Boolean Gates Research Articles

RNAi-based Boolean gates in the yeast Saccharomyces cerevisiae.

Boolean gates, the fundamental components of digital circuits, have been widely investigated in synthetic biology because they permit the fabrication of biosensors and facilitate biocomputing. This study was conducted to design and construct Boolean gates in the yeast Saccharomyces cerevisiae, the main component of which was the RNA interference pathway (RNAi) that is naturally absent from the budding yeast cells. We tested different expression cassettes for the siRNA precursor (a giant hairpin sequence, a DNA fragment-flanked by one or two introns-between convergent promoters or transcribed separately in the sense and antisense directions) and placed different components under the control of the circuit inputs (i.e., the siRNA precursor or proteins such as the Dicer and the Argonaute). We found that RNAi-based logic gates are highly sensitive to promoter leakage and, for this reason, challenging to implement in vivo. Convergent-promoter architecture turned out to be the most reliable solution, even though the overall best performance was achieved with the most difficult design based on the siRNA precursor as a giant hairpin.

Design and engineering of logic genetic-enzymatic gates based on the activity of the human CYP2C9 enzyme in permeabilized Saccharomyces cerevisiae cells

Gene circuits allow cells to carry out complex functions such as the precise regulation of biological metabolic processes. In this study, we combined, in the yeast S. cerevisiae, genetic regulatory elements with the enzymatic reactions of the human CYP2C9 and its redox partner CPR on luciferin substrates and diclofenac. S. cerevisiae cells were permeabilized and used as enzyme bags in order to host these metabolic reactions. We engineered three different (genetic)-enzymatic basic Boolean gates (YES, NOT, and N-IMPLY). In the YES and N-IMPLY gates, human CYP2C9 was expressed under the galactose-inducible GAL1 promoter. The carbon monoxide releasing molecule CORM-401 was used as an input in the NOT and N-IMPLY gates to impair CYP2C9 activity through inhibition of the Fe+2- heme prosthetic group in the active site of the human enzyme. Our study provides a new approach in designing synthetic bio-circuits and optimizing experimental conditions to favor the heterologous expression of human drug metabolic enzymes over their endogenous counterparts. This new approach will help study precise metabolic attributes of human P450s.

Complex chemical reaction networks for future information processing.

Tackling the increasing energy demand of our society is one of the key challenges today. With the rise of artificial intelligence, information and communication technologies started to substantially contribute to this alarming trend and therefore necessitate more sustainable approaches for the future. Brain-inspired computing paradigms represent a radically new and potentially more energy-efficient approach for computing that may complement or even replace CMOS in the long term. In this perspective, we elaborate on the concepts and properties of complex chemical reaction networks (CRNs) that may serve as information-processing units based on chemical reactions. The computational capabilities of simpler, oscillatory chemical reactions have already been demonstrated in scenarios ranging from the emulation of Boolean gates to image-processing tasks. CRNs offer higher complexity and larger non-linearity, potentially at lower energy consumption. Key challenges for the successful development of CRN-based computers are associated with their specific physical implementations, operability, and readout modalities. CRNs are sensible to various reaction triggers, and provide multiple and interlinked reaction pathways and a diverse compound space. This bears a high potential to build radically new hardware and software concepts for energy-efficient computing based on neuromorphic architectures-with computing capabilities in real-world applications yet to be demonstrated.

