Search

We present the Infobiotics Workbench (IBW), a user-friendly, scalable, and integrated computational environment for the computer-aided design of synthetic biological systems. It supports an iterative workflow that begins with specification of the desired synthetic system, followed by simulation and verification of the system in high-performance environments and ending with the eventual compilation of the system specification into suitable genetic constructs. IBW integrates modeling, simulation, verification, and biocompilation features into a single software suite. This integration is achieved through a new domain-specific biological programming language, the Infobiotics Language (IBL), which tightly combines these different aspects of in silico synthetic biology into a full-stack integrated development environment. Unlike existing synthetic biology modeling or specification languages, IBL uniquely blends modeling, verification, and biocompilation statements into a single file. This allows biologists to incorporate design constraints within the specification file rather than using decoupled and independent formalisms for different in silico analyses. This novel approach offers seamless interoperability across different tools as well as compatibility with SBOL and SBML frameworks and removes the burden of doing manual translations for standalone applications. We demonstrate the features, usability, and effectiveness of IBW and IBL using well-established synthetic biological circuits.

Metabolic engineering technologies have been employed with increasing success over the last three decades for the engineering and optimization of industrial host strains to competitively produce high-value chemical targets. To this end, continued reductions in the time taken from concept, to development, to scale-up are essential. Design–Build–Test–Learn pipelines that are able to rapidly deliver diverse chemical targets through iterative optimization of microbial production strains have been established. Biofoundries are employing in silico tools for the design of genetic parts, alongside combinatorial design of experiments approaches to optimize selection from within the potential design space of biological circuits based on multi-criteria objectives. These genetic constructs can then be built and tested through automated laboratory workflows, with performance data analysed in the learn phase to inform further design. Successful examples of rapid prototyping processes for microbially produced compounds reveal the potential role of biofoundries in leading the sustainable production of next-generation bio-based chemicals.

Biological circuits are responsible for transitions between cellular states in a timely fashion. For example, stem cells switch from an undifferentiated (unstable) state to a differentiated (stable) state. Conversely, cell cycle and circadian clocks are completed through transitions among successive (stable) states, i.e. waves, with (unstable) states switching them at definite timing. These transitions irreversibly determine the biological response or fate of a cell, to commit to reversible switches or to generate periodic oscillations of its state. Here, we review synthetic circuits that, in silico and in vivo, allow a cell to ‘make a decision’, i.e. to select which state to reach, among multiple ones available, through definite network designs. Specifically, we propose and discuss the designs, and their constituents motifs, which we consider to be more prone to reprogram cell behaviour, and whose parameters can be fine-tuned through systems biology and tested experimentally through Synthetic Biology. For these designs, exploration of the parameter space and of the influence of (external) cellular signals – which modulate circuit parameters – allows for the prediction of the circuit's response and its consequent impact on cell fate.

Background Synthetic biological circuits are widely utilized to control microbial cell functions. Natural and synthetic riboswitches are attractive sensor modules for use in synthetic biology applications. However, tuning the fold-change of riboswitch circuits is challenging because a deep understanding of the riboswitch mechanism and screening of mutant libraries is generally required. Therefore, novel molecular parts and strategies for straightforward tuning of the fold-change of riboswitch circuits are needed. Results In this study, we devised a toehold switch-based modulator approach that combines a hybrid input construct consisting of a riboswitch and transcriptional repressor and de-novo-designed riboregulators named toehold switches. First, the introduction of a pair of toehold switches and triggers as a downstream signal-processing module to the hybrid input for coenzyme B12 resulted in a functional riboswitch circuit. Next, several optimization strategies that focused on balancing the expression levels of the RNA components greatly improved the fold-change from 260- to 887-fold depending on the promoter and host strain. Further characterizations confirmed low leakiness and high orthogonality of five toehold switch pairs, indicating the broad applicability of this strategy to riboswitch tuning. Conclusions The toehold switch-based modulator substantially improved the fold-change compared to the previous sensors with only the hybrid input construct. The programmable RNA-RNA interactions amenable to in silico design and optimization can facilitate further development of RNA-based genetic modulators for flexible tuning of riboswitch circuitry and synthetic biosensors.

Synthetic biology strives to reliably control cellular behavior, typically in the form of user-designed interactions of biological components to produce a predetermined output. Engineered circuit components are frequently derived from natural sources and are therefore often hampered by inadvertent interactions with host machinery, most notably within the host central dogma. Reliable and predictable gene circuits require the targeted reduction or elimination of these undesirable interactions to mitigate negative consequences on host fitness and develop context-independent bioactivities. Here, we review recent advances in biological orthogonalization, namely the insulation of researcher-dictated bioactivities from host processes, with a focus on systematic developments that may culminate in the creation of an orthogonal central dogma and novel cellular functions.

Synthetic biology is an interdisciplinary field that uses well-established engineering principles for performing the analysis of the biological systems, such as biological circuits, pathways, controllers and enzymes. Conventionally, the analysis of these biological systems is performed using paper-and-pencil proofs and computer simulation methods. However, these methods cannot ensure accurate results due to their inherent limitations. Higher-order-logic (HOL) theorem proving is proposed and used as a complementary approach for analysing linear biological systems, which is based on developing a mathematical model of the genetic circuits and the bio-controllers used in synthetic biology based on HOL and analysing it using deductive reasoning in an interactive theorem prover. The involvement of the logic, mathematics and the deductive reasoning in this method ensures the accuracy of the analysis. It is proposed to model the continuous dynamics of the genetic circuits and their associated controllers using differential equations and perform their transfer function-based analysis using the Laplace transform in a theorem prover. For illustration, the genetic circuits of activated and repressed expressions and autoactivation of protein, and phase lag and lead controllers, which are widely used in cancer-cell identifiers and multi-input receptors for precise disease detection, are formally analyzed.

Plasmids of the ColE1 family are among the most frequently used plasmids in molecular biology. They were adopted early in the field for many biotechnology applications, and as model systems to study plasmid biology. The mechanism of replication of ColE1 plasmids is well understood, involving the interaction between a plasmid-encoded sense-antisense gene pair (RNAI and RNAII). Because of its mechanism of replication, bacterial cells cannot maintain two different plasmids with the same origin, with one being rapidly lost from the population – a process known as plasmid incompatibility. While mutations in the regulatory genes RNAI and RNAII have been reported to make colE1 plasmids more compatible, there has been no attempt to engineer compatible colE1 origins, which can be used for multi-plasmid applications and that can bypass design constrains created by the current limited plasmid origin repertoire available. Here, we show that by targeting sequence diversity to the loop regions of RNAI (and RNAII), it is possible to select new viable colE1 origins that are compatible with the wild-type one. We demonstrate origin compatibility is not simply determined by sequence divergence in the loops, and that pairwise compatibility is not an accurate guide for higher order interactions. We identify potential principles to engineer plasmid copy number independently from other regulatory strategies and we propose plasmid compatibility as a tractable model to study biological orthogonality. New characterised plasmid origins increase flexibility and accessible complexity of design for challenging synthetic biology applications where biological circuits can be dispersed between multiple independent genetic elements.

Cell-free transcription and translation systems promise to accelerate and simplify the engineering of proteins, biological circuits and metabolic pathways. Their encapsulation on microfluidic platforms can generate millions of cell-free reactions in picoliter volume droplets. However, current methods struggle to create DNA diversity between droplets while also reaching sufficient protein expression levels. In particular, efficient multi-gene expression has remained elusive. We here demonstrate that co-encapsulation of DNA-coated beads with a defined cell-free system allows high protein expression while also supporting genetic diversity between individual droplets. We optimize DNA loading on commercially available microbeads through direct binding as well as through the sequential coupling of up to three genes via a solid-phase Golden Gate assembly or BxB1 integrase-based recombineering. Encapsulation with an off-the-shelf microfluidics device allows for single or multiple protein expression from a single DNA-coated bead per 14 pL droplet. We envision that this approach will help to scale up and parallelize the rapid prototyping of more complex biological systems.

System and synthetic biology, the two emerging interdisciplinary sciences are important to study the complex behavior of a biological system or to discover new drugs or predict diseases. A powerful tool is important to model and simulate biochemical reactions, which can help in designing synthetic biological circuits. The authors in this paper attempted to model some of the biochemical reactions using the System Generator tool and implemented them in the FPGA domain to build a programmable synthetic biochemical reaction library. The simulated response of the model is validated using the SimBiology tool. In the future, integrating such synthetic libraries can help in designing a synthetic model of large biological network dynamics.

We study the problem of sparse nonlinear model recovery of high dimensional compositional functions. Our study is motivated by emerging opportunities in neuroscience to recover fine-grained models of biological neural circuits using collected measurement data. Guided by available domain knowledge in neuroscience, we explore conditions under which one can recover the underlying biological circuit that generated the training data. Our results suggest insights of both theoretical and practical interests. Most notably, we find that a sign constraint on the weights is a necessary condition for system recovery, which we establish both theoretically with an identifiability guarantee and empirically on simulated biological circuits. We conclude with a case study on retinal ganglion cell circuits using data collected from mouse retina, showcasing the practical potential of this approach., Preprint. 11 pages, 8 figures. Accepted Research Track paper to appear at KDD '21, August 14-18, Virtual Event, Singapore

Efforts to construct synthetic biological circuits with more complex functions have often been hindered by the idiosyncratic behavior, limited dynamic range, and crosstalk of commonly utilized parts. Here, we employ de novo RNA design to develop two high-performance translational repressors with sensing and logic capabilities. These synthetic riboregulators, termed toehold repressors and three-way junction (3WJ) repressors, detect transcripts with nearly arbitrary sequences, repress gene expression by up to 300-fold, and yield orthogonal sets of up to 15 devices. Automated forward engineering is used to improve toehold repressor dynamic range and SHAPE-Seq is applied to confirm the designed switching mechanism of 3WJ repressors in living cells. We integrate the modular repressors into biological circuits that execute universal NAND and NOR logic and evaluate the four-input expression NOT ((A1 AND A2) OR (B1 AND B2)) in Escherichia coli. These capabilities make toehold and 3WJ repressors valuable new tools for biotechnological applications.

Enabled by its central role in the molecular networks that govern cell function, RNA has been widely used for constructing components used in biological circuits for synthetic biology. Nucleic acid nanotechnology, which exploits predictable nucleic acid interactions to implement programmable molecular systems, has seen remarkable advances in in vitro nanoscale self-assembly and molecular computation, enabling the production of complex nanostructures and DNA-based neural networks. Living cells genetically engineered to execute nucleic acid nanotechnology programs thus have outstanding potential to significantly extend the current limits of synthetic biology. This perspective discusses the recent developments and future challenges in the field of synthetic bionanotechnology. Thus far, researchers in this emerging area have implemented dozens of programmable RNA nanodevices that provide precise control over gene expression at the transcriptional and translational levels and through CRISPR/Cas effectors. Moreover, they have employed synthetic self-assembling RNA networks in engineered bacteria to carry out computations featuring up to a dozen inputs and to substantially enhance the rate of chemical synthesis. Continued advancement of the field will benefit from improved in vivo strategies for streamlining nucleic acid network synthesis and new approaches for enhancing network function. As the field matures and the complexity gap between in vitro and in vivo systems narrows, synthetic bionanotechnology promises to have diverse potential applications ranging from intracellular circuits that detect and treat disease to synthetic enzymatic pathways that efficiently produce novel drug molecules.

DNA is a highly programmable biomolecule and has been used to construct biological circuits for different purposes. An important development of DNA circuits is to process the information on receptors on cell membranes. In this Communication, we introduce an architecture to program localized DNA-based biomolecular reaction networks on cancer cell membranes. Based on our architecture, various types of reaction networks have been experimentally demonstrated, from simple linear cascades to reaction networks of complex structures. These localized DNA-based reaction networks can be used for medical applications such as cancer cell detection. Compared to prior work on DNA circuits for evaluating cell membrane receptors, the DNA circuits made by our architecture have several major advantages including simpler design, lower leak, lower cost, and higher signal-to-background ratio.

Engineering microrobotic systems using a bio-hybrid approach that couples synthetic micro/nanoscale components with live cells is a powerful approach to address key shortcomings of micro/nanotechnology such as providing an on-board power source, communication, and decentralized control. In the last decade, a number of centralized control strategies based on native biological mechanisms have been demonstrated; however, decentralized cooperative control of a swarm of bio-hybrid microrobots has rarely been explored. In this work, we use our previously developed agent-based computational model to explore the utility of integrating synthetic biology with bio-hybrid microrobotics with the goal of developing strategies for robust decentralized control of bio-hybrid microrobotic systems. To this end, our Nanoscale Bacteria-Enabled Autonomous Delivery System (NanoBEADS), wherein each agent is comprised of an Escherichia coli bacterium conjugated with an ensemble of nanoparticles, was used as a model system. We imparted bacteria with engineered biological circuits to facilitate agent-agent communication and enable predictable and robust cooperative control of a network of NanoBEADS agents. We developed a hybrid control strategy wherein a centralized chemotaxis-based control scheme is used to direct migration, and a decentralized quorum sensing (QS)-based control scheme enables the agents to independently coordinate a cooperative behavior (fluorescent protein expression). We then computationally analyzed the role of bacterial subsystem properties and spatiotemporal changes in population organization in the emergent behavior (i.e., the activation time of the QS response) of the NanoBEADS. We show that the spatial changes in NanoBEADS agents’ distribution, in response to the centralized control input, and the mass transport boundary conditions are critical factors in determining the dynamics as well as the sensitivity and robustness of the QS response. We analyze the stark differences in feasible QS parameter domains under dissimilar chemoattractant gradients and show that the flow boundary conditions may greatly influence system function. Altogether, we show that a bacteria-based bio-hybrid system must be designed with careful consideration of growth rate, chemotaxis properties, QS properties, and the environment initial and boundary conditions in the context of its target application. This modeling framework can serve as an insightful tool for the predictive design of bio-hybrid microrobotic swarms with a tunable and robust response.

Cells utilize transcriptional regulation networks to respond to environmental signals. Network motifs, such as feedforward loops, play essential roles in these regulatory networks. In this work, we construct two different functional and modular incoherent type 1 feedforward loop circuits in a cell-free transcription-translation system and in cells. With the help of mathematical modeling and the cell-free system, we can streamline the design-build-test cycles of the circuits, in which we characterize and optimize these circuits in vitro to confirm that they function as expected before implementing them in vivo. We show that the performance of these circuits from in vitro studies closely recapitulates those from in vivo experiments. We demonstrate that these feedforward loops show dynamic response and pulse-like behavior both in vitro and in vivo. These novel feedforward loop network motifs can be incorporated in more complicated biological circuits as detectors or responders.

Summary Artificial neural networks trained to solve sensory tasks can develop statistical representations that match those in biological circuits. However, it remains unclear whether they can reproduce properties of individual neurons. Here, we investigated how artificial networks predict individual neuron properties in the visual motion circuits of the fruit fly Drosophila. We trained anatomically constrained networks to predict movement in natural scenes, solving the same inference problem as fly motion detectors. Units in the artificial networks adopted many properties of analogous individual neurons, even though they were not explicitly trained to match these properties. Among these properties was the split into ON and OFF motion detectors, which is not predicted by classical motion detection models. The match between model and neurons was closest when models were trained to be robust to noise. These results demonstrate how anatomical, task, and noise constraints can explain properties of individual neurons in a small neural network.

Ensembles of autonomous, spatially distributed wireless stimulators can offer a versatile approach to patterned microstimulation of biological circuits such as the cortex. Here, we demonstrate the concept of a distributed, untethered, and addressable microstimulator, integrating an ultraminiaturized ASIC with a custom-designed GaAs photovoltaic (PV) microscale energy harvester, dubbed as an "optical neurograin (ONG)". An on-board Manchester-encoded near-infrared downlink delivers incident IR power and provides a synchronous clock across an ensemble of microdevices, triggering stimulus events by remote command. Each ONG has a unique device address and, when an incoming downlink bit sequence matches with this device identification (ID), the implant delivers a charge-balanced current stimulus to the target cortex. Present devices use 7-bit metal fuses fabricated during the CMOS process for their device ID, laser-scribed in post-processing, allowing in principle for a stimulator network of up to 128 nodes. We have characterized small ensembles of ONGs and shown a proof of concept of the system both on benchtop and in vivo rat rodent model.

Deep convolutional neural networks (CNNs) are powerful computational tools for a large variety of tasks (Goodfellow, 2016). Their architecture, composed of layers of repeated identical neural units, draws inspiration from visual neuroscience. However, biological circuits contain a myriad of additional details and complexity not translated to CNNs, including diverse neural cell types (Tasic, 2018). Many possible roles for neural cell types have been proposed, including: learning, stabilizing excitation and inhibition, and diverse normalization (Marblestone, 2016; Gouwens, 2019). Here we investigate whether neural cell types, instantiated as diverse activation functions in CNNs, can assist in the feed-forward computational abilities of neural circuits. Our heterogeneous cell type networks mix multiple activation functions within each activation layer. We assess the value of mixed activation functions by comparing image classification performance to that of homogeneous control networks with only one activation function per network. We observe that mixing activation functions can improve the image classification abilities of CNNs. Importantly, we find larger improvements when the activation functions are more diverse, and in more constrained networks. Our results suggest a feed-forward computational role for diverse cell types in biological circuits. Additionally, our results open new avenues for the development of more powerful CNNs.

In 1961, Jacob and Monod proposed the operon model of gene regulation. At the model's core was the modular assembly of regulators, operators, and structural genes. To illustrate the composability of these elements, Jacob and Monod linked phenotypic diversity to the architectures of regulatory circuits. In this review, we examine how the circuit blueprints imagined by Jacob and Monod laid the foundation for the first synthetic gene networks that launched the field of synthetic biology in 2000. We discuss the influences of the operon model and its broader theoretical framework on the first generation of synthetic biological circuits, which were predominantly transcriptional and posttranscriptional circuits. We also describe how recent advances in molecular biology beyond the operon model—namely, programmable DNA- and RNA-binding molecules as well as models of epigenetic and posttranslational regulation—are expanding the synthetic biology toolkit and enabling the design of more complex biological circuits.

Nucleic acid-based regulatory components provide a promising toolbox for constructing synthetic biological circuits due to their design flexibility and seamless integration towards complex systems. In particular, small-transcriptional activating RNA (STAR) and toehold switch as regulators of transcription and translation steps have shown a large library size and a wide dynamic range, meeting the criteria to scale up genetic circuit construction. Still, there are limited attempts to integrate the heterogeneous regulatory components for multilevel regulatory circuits in living cells. In this work, inspired by the design principle of STAR, we designed several switchable transcription terminators starting from natural and synthetic terminators. These switchable terminators could be designed to respond to specific RNA triggers with minimal sequence constraints. When combined with toehold switches, the switchable terminators allow simultaneous control of transcription and translation processes to minimize leakage in Escherichia coli. Further, we demonstrated a set of logic gates implementing 2-input AND circuits and multiplexing capabilities to control two different output proteins. This study shows the potential of novel switchable terminator designs that can be computationally designed and seamlessly integrated with other regulatory components, promising to help scale up the complexity of synthetic gene circuits in living cells.

International audience; In this preliminary work we give a geometric characterization of the time-optimal trajectories for a piecewise affine bistable switch. Such hybrid models play a major role in systems biology, as they can expressively account for the behaviors of simple gene-regulatory networks. A key example of these biological circuits is the genetic toggle switch, for which the optimality of a state switch has not been studied to the present day. The main tool of this work is an adaptation of the Hybrid Pontryagin's Maximum Principle to our setting, which allows to prove that the optimal control strategy is indeed a simple bang-bang feedback control law. In opposition to previous works, we show that a time-optimal transition between states should pass by an undifferentiated state, well known in cell biology for playing a major role in fate differentiation of cells. We conclude by showing numerical simulations of optimal trajectories illustrating the structure of the bang-bang optimal control for different scenarios.

Synthetic biologists use and combine diverse biological parts to build systems such as genetic circuits that perform desirable functions in, for example, biomedical or industrial applications. Computer-aided design methods have been developed to help choose appropriate network structures and biological parts for a given design objective. However, they almost always model the behavior of the network in an average cell, despite pervasive cell-to-cell variability. Here, we present a computational framework to guide the design of synthetic biological circuits while accounting for cell-to-cell variability explicitly. Our design method integrates a NonLinear Mixed-Effect (NLME) framework into an existing algorithm for design based on ordinary differential equation (ODE) models. The analysis of a recently developed transcriptional controller demonstrates first insights into design guidelines when trying to achieve reliable performance under cell-to-cell variability. We anticipate that our method not only facilitates the rational design of synthetic networks under cell-to-cell variability, but also enables novel applications by supporting design objectives that specify the desired behavior of cell populations., Lecture Notes in Computer Science, 12881, ISSN:0302-9743, ISSN:1611-3349, Computational Methods in Systems Biology. CMSB 2021, ISBN:978-3-030-85632-8, ISBN:978-3-030-85633-5

Transcription and translation are at the heart of metabolism and signal transduction. In this study, we developed an effective biophysical modeling approach to simulate transcription and translation processes. The model, composed of coupled ordinary differential equations, was tested by comparing simulations of two cell free synthetic circuits with experimental measurements generated in this study. First, we considered a simple circuit in which sigma factor 70 induced the expression of green fluorescent protein. This relatively simple case was then followed by a more complex negative feedback circuit in which two control genes were coupled to the expression of a third reporter gene, green fluorescent protein. Many of the model parameters were estimated from previous biophysical studies in the literature, while the remaining unknown model parameters for each circuit were estimated by minimizing the difference between model simulations and messenger RNA (mRNA) and protein measurements generated in this study. In particular, either parameter estimates from published studies were used directly, or characteristic values found in the literature were used to establish feasible ranges for the parameter estimation problem. In order to perform a detailed analysis of the influence of individual model parameters on the expression dynamics of each circuit, global sensitivity analysis was used. Taken together, the effective biophysical modeling approach captured the expression dynamics, including the transcription dynamics, for the two synthetic cell free circuits. While, we considered only two circuits here, this approach could potentially be extended to simulate other genetic circuits in both cell free and whole cell biomolecular applications as the equations governing the regulatory control functions are modular and easily modifiable. The model code, parameters, and analysis scripts are available for download under an MIT software license from the Varnerlab GitHub repository.

Summary Biological circuits and systems within even a single cell need to be represented by large-scale feedback networks of nonlinear, stochastic, stiff, asynchronous, non-modular coupled differential equations governing complex molecular interactions. Thus, rational drug discovery and synthetic biological design is difficult. We suggest that a four-pronged interdisciplinary approach merging biology and electronics can help: (1) The mapping of biological circuits to electronic circuits via quantitatively exact schematics; (2) The use of existing electronic circuit software for hierarchical modeling, design, and analysis with such schematics; (3) The use of cytomorphic electronic hardware for rapid stochastic simulation of circuit schematics and associated parameter discovery to fit measured biological data; (4) The use of bio-electronic reporting circuits rather than bio-optical circuits for measurement. We suggest how these approaches can be combined to automate design, modeling, analysis, simulation, and quantitative fitting of measured data from a synthetic biological operational amplifier circuit in living microbial cells., Graphical Abstract, Bioelectronics; Engineering; Electrical Engineering

Molecules and combinations of molecules are the natural communication currency of microbes; microbes have evolved and been engineered to sense a variety of compounds, often with exquisite sensitivity. The availability of microbial biosensors, combined with the ability to genetically engineer biological circuits to process information, make microbes attractive bionanomachines for propagating information through molecular communication (MC) networks. However, MC networks built entirely of biological components suffer a number of limitations. They are extremely slow due to processing and propagation delays and must employ simple algorithms due to the still limited computational capabilities of biological circuits. In this work, we propose a hybrid bio-electronic framework which utilizes biological components for sensing but offloads processing and computation to traditional electronic systems and communication infrastructure. This is achieved by using tools from the burgeoning field of optogenetics to trigger biosensing through an optoelectronic interface, alleviating the need for computation and communication in the biological domain.

Synthetic biology applications often require engineered computing structures, which can be programmed to process the information in a given way. However, programming of these structures usually requires significant amount of trial-and-error genetic engineering. This process is to some degree analogous to the design of application-specific integrated circuits (ASIC) in the domain of digital electronic circuits, which often require complex and time-consuming workflows to obtain a desired response. We describe a design of programmable biological circuits that can be configured without additional genetic engineering. Their configuration can be changed in vivo, i.e. during the execution of their biological program, simply with an introduction of programming inputs. These, e.g., increase the degradation rates of selected proteins that store the current configuration of the circuit. Programming can be thus performed in the field as in the case of field-programmable gate array (FPGA) circuits, which present an attractive alternative of ASICs in digital electronics. We describe a basic programmable unit, which we denote configurable (bio)logical block (CBLB) inspired by the architecture of configurable logic blocks (CLBs), basic functional units within the FPGA circuits. The design of a CBLB is based on distributed cellular computing modules, which makes its biological implementation easier to achieve. We establish a computational model of a CBLB and analyse its response with a given set of biologically feasible parameter values. Furthermore, we show that the proposed CBLB design exhibits correct behaviour for a vast range of kinetic parameter values, different population ratios, and as well preserves this response in stochastic simulations.

Over the last decade, the combining of newly developed molecular tools for DNA editing with engineering principles has allowed the creation of complex cellular devices, usually based on complex genetic circuits, for many different purposes. However, when the technological evolution of genetic circuitry is compared with previous technologies such as electronic circuitry, clear limitations regarding the technological scalability of genetic circuitry are observed due to the lack of predictability. To overcome this problem, it is necessary to create new theoretical frameworks for designing genetic circuits in a feasible and reliable manner, taking into account those limitations. Among a number of such limitations, the so-called genetic burden is one of the main constraints. Surprisingly, despite its relevance, little attention has been paid to genetic burden, and it is often not considered when designing genetic circuits. In this study, a new general mathematical formalism is presented, describing the effects of genetic burden on gene expression. The mathematical analysis shows that alterations in gene expression due to genetic burden can be qualitatively described independently of the specific genetic features of the system under consideration. The mathematical model was experimentally tested in different genetic circuits. The experimental evidence coincides with the expected behaviors described by the model in complex scenarios. For instance, observed modulations in the expression levels of constitutive genes in response to changes in the levels of external inducers of gene expression that do not directly modulate them, or the emergence of limitations in gene overexpression, can be understood in terms of genetic burden. The present mathematical formalism provides a useful general framework for gene circuit design that will help to advance synthetic biological systems.

The estimation of parameters controlling the electrical properties of biological neurons is essential to determine their complement of ion channels and to understand the function of biological circuits. By synchronizing conductance models to time series observations of the membrane voltage, one may construct models capable of predicting neuronal dynamics. However, identifying the actual set of parameters of biological ion channels remains a formidable theoretical challenge. Here, we present a regularization method that improves convergence towards this optimal solution when data are noisy and the model is unknown. Our method relies on the existence of an offset in parameter space arising from the interplay between model nonlinearity and experimental error. By tuning this offset, we induce saddle-node bifurcations from sub-optimal to optimal solutions. This regularization method increases the probability of finding the optimal set of parameters from 67% to 94.3%. We also reduce parameter correlations by implementing adaptive sampling and stimulation protocols compatible with parameter identifiability requirements. Our results show that the optimal model parameters may be inferred from imperfect observations provided the conditions of observability and identifiability are fulfilled., Author summary The accurate estimation of neuronal parameters inaccessible to experiment is essential to our understanding of intracellular dynamics and to predicting the behaviour of biocircuits. However, this program is met with challenges including our lack of knowledge of the precise equations of biological neurons, their highly nonlinear response to stimulation and error introduced by the measurement apparatus. The imprecise knowledge of model and data introduces uncertainty in the parameter field. Our work describes a regularization method that arrives at the optimal parameter solution with a probability of 94%. The uncertainty on parameter estimates is further reduced with the help of an adaptive sampling method that maximises the duration of the assimilation window while keeping the size of the problem constant. Our work shows that the true configuration of a neuronal system may be inferred from time series observations provided external stimuli are calibrated to drive the system over its entire dynamic range.

Fanouts are an essential element for signal cloning to achieve logic sharing, but can be a very limited resource in certain emerging technologies, such as quantum circuits, superconducting electronic circuits, photonic integrated circuits, and biological circuits. Although fanout synthesis has been intensively studied for high performance circuit synthesis, prior methods often treat fanout as a soft constraint for critical path optimization or target on specific high-fanout nets such as clock and reset signals. They are not particularly suited for circuit synthesis of these emerging technologies. By treating fanouts as first class citizens, the problem of fanout-bounded logic synthesis was posed as a challenge in the 2019 IWLS Programming Contest. In this paper, we present our winning method, which achieved the overall best quality in the competition, based on fanout load redistribution among existing or expanded equivalent signals.

Modern electronic computing devices profit from two far-reaching design choices: the digital abstraction of a continuous signal domain into a binary domain to tolerate noise, and the use of the complementary metal-oxide-semiconductor (CMOS) technique in gates to reduce noise and power consumption. Biological circuit design in the last two decades has been greatly inspired by the digital abstraction of electronic circuits, while still using a relatively analog machinery. With the constantly growing techniques and components available in synthetic biology, the question arises if chip design choices and other lessons learned from silicon computers can be transferred to biological designs. In this context, we discuss here the similarities and differences between the two fields concerning representation of circuits, solutions to computational problems, their implementations, and challenges.

Stochastic pulsatile dynamics have been observed in an increasing number of biological circuits with typical mechanism involving feedback control. Surprisingly, recent single-cell experiments showed that E. coli flagellar class-2&3 promoters are activated in stochastic pulses without the means of feedback, however, the underlying design principles of pulse generation have remained unclear. Here, by developing a system-level stochastic model constrained by a large set of E. coli flagellar synthesis data from different strains and mutants, we identify the underlying design principles for generating stochastic transcriptional pulses without feedback. Our model shows that YdiV, an inhibitor of the class-1 master regulator (FlhDC), creates an ultrasensitve switch that serves as a digital filter to eliminate small amplitude FlhDC fluctuations. Additionally, we demonstrate that fast temporal fluctuations of FlhDC are smoothed out and integrated over time before affecting class-2 downstream genes. Together, our results reveal the existence of a filter-and-integrate design that is necessary for generating stochastic pulses without feedback. This strategy suggests that E. coli may avoid premature activation of the expensive flagellar gene expression by filtering input fluctuations in intensity and in time.

Design and implementation of synthetic biological circuits highly depends on well-characterized, robust promoters with predictable input-output responses. While great progress has been made with heterotrophic model organisms such as Escherichia coli, the available variety of tunable promoter parts for phototrophic cyanobacteria is still limited. Commonly used synthetic and semisynthetic promoters show weak dynamic ranges or no regulation at all in cyanobacterial models. Well-controlled alternatives such as native metal-responsive promoters, however, pose the problems of inducer toxicity and lacking orthogonality. Here, we present the comparative assessment of dose-response functions of four different inducible promoter systems in the model cyanobacterium Synechocystis sp. PCC 6803. Using the novel bimodular reporter plasmid pSHDY, dose-response dynamics of the re-established vanillate-inducible promoter PvanCC was compared to the previously described rhamnose-inducible Prha, the anhydrotetracycline-inducible PL03, and the Co2+-inducible PcoaT. We estimate individual advantages and disadvantages regarding dynamic range and strength of each promoter, also in comparison with well-established constitutive systems. We observed a delicate balance between transcription factor toxicity and sufficient expression to obtain a dose-dependent response to the inducer. In summary, we expand the current understanding and employability of inducible promoters in cyanobacteria, facilitating the scalability and robustness of synthetic regulatory network designs and of complex metabolic pathway engineering strategies.

We present a Python-based software package to automatically obtain phenomenological models of input-controlled synthetic biological circuits that guide the design using chemical reaction-level descriptive models. From the parts and mechanism description of a synthetic biological circuit, it is easy to obtain a chemical reaction model of the circuit under the assumptions of mass-action kinetics using various existing tools. However, using these models to guide design decisions during an experiment is difficult due to a large number of reaction rate parameters and species in the model. Hence, phenomenological models are often developed that describe the effective relationships among the circuit inputs, outputs, and only the key states and parameters. In this paper, we present an algorithm to obtain these phenomenological models in an automated manner using a Python package for circuits with inputs that control the desired outputs. This model reduction approach combines the common assumptions of time-scale separation, conservation laws, and species’ abundance to obtain the reduced models that can be used for design of synthetic biological circuits. We consider an example of a simple gene expression circuit and another example of a layered genetic feedback control circuit to demonstrate the use of the model reduction procedure.

Dynamic RNA nanotechnology with small conditional RNAs (scRNAs) offers a promising conceptual approach to introducing synthetic regulatory links into endogenous biological circuits. Here, we use human cell lysate containing functional Dicer and RNases as a testbed for engineering scRNAs for conditional RNA interference (RNAi). scRNAs perform signal transduction via conditional shape change: detection of a subsequence of mRNA input X triggers formation of a Dicer substrate that is processed to yield small interfering RNA (siRNA) output anti-Y targeting independent mRNA Y for destruction. Automated sequence design is performed using the reaction pathway designer within NUPACK to encode this conditional hybridization cascade into the scRNA sequence subject to the sequence constraints imposed by X and Y. Because it is difficult for secondary structure models to predict which subsequences of mRNA input X will be accessible for detection, here we develop the RNAhyb method to experimentally determine accessible windows within the mRNA that are provided to the designer as sequence constraints. We demonstrate the programmability of scRNA regulators by engineering scRNAs for transducing in both directions between two full-length mRNAs X and Y, corresponding to either the forward molecular logic "if X then not Y" (X [Formula: see text] Y) or the reverse molecular logic "if Y then not X" (Y [Formula: see text] X). In human cell lysate, we observe a strong OFF/ON conditional response with low crosstalk, corresponding to a ≈20-fold increase in production of the siRNA output in response to the cognate versus noncognate full-length mRNA input. 2'OMe-RNA chemical modifications protect signal transduction reactants and intermediates against RNase degradation while enabling Dicer processing of signal transduction products. Because diverse biological pathways interact with RNA, scRNAs that transduce between detection of endogenous RNA inputs and production of biologically active RNA outputs hold great promise as a synthetic regulatory paradigm.

This paper traced an introductory script about the natural communication by an interface between the tissues and the environment theoretically, as a possible form of biological adaptation in environment, as a first step for popularization in tissue engineering for mechanical and chemical engineers. Knowing the keys communication system, it’s theoretically possible to understand that even after generating a specialized tissue, it is still possible to induce new stem cells to generate tissues. According to external and internal tissues readings, as by integrin mechanism, or actomyosin induced by various means, presented throughout this study. Also, make visible and clear new stem cells induction techniques through stimuli, activated by means for the formation of specialized and interconnected specialized cells for tissue engineering. In this objective investigation is showed some mechanical analytical apparatus in new designs to automate biological processes. The domain of all flow and mechanism in stem cell activation triggers is as important as the knowledge of DNA sequencing. With the focus on proving the ability of the tissues to read passively, interpret the external environment and make changes by biochemicals for appropriate adaptation, the genes interpret the environmental stimuli, human senses interfaces and activate adult stem cell niches. External factors along the stem cell are the focus of this investigation, e.g. it has already been proven that a specific nanotopography is a platform to induce differentiation control in stem cells. By nanotopography a stem cell can being induced to become a specific tissue. Through the control and listing these activation keys through mechanical engineering and logic, we can create true artificial tissue factories in high precision innovative scaffolds geometries, with final objective new addictive manufacture and innovative architecture for biological circuits.

Background Synthesis of a variety of biological circuits for specific functional purposes has made a tremendous progress in recent years. The ultimate goal of combining molecular biology and engineering is to realize a functional biocomputer. To address this challenge, all previous efforts work toward building up the bio-computer as the ultimate goal. To this aim, there should be a key module, named control unit (CU), to direct a serious of logic or arithmetic operations within the processor. Methods This research task develops a bio-CU to work with a bio-ALU, which is realized from the combination of previously developed genetic logic gates to fulfill the kernel function of CPU as those done in the silicon computer. Results A possible framework of the bio-CPU has demonstrated how to connect a bio-CU with a bio-ALU to conduct a fetch-decode-execute cycle of a macro instruction. It presents not only capability of 4-bit full adder but coordination of related modules in biocomputer. Conclusions We have demonstrated computer simulation for applications of the genetic circuits in biocomputer construction. It’s expected to inspire follow-up study to synthesize potential configurations of the future biocomputer.

Background The pursuit of standardization and reliability in synthetic biology has achieved, in recent years, a number of advances in the design of more predictable genetic parts for biological circuits. However, even with the development of high-throughput screening methods and whole-cell models, it is still not possible to predict reliably how a synthetic genetic construct interacts with all cellular endogenous systems. This study presents a genome-wide analysis of how the expression of synthetic genes is affected by systematic perturbations of cellular functions. We found that most perturbations modulate expression indirectly through an effect on cell size, putting forward the existence of a generic Size-Expression interaction in the model prokaryote Escherichia coli. Results The Size-Expression interaction was quantified by inserting a dual fluorescent reporter gene construct into each of the 3822 single-gene deletion strains comprised in the KEIO collection. Cellular size was measured for single cells via flow cytometry. Regression analyses were used to discriminate between expression-specific and gene-specific effects. Functions of the deleted genes broadly mapped onto three systems with distinct primary influence on the Size-Expression map. Perturbations in the Division and Biosynthesis (DB) system led to a large-cell and high-expression phenotype. In contrast, disruptions of the Membrane and Motility (MM) system caused small-cell and low-expression phenotypes. The Energy, Protein synthesis and Ribosome (EPR) system was predominantly associated with smaller cells and positive feedback on ribosome function. Conclusions Feedback between cell growth and gene expression is widespread across cell systems. Even though most gene disruptions proximally affect one component of the Size-Expression interaction, the effect therefore ultimately propagates to both. More specifically, we describe the dual impact of growth on cell size and gene expression through cell division and ribosomal content. Finally, we elucidate aspects of the tight control between swarming, gene expression and cell growth. This work provides foundations for a systematic understanding of feedbacks between genetic and physiological systems. Electronic supplementary material The online version of this article (10.1186/s12918-017-0494-1) contains supplementary material, which is available to authorized users.

Computational methods enable the design of synthetic biological circuits demonstrating a specific dynamic behavior. Current methods are based on the assembly of parts characterized in different contexts, which often fail to operate as predicted when combined. Here we introduce a circuit design method that compensates for parts uncertainty by identifying circuit topologies whose behavior is robust to variations in parameters. Our heuristic topological filtering approach efficiently yields robust circuit designs in a Bayesian framework, and enables to reliably assess trade-offs between performance, robustness, and experimental feasibility, thus increasing the probability of success of circuit implementation.

Synthetic biology is an emerging science that aims to create new biological functions that do not exist in nature, based on the knowledge acquired in life science over the last century. Since the beginning of this century, several projects in synthetic biology have emerged. The complexity of the developed artificial bio-functions is relatively low so that empirical design methods could be used for the design process. Nevertheless, with the increasing complexity of biological circuits, this is no longer the case and a large number of computer aided design softwares have been developed in the past few years. These tools include languages for the behavioral description and the mathematical modelling of biological systems, simulators at different levels of abstraction, libraries of biological devices and circuit design automation algorithms. All of these tools already exist in other fields of engineering sciences, particularly in microelectronics. This is the approach that is put forward in this paper.

Cells process information using bio-molecular circuits of interacting proteins and genes. A remarkable property of these circuits is their ability to function in highly variable biological environments: Biochemical processes are stochastic, environmental conditions fluctuate, and genetic polymorphisms are abundant. How is variability buffered to maintain a robust output? Can variability be exploited to allow computations not possible by deterministic dynamics? These questions lie at the heart of contemporary systems biology. We argue here that biological variability is fundamental for understanding principles underlying design and function of biological circuits. As concrete examples, we will discuss the buffering of variability during embryonic patterning, and the incorporation of variability in microbial responses to changing conditions.

Variation in the concentration of biological components is inescapable for any cell. Robustness in any biological circuit acts as a cushion against such variation and enables the cells to produce homogeneous output despite the fluctuation. The two-component system (TCS) with a bifunctional sensor kinase (that possesses both kinase and phosphatase activities) is proposed to be a robust circuit. Few theoretical models explain the robustness of a TCS, although the criteria and extent of robustness by these models differ. Here, we provide experimental evidence to validate the extent of the robustness of a TCS signaling pathway. We have designed a synthetic circuit in Escherichia coli using a representative TCS of Mycobacterium tuberculosis, MprAB, and monitored the in vivo output signal by systematically varying the concentration of either of the components or both. We observed that the output of the TCS is robust if the concentration of MprA is above a threshold value. This observation is further substantiated by two in vitro assays, in which we estimated the phosphorylated MprA pool or MprA-dependent transcription yield by varying either of the components of the TCS. This synthetic circuit could be used as a model system to analyze the relationship among different components of gene regulatory networks. IMPORTANCE Robustness in essential biological circuits is an important feature of the living organism. A few pieces of evidence support the existence of robustness in vivo in the two-component system (TCS) with a bifunctional sensor kinase (SK). The assays were done under physiological conditions in which the SK was much lower than the response regulator (RR). Here, using a synthetic circuit, we varied the concentrations of the SK and RR of a representative TCS to monitor output robustness in vivo. In vitro assays were also performed under conditions where the concentration of the SK was greater than that of the RR. Our results demonstrate the extent of output robustness in the TCS signaling pathway with respect to the concentrations of the two components.

The interaction between synaptic and intrinsic dynamics can efficiently shape neuronal input-output relationships in response to temporally structured spike trains. We use a neuron model with subthresh-old oscillations receiving inputs through a synapse with short-term depression and facilitation to show that the combination of intrinsic subthreshold and synaptic dynamics leads to channel-specific nontrivial responses and recognition of specific temporal structures. Our study employs the Generalized Integrate and-Fire (GIF) model, which can be subjected to analytical characterization. We map the temporal structure of spike input trains to the type of spike response, and show how the emergence of nontrivial input- output preferences is modulated by intrinsic and synaptic parameters in a synergistic manner. We demonstrate that these temporal input discrimination properties are robust to noise and to variations in synaptic strength. Furthermore, we also illustrate the presence of these input-output relationships in conductance-based models. Our results suggest a widespread computationally economic and easily tunable mechanism for temporal information discrimination in single neurons. (c) 2020 Elsevier B.V. All rights reserved., This work was supported AEI/FEDER grants FIS2017-84256-P (JJT) and PGC2018-095895-B-I00, DPI2015-65833-P (RL & PV).

The past two decades have witnessed an increasing popularity of zebrafish (Danio rerio) as a vertebrate model organism for studying complex behaviors. Paradigms capable of high throughput and comprehensive characterization in parallel with advances of genetic and genomic approaches have shed lights on the underpinning genetic mechanisms by which a behavior repertoire is displayed in zebrafish. In this chapter, we will first describe several paradigms used to characterize zebrafish behaviors with high efficiency and translational relevance. Then we will discuss how these behavioral paradigms can be further exploited with cutting-edge genetic and genomic technologies in order to depict the biological circuits involved.

We show that logic computational circuits in gene regulatory networks arise from a fibration symmetry breaking in the network structure. From this idea we implement a constructive procedure that reveals a hierarchy of genetic circuits, ubiquitous across species, that are surprising analogues to the emblematic circuits of solid-state electronics: starting from the transistor and progressing to ring oscillators, current-mirror circuits to toggle switches and flip-flops. These canonical variants serve fundamental operations of synchronization and clocks (in their symmetric states) and memory storage (in their broken symmetry states). These conclusions introduce a theoretically principled strategy to search for computational building blocks in biological networks, and present a systematic route to design synthetic biological circuits., Author summary We show that the core functional logic of genetic circuits arises from a fundamental symmetry breaking of the interactions of the biological network. The idea can be put into a hierarchy of symmetric genetic circuits that reveals their logical functions. We do so through a constructive procedure that naturally reveals a series of building blocks, widely present across species. This hierarchy maps to a progression of fundamental units of electronics, starting with the transistor, progressing to ring oscillators and current-mirror circuits and then to synchronized clocks, switches and finally to memory devices such as latches and flip-flops.