Biomolecular Circuits Research Articles

Facile Strategy to Output Fluorescein from Nucleic Acid Interactions.

Biomolecular operations, which involve the conversion of molecular signals or interactions into specific functional outputs, are fundamental to the field of biology and serve as the important foundation for the design of diagnostic and therapeutic systems. To maximize their functionalities and broaden their applicability, it is crucial to develop novel outputs and facile chemical transformation methods. With this aim, in this study, we present a straightforward method for converting nucleic acid signals into fluorescein outputs that exhibit a wide range of functionalities. This operation is designed through a DNA-templated reaction based on riboflavin-photocatalyzed oxidation of dihydrofluorescein, which is readily prepared by simple NaBH4 reduction of the fluorescein with no complicated chemical caging steps. The templated photooxidation exhibits high efficiency (kapp = 2.7 × 10-3/s), generating a clear fluorescein output signal distinguishable from a low background, originating from the high stability of the synthesized dihydrofluorescein. This facile and efficient operation allows the nucleic acid-initiated activation of various fluorescein functions, such as fluorescence and artificial oxidase activity, which are applied in the design of novel bioanalytical systems, including fluorescent and colorimetric DNA sensors. The operation presented herein would expand the scope of biomolecular circuit systems for diagnostic and therapeutic applications.

A General Approach for the Modelling of Negative Feedback Physiological Control Systems.

Mathematical models can improve the understanding of physiological systems behaviour, which is a fundamental topic in the bioengineering field. Having a reliable model enables researchers to carry out in silico experiments, which require less time and resources compared to their in vivo and in vitro counterparts. This work's objective is to capture the characteristics that a nonlinear dynamical mathematical model should exhibit, in order to describe physiological control systems at different scales. The similarities among various negative feedback physiological systems have been investigated and a unique general framework to describe them has been proposed. Within such a framework, both the existence and stability of equilibrium points are investigated. The model here introduced is based on a closed-loop topology, on which the homeostatic process is based. Finally, to validate the model, three paradigmatic examples of physiological control systems are illustrated and discussed: the ultrasensitivity mechanism for achieving homeostasis in biomolecular circuits, the blood glucose regulation, and the neuromuscular reflex arc (also referred to as muscle stretch reflex). The results show that, by a suitable choice of the modelling functions, the dynamic evolution of the systems under study can be described through the proposed general nonlinear model. Furthermore, the analysis of the equilibrium points and dynamics of the above-mentioned systems are consistent with the literature.

Control of charge transport in electronically active systems towards integrated biomolecular circuits (IbC).

The miniaturization of traditional silicon-based electronics will soon reach its limitation as quantum tunneling and heat become serious problems at the several-nanometer scale. Crafting integrated circuits via self-assembly of electronically active molecules using a "bottom-up" paradigm provides a potential solution to these technological challenges. In particular, integrated biomolecular circuits (IbC) offer promising advantages to achieve this goal, as nature offers countless examples of functionalities entailed by self-assembly and examples of controlling charge transport at the molecular level within the self-assembled structures. To this end, the review summarizes the progress in understanding how charge transport is regulated in biosystems and the key redox-active amino acids that enable the charge transport. In addition, charge transport mechanisms at different length scales are also reviewed, offering key insights for controlling charge transport in IbC in the future.

Quantitative Steady-State Bounds in Biomolecular Circuits Due to Bounded Multi-Parametric Perturbations

Quantitative Steady-State Bounds in Biomolecular Circuits Due to Bounded Multi-Parametric Perturbations

Molecular convolutional neural networks with DNA regulatory circuits

Complex biomolecular circuits enabled cells with intelligent behaviour to survive before neural brains evolved. Since DNA computing was first demonstrated in the mid-1990s, synthetic DNA circuits in liquid phase have been developed as computational hardware to perform neural network-like computations that harness the collective properties of complex biochemical systems. However, scaling up such DNA-based neural networks to support more powerful computation remains challenging. Here we present a systematic molecular implementation of a convolutional neural network algorithm with synthetic DNA regulatory circuits based on a simple switching gate architecture. Our DNA-based weight-sharing convolutional neural network can simultaneously implement parallel multiply–accumulate operations for 144-bit inputs and recognize patterns in up to eight categories autonomously. Further, this system can be connected with other DNA circuits to construct hierarchical networks to recognize patterns in up to 32 categories with a two-step approach: coarse classification on language (Arabic numerals, Chinese oracles, English alphabets and Greek alphabets) followed by classification into specific handwritten symbols. We also reduced the computation time from hours to minutes by using a simple cyclic freeze–thaw approach. Our DNA-based regulatory circuits are a step towards the realization of a molecular computer with high computing power and the ability to classify complex and noisy information. Artificial DNA circuits that can perform neural network-like computations have been developed, but scaling up these circuits to recognize a large number of patterns is a challenging task. Xiong, Zhu and colleagues experimentally demonstrate a convolutional neural network algorithm using a synthetic DNA-based regulatory circuit in vitro and develop a freeze–thaw approach to reduce the computation time from hours to minutes, paving the way towards more powerful biomolecular classifiers.

What Is the Trait d’Union between Retroactivity and Molecular Communication Performance Limits?

Information exchange is a critical process in all communication systems, including biological ones. Retroactivity describes the load that downstream modules apply to their upstream systems in biological circuits. The motivation behind this work is that of integrating retroactivity, a concept proper of biochemical circuits, with the metrics defined in Information Theory and Digital Communications. This paper focuses on studying the impact of retroactivity on different biological signaling system models, which present analogies with well-known telecommunication systems. The mathematical analysis is performed both in the high and low molecular counts regime, by mean of the Chemical Master Equation and the Linear Noise Approximation, respectively. The main goal of this work is to provide analytical tools to maximize the reliable information exchange across different biomolecular circuit models. Results highlight how, in general, retroactivity harms communication performance. This negative effect can be mitigated by adding to the signaling circuit an independent upstream system that connects with the same pool of downstream circuits.

Advances in the Computational Design of Small-Molecule-Controlled Protein-Based Circuits for Synthetic Biology.

Synthetic biology approaches living systems with an engineering perspective and promises to deliver solutions to global challenges in healthcare and sustainability. A critical component is the design of biomolecular circuits with programmable input-output behaviors. Such circuits typically rely on a sensor module that recognizes molecular inputs, which is coupled to a functional output via protein-level circuits or regulating the expression of a target gene. While gene expression outputs can be customized relatively easily by exchanging the target genes, sensing new inputs is a major limitation. There is a limited repertoire of sensors found in nature, and there are often difficulties with interfacing them with engineered circuits. Computational protein design could be a key enabling technology to address these challenges, as it allows for the engineering of modular and tunable sensors that can be tailored to the circuit's application. In this article, we review recent computational approaches to design protein-based sensors for small-molecule inputs with particular focus on those based on the widely used Rosetta software suite. Furthermore, we review mechanisms that have been harnessed to couple ligand inputs to functional outputs. Based on recent literature, we illustrate how the combination of protein design and synthetic biology enables new sensors for diverse applications ranging from biomedicine to metabolic engineering. We conclude with a perspective on how strategies to address frontiers in protein design and cellular circuit design may enable the next generation of sense-response networks, which may increasingly be assembled from de novo components to display diverse and engineerable input-output behaviors.

Protocellular CRISPR/Cas‐Based Diffusive Communication Using Transcriptional RNA Signaling

AbstractProtocells containing enzyme‐driven biomolecular circuits that can process and exchange information offer a promising approach for mimicking cellular features and developing molecular information platforms. Here, we employ synthetic transcriptional circuits together with CRISPR/Cas‐based DNA processing inside semipermeable protein‐polymer microcompartments. We first establish a transcriptional protocell that can be activated by external DNA strands and produce functional RNA aptamers. Subsequently, we engineer a transcriptional module to generate RNA strands functioning as diffusive signals that can be sensed by neighboring protocells and trigger the activation of internalized DNA probes or localization of Cas nucleases. Our results highlight the opportunities to combine CRISPR/Cas machinery and DNA nanotechnology for protocellular communication and provide a step towards the development of protocells capable of distributed molecular information processing.

Protocellular CRISPR/Cas-Based Diffusive Communication Using Transcriptional RNA Signaling.

Protocells containing enzyme‐driven biomolecular circuits that can process and exchange information offer a promising approach for mimicking cellular features and developing molecular information platforms. Here, we employ synthetic transcriptional circuits together with CRISPR/Cas‐based DNA processing inside semipermeable protein‐polymer microcompartments. We first establish a transcriptional protocell that can be activated by external DNA strands and produce functional RNA aptamers. Subsequently, we engineer a transcriptional module to generate RNA strands functioning as diffusive signals that can be sensed by neighboring protocells and trigger the activation of internalized DNA probes or localization of Cas nucleases. Our results highlight the opportunities to combine CRISPR/Cas machinery and DNA nanotechnology for protocellular communication and provide a step towards the development of protocells capable of distributed molecular information processing.

Chaos and Its Degradation-Promoting-Based Control in an Antithetic Integral Feedback Circuit

This letter deals with a novel variant of antithetic integral feedback controller (AIFC) motifs which can feature robust perfect adaptation, a pervasive (desired) ability in natural (synthetic) biomolecular circuits, when coupled with a wide class of process networks to be regulated. Using the separation of time-scales in the proposed kind of AIFC, here we find a reduced-order controller that captures the governing slow part of the original solutions under suitable assumptions. Inspired by Rössler systems, we then make use of such a simpler controller to show that the antithetic circuit can exhibit chaotic behaviors with strange attractors, where the bifurcation from a homeostatic state to chaotic orbits can happen, e.g., when considering saturated Hill-type reactions for the actuation. Addition of degradation terms to the controller species, whether naturally due to dilution or exogenously using protein tags, is showcased by simulation results to be an effective solution in suppressing deterministic chaos and aperiodic oscillations. In the same vein, we recapitulate the recently introduced antithetic rein IFC motif and confirm that the promotion of degradation by a rein mechanism also can control chaos and improve the stability of closed-loop circuit.

Next generation whole-cell biosensors

Mammalian whole-cell biosensors (WCBs) are cells engineered with biomolecular circuits providing them with ability to sense and report on bioactive molecules, chemicals, or even pathogens (analytes) relevant to human health, that are present within the cell or in the surrounding microenvironment. Applications range from toxicology and drug discovery, to biomanufacturing and biosecurity. Current state-of-the-art whole-cell biosensors are highly dependent on the cell state and have several limitations such as sensitivity to analyte concentration, linear and dynamic range, and robustness to constant disturbances. Here, we introduce two novel designs for whole cell biosensors, based on a feed-forward loop or a negative feedback loop topologies, and perform mathematical modelling and analysis to compare their metrological features against the current state of the art biosensors.

Mapping parameter spaces of biological switches.

Since the seminal 1961 paper of Monod and Jacob, mathematical models of biomolecular circuits have guided our understanding of cell regulation. Model-based exploration of the functional capabilities of any given circuit requires systematic mapping of multidimensional spaces of model parameters. Despite significant advances in computational dynamical systems approaches, this analysis remains a nontrivial task. Here, we use a nonlinear system of ordinary differential equations to model oocyte selection in Drosophila, a robust symmetry-breaking event that relies on autoregulatory localization of oocyte-specification factors. By applying an algorithmic approach that implements symbolic computation and topological methods, we enumerate all phase portraits of stable steady states in the limit when nonlinear regulatory interactions become discrete switches. Leveraging this initial exact partitioning and further using numerical exploration, we locate parameter regions that are dense in purely asymmetric steady states when the nonlinearities are not infinitely sharp, enabling systematic identification of parameter regions that correspond to robust oocyte selection. This framework can be generalized to map the full parameter spaces in a broad class of models involving biological switches.

Synthetic Gene Circuit Analysis and Optimization.

Synthetic biology aims at engineering synthetic circuits with pre-defined target functions. From a systems (model-based) perspective, the following problems are of central importance: (1) given the model of a biomolecular circuit, elucidate whether it is capable of a certain behavior/functionality; and (2) starting from a pre-defined required functionality and a library of biological parts, find the biomolecular circuit that, built as a combination of such parts, achieves the desired behavior. These two problems, framed, respectively, as nonlinear analysis and automated design problems, are tackled here by efficient optimization methods. We illustrate these methods with case studies considering the analysis and design of biocircuits capable of bistability (bistable switches). Bistability is of particular interest in the context of systems and synthetic biology because it endows cells with the capacity to make decisions.

Experimental evidence for constraints in amplitude-timescale co-variation of a biomolecular pulse generating circuit design.

Understanding constraints on the functional properties of biomolecular circuit dynamics, such as the possible variations of amplitude and timescale of a pulse, is an important part of biomolecular circuit design. While the amplitude‐timescale co‐variations of the pulse in an incoherent feedforward loop have been investigated computationally using mathematical models, experimental support for any such constraints is relatively unclear. Here, the authors address this using experimental measurement of an existing pulse generating incoherent feedforward loop circuit realisation in the context of a standard mathematical model. They characterise the trends of co‐variation in the pulse amplitude and rise time computationally by randomly exploring the parameter space. They experimentally measured the co‐variation by varying inducers and found that larger amplitude pulses have a slower rise time. They discuss the gap between the experimental measurements and predictions of the standard model, highlighting model additions and other biological factors that might bridge the gap.

Biosensors for Biomolecular Computing: a Review and Future Perspectives

Biomolecular computing is the field of engineering where computation, storage, communication, and coding are obtained by exploiting interactions between biomolecules, especially DNA, RNA, and enzymes. They are a promising solution in a long-term vision, bringing huge parallelism and negligible power consumption. Despite significant efforts in taking advantage of the massive computational power of biomolecules, many issues are still open along the way for considering biomolecular circuits as an alternative or a complement to competing with complementary metal–oxide–semiconductor (CMOS) architectures. According to the Von Neumann architecture, computing systems are composed of a central processing unit, a storage unit, and input and output (I/O). I/O operations are crucial to drive and read the computing core and to interface it to other devices. In emerging technologies, the complexity overhead and the bottleneck of I/O systems are usually limiting factors. While computing units and memories based on biomolecular systems have been successfully presented in literature, the published I/O operations are still based on laboratory equipment without a real development of integrated I/O. Biosensors are suitable devices for transducing biomolecular interactions by converting them into electrical signals. In this work, we explore the latest advancements in biomolecular computing, as well as in biosensors, with focus on technology suitable to provide the required and still missing I/O devices. Therefore, our goal is to picture out the present and future perspectives about DNA, RNA, and enzymatic-based computing according to the progression in its I/O technologies, and to understand how the field of biosensors contributes to the research beyond CMOS.

Assessment of Robustness to Temperature in a Negative Feedback Loop and a Feedforward Loop.