Secure Genomic String Search with Parallel Homomorphic Encryption

Fully homomorphic encryption (FHE) cryptographic systems enable limitless computations over encrypted data, providing solutions to many of today’s data security problems. While effective FHE platforms can address modern data security concerns in unsecure environments, the extended execution time for these platforms hinders their broader application. This project aims to enhance FHE systems through an efficient parallel framework, specifically building upon the existing torus FHE (TFHE) system chillotti2016faster. The TFHE system was chosen for its superior bootstrapping computations and precise results for countless Boolean gate evaluations, such as AND and XOR. Our first approach was to expand upon the gate operations within the current system, shifting towards algebraic circuits, and using graphics processing units (GPUs) to manage cryptographic operations in parallel. Then, we implemented this GPU-parallel FHE framework into a needed genomic data operation, specifically string search. We utilized popular string distance metrics (hamming distance, edit distance, set maximal matches) to ascertain the disparities between multiple genomic sequences in a secure context with all data and operations occurring under encryption. Our experimental data revealed that our GPU implementation vastly outperforms the former method, providing a 20-fold speedup for any 32-bit Boolean operation and a 14.5-fold increase for multiplications.This paper introduces unique enhancements to existing FHE cryptographic systems using GPUs and additional algorithms to quicken fundamental computations. Looking ahead, the presented framework can be further developed to accommodate more complex, real-world applications.

Flexible nanomechanical bit based on few-layer graphene.

Mechanical computers have gained intense research interest at size scales ranging from nano to macro as they may complement electronic computers operating in extreme environments. While nanoscale mechanical computers may be easier to integrate with traditional electronic components, most current nanomechanical computers are based on volatile resonator systems that require continuous energy input. In this study, we propose a non-volatile nanomechanical bit based on the quasi-stable configurations of few-layer graphene with void defects, and demonstrate its multiple quasi-stable states by deriving an analytic relationship for the void configuration based on a competition between the bending energy and the cohesive energy. Using this nanomechanical bit, typical logic gates are constructed to perform Boolean calculations, including NOT, AND, OR, NAND and NOR gates, and demonstrate reprogrammability between these logic gates. We also study the accuracy and the stability of the nanomechanical bits based on the few-layer graphene. These findings provide a novel approach to realize the nanomechanical computing process.

Light-induced spiking in proteinoids yields Boolean gates

This study analyzes light modulation of spiking in proteinoids, thermally condensed proteins exhibiting spike dynamics. We elucidate how light induces changes in proteinoids, modulating spike frequency. Results show proteinoids' spiking can be adjusted by light intensity and wavelength. Structural investigations demonstrate light triggers conformational changes influencing spiking kinetics. Spiking proteinoids can perform Boolean logic, generating programmable expressions in response to light input modifications. This reveals potential unconventional computing applications. Our comprehensive analyses establish a fundamental understanding of light's impact on proteinoids' structure and protrusion dynamics, facilitating optically programmed bio-logic gates. Outcomes will catalyze future research into light-modified proteinoids for information processing and unconventional computation. In summary, this study provides key insights into light modulation of proteinoids' spiking behaviour, enabling novel optically controlled bio-logic gate operations and motivating continued efforts to integrate photo-responsive proteinoids in bioinspired computational systems.

Hybrid Boolean gates show that Cas12c controls transcription activation effectively in the yeast S. cerevisiae.

Among CRISPR-Cas systems, type V CRISPR-Cas12c is of significant interest because Cas12c recognizes a very simple PAM (TN) and has the ability to silence gene expression without cleaving the DNA. We studied how new transcription factors for the yeast Saccharomyces cerevisiae can be built on Cas12c. We found that, upon fusion to a strong activation domain, Cas12c is an efficient activator. Its functionality was proved as a component of hybrid Boolean gates, i.e., logic circuits that mix transcriptional and translational control (the latter reached via tetracycline-responsive riboswitches). Moreover, Cas12c activity can be strongly inhibited by the anti-CRISPR AcrVA1 protein. Thus, Cas12c has the potential to be a new tool to control the activation of gene expression within yeast synthetic gene circuits.

Multi-key fully homomorphic encryption from NTRU and (R)LWE with faster bootstrapping

Multi-Key Fully Homomorphic Encryption (MKFHE) supports homomorphic evaluation on ciphertexts encrypted under different keys, guaranteeing data privacy in the case of multiple mutually untrusted parties. In ASIACRYPT 2019, Chen et al. designed an LWE-based MKFHE scheme, which evaluates a boolean gate on ciphertexts followed by a bootstrapping procedure in the dynamic multi-key setting. In this paper, we propose a new MKFHