreover, we identify that the mechanism underlying the emergence of oscillation is determined by the negative feedback loop structure within the signaling. Bifurcation analysis and landscape theory are further employed to study the stochastic dynamic and global stability of the system, revealing two faces of toxic metabolic by-products in controlling oscillatory behavior. Overall, our study presents a quantitative basis for understanding and new insights into the control mechanism of quorum sensing system, providing possible clues to guide the development of more rational control strategy.

Topologies of synthetic gene circuit for optimal fold change activation

Computations widely exist in biological systems for functional regulations. Recently, incoherent feedforward loop and integral feedback controller have been implemented into Escherichia coli to achieve a robust adaptation. Here, we demonstrate that an indirect coherent feedforward loop and mutual inhibition designs can experimentally improve the fold change of promoters, by reducing the basal level while keeping the maximum activity high. We applied both designs to six different promoters in E. coli, starting with synthetic inducible promoters as a proof-of-principle. Then, we examined native promoters that are either functionally specific or systemically involved in complex pathways such as oxidative stress and SOS response. Both designs include a cascade having a repressor and a construct of either transcriptional interference or antisense transcription. In all six promoters, an improvement of up to ten times in the fold change activation was observed. Theoretically, our unitless models show that when regulation strength matches promoter basal level, an optimal fold change can be achieved. We expect that this methodology can be applied in various biological systems for biotechnology and therapeutic applications.

DNA strand displacement reactions to accomplish a two-degree-of-freedom PID controller and its application in subtraction gate

Synthesis control circuits can be used to effectively control biochemical molecule processes. In the controller design based on chemical reaction networks (CRNs), generally only the tracking set-point is considered. However, the influence of disturbances, which are frequently encountered in biochemical systems, is often neglected, thus weakening the control effect of the system. In this article, tracking set-point input and suppressing disturbance input are considered in the control effect. Firstly, CRNs are adopted to construct a two-degree-of-freedom PID controller by combining a one-degree-of-freedom PID controller with a feedforward controller for the first time. Then, CRN expressions of the two input functions (step function and ramp function) used as input signals are defined. Furthermore, the two-degree-of-freedom PID controller is founded by DNA strand displacement (DSD) reaction networks, because DNA is an ideal engineering material to constitute molecular devices based on CRNs. The overshoot of the two-degree-of-freedom PID control system is significantly reduced compared to the one-degree-of-freedom PID control system. Finally, a leak reaction is treated as an extraneous disturbance input to a subtraction gate. The influence of external disturbance is solved by the two-degree-of-freedom PID controller. It is worth noting that the two-degree-of-freedom subtraction gate control system better restrains the impact of a disturbance input (leak reaction).

Synthetic Biology-Empowered Hydrogels for Medical Diagnostics

Synthetic biology is strongly inspired by concepts of engineering science and aims at the design and generation of artificial biological systems in different fields of research such as diagnostics, analytics, biomedicine, or chemistry. To this aim, synthetic biology uses an engineering approach relying on a toolbox of molecular sensors and switches that endows cellular hosts with non-natural computing functions and circuits. Importantly, this concept is not only limited to cellular approaches. Synthetic biological building blocks have also conferred sensing and switching capability to otherwise inactive materials. This principle has attracted high interest for the development of biohybrid materials capable of sensing and responding to specific molecular stimuli, such as disease biomarkers, antibiotics, or heavy metals. Moreover, the interconnection of individual sense-and-respond materials to complex materials systems has enabled the processing of, for example, multiple inputs or the amplification of signals using feedback topologies. Such systems holding high potential for applications in the analytical and diagnostic sectors will be described in this chapter.

Synthetic biology as driver for the biologization of materials sciences

Materials in nature have fascinating properties that serve as a continuous source of inspiration for materials scientists. Accordingly, bio-mimetic and bio-inspired approaches have yielded remarkable structural and functional materials for a plethora of applications. Despite these advances, many properties of natural materials remain challenging or yet impossible to incorporate into synthetic materials. Natural materials are produced by living cells, which sense and process environmental cues and conditions by means of signaling and genetic programs, thereby controlling the biosynthesis, remodeling, functionalization, or degradation of the natural material. In this context, synthetic biology offers unique opportunities in materials sciences by providing direct access to the rational engineering of how a cell senses and processes environmental information and translates them into the properties and functions of materials. Here, we identify and review two main directions by which synthetic biology can be harnessed to provide new impulses for the biologization of the materials sciences: first, the engineering of cells to produce precursors for the subsequent synthesis of materials. This includes materials that are otherwise produced from petrochemical resources, but also materials where the bio-produced substances contribute unique properties and functions not existing in traditional materials. Second, engineered living materials that are formed or assembled by cells or in which cells contribute specific functions while remaining an integral part of the living composite material. We finally provide a perspective of future scientific directions of this promising area of research and discuss science policy that would be required to support research and development in this field.