Robustness to temperature variation is an important specification in biomolecular circuit design. While the cancellation of parametric temperature dependencies has been shown to improve the temperature robustness of the period in a synthetic oscillator design, the performance of other biomolecular circuit designs in different temperature conditions is relatively unclear. Using a combination of experimental measurements and mathematical models, we assessed the temperature robustness of two biomolecular circuit motifs-a negative feedback loop and a feedforward loop. We found that the measured responses of both the circuits changed with temperature, both in the amplitude and in the transient response. We also found that, in addition to the cancellation of parametric temperature dependencies, certain parameter regimes could facilitate the temperature robustness of the negative feedback loop, although at a performance cost. We discuss these parameter regimes in the context of the measured data for the negative feedback loop. These results should help develop a framework for assessing and designing temperature robustness in biomolecular circuits.

Dependence of bacterial growth rate on dynamic temperature changes.

Temperature is an important determinant of bacterial growth. While the dependence of bacterial growth on different temperatures has been well studied for many bacterial species, prediction of bacterial growth rate for dynamic temperature changes is relatively unclear. Here, the authors address this issue using a combination of experimental measurements of the growth, at the resolution of 5 min, of Escherichia coli and mathematical models. They measure growth curves at different temperatures and estimate model parameters to predict bacterial growth profiles subject to dynamic temperature changes. They compared these predicted growth profiles for various step‐like temperature changes with experimental measurements using the coefficient of determination and mean square error and based on this comparison, ranked the different growth models, finding that the generalised logistic growth model gave the smallest error. They note that as the maximum specific growth increases the duration of this growth predominantly decreases. These results provide a basis to compute the dependence of the growth rate parameter in biomolecular circuits on dynamic temperatures and may be useful for designing biomolecular circuits that are robust to temperature.

Computational Framework for Design of a Pulse Generating Biomolecular Circuit

The design of dynamics according to the quantitative specifications is widespread throughout engineering, and may be important for synthetic biology. While there have been multiple instances, experimental and theoretical, of design of dynamics in biomolecular circuits, the design as per quantitative specifications is relatively less common. Here we address this issue, computationally, for the design of pulse dynamics in multiple biomolecular circuits. We map the pulse specifications of rise time, peak amplitude and decay timescale exactly to the solution of linearized dynamics of incoherent feedforward loops by exploiting the non-normal structure of these dynamics. We find that there can be an error between the pulse specifications and the pulse output of the full nonlinear model and propose a bias factor so that the output almost exactly matches the specifications. We note the degeneracy of solutions possible for a given specification and analyse how robustness to parametric perturbations can guide the choice of solutions. These results present a framework for specification-based design of biomolecular pulses that provides insight into the design process and is scalable.

Engineering DNAzyme cascade for signal transduction and amplification.

Inspired by the natural enzyme cascade reaction, a multiple DNAzyme cascade platform is engineered to imitate the intracellular process of signal transduction and signal amplification. In this design, when particular stimuli appear, an activated upstream DNAzyme will cleave a well-designed intermediary S1, releasing a downstream DNAzyme that can cleave the reporter substrate S2 to output signals. Thus, the signal is passed from the upstream DNAzyme to the downstream DNAzyme through a well-designed intermediary, accomplishing signal transduction and signal amplification. According to the experimental results, the DNAzyme cascades are capable of improving sensitivity for bioassays compared with that for single DNAzyme-based biocatalysis, which holds promise for potential applications, such as biomolecular computing, logic circuits and precision medicine.

Antithetic integral feedback for the robust control of monostable and oscillatory biomolecular circuits

Biomolecular feedback systems are now a central application area of interest within control theory. While classical control techniques provide valuable insight into the function and design of both natural and synthetic biomolecular systems, there are certain aspects of biological control that have proven difficult to analyze with traditional methods. To this end, we describe here how the recently developed tools of dominance analysis can be used to gain insight into the nonlinear behavior of the antithetic integral feedback circuit, a recently discovered control architecture which implements integral control of arbitrary biomolecular processes using a simple feedback mechanism. We show that dominance theory can predict both monostability and periodic oscillations in the circuit, depending on the corresponding parameters and architecture. We then use the theory to characterize the robustness of the asymptotic behavior of this circuit in a nonlinear setting.