Synthetic neural-like computing in microbial consortia for pattern recognition

Complex biological systems in nature comprise cells that act collectively to solve sophisticated tasks. Synthetic biological systems, in contrast, are designed for specific tasks, following computational principles including logic gates and analog design. Yet such approaches cannot be easily adapted for multiple tasks in biological contexts. Alternatively, artificial neural networks, comprised of flexible interactions for computation, support adaptive designs and are adopted for diverse applications. Here, motivated by the structural similarity between artificial neural networks and cellular networks, we implement neural-like computing in bacteria consortia for recognizing patterns. Specifically, receiver bacteria collectively interact with sender bacteria for decision-making through quorum sensing. Input patterns formed by chemical inducers activate senders to produce signaling molecules at varying levels. These levels, which act as weights, are programmed by tuning the sender promoter strength Furthermore, a gradient descent based algorithm that enables weights optimization was developed. Weights were experimentally examined for recognizing 3 × 3-bit pattern.

A Whole-Cell Bacterial Biosensor for Blood Markers Detection in Urine

The early detection of blood in urine (hematuria) can play a crucial role in the treatment of serious diseases (e.g., infections, kidney disease, schistosomiasis, and cancer). Therefore, the development of low-cost portable biosensors for blood detection in urine has become necessary. Here, we designed an ultrasensitive whole-cell bacterial biosensor interfaced with an optoelectronic measurement module for heme detection in urine. Heme is a red blood cells (RBCs) component that is liberated from lysed cells. The bacterial biosensor includes Escherichia coli cells carrying a heme-sensitive synthetic promoter integrated with a luciferase reporter (luxCDABE) from Photorhabdus luminescens. To improve the bacterial biosensor performance, we re-engineered the genetic structure of luxCDABE operon by splitting it into two parts (luxCDE and luxAB). The luxCDE genes were regulated by the heme-sensitive promoter, and the luxAB genes were regulated by either constitutive or inducible promoters. We examined the genetic circuit's performance in synthetic urine diluent supplied with heme and in human urine supplied with lysed blood. Finally, we interfaced the bacterial biosensor with a light detection setup based on a commercial optical measurement single-photon avalanche photodiode (SPAD). The whole-cell biosensor was tested in human urine with lysed blood, demonstrating a low-cost, portable, and easy-to-use hematuria detection with an ON-to-OFF ratio of 6.5-fold for blood levels from 5 × 10⁴ to 5 × 10⁵ RBC per mL of human urine.

Measuring and modeling energy and power consumption in living microbial cells with a synthetic ATP reporter

Background

Adenosine triphosphate (ATP) is the main energy carrier in living organisms, critical for metabolism and essential physiological processes. In humans, abnormal regulation of energy levels (ATP concentration) and power consumption (ATP consumption flux) in cells is associated with numerous diseases from cancer, to viral infection and immune dysfunction, while in microbes it influences their responses to drugs and other stresses. The measurement and modeling of ATP dynamics in cells is therefore a critical component in understanding fundamental physiology and its role in pathology. Despite the importance of ATP, our current understanding of energy dynamics and homeostasis in living cells has been limited by the lack of easy-to-use ATP sensors and the lack of models that enable accurate estimates of energy and power consumption related to these ATP dynamics. Here we describe a dynamic model and an ATP reporter that tracks ATP in E. coli over different growth phases.

Results

The reporter is made by fusing an ATP-sensing rrnB P1 promoter with a fast-folding and fast-degrading GFP. Good correlations between reporter GFP and cellular ATP were obtained in E. coli growing in both minimal and rich media and in various strains. The ATP reporter can reliably monitor bacterial ATP dynamics in response to nutrient availability. Fitting the dynamics of experimental data corresponding to cell growth, glucose, acetate, dissolved oxygen, and ATP yielded a mathematical and circuit model. This model can accurately predict cellular energy and power consumption under various conditions. We found that cellular power consumption varies significantly from approximately 0.8 and 0.2 million ATP/s for a tested strain during lag and stationary phases to 6.4 million ATP/s during exponential phase, indicating ~ 8–30-fold changes of metabolic rates among different growth phases. Bacteria turn over their cellular ATP pool a few times per second during the exponential phase and slow this rate by ~ 2–5-fold in lag and stationary phases.

Conclusion

Our rrnB P1-GFP reporter and kinetic circuit model provide a fast and simple way to monitor and predict energy and power consumption dynamics in bacterial cells, which can impact fundamental scientific studies and applied medical treatments in the future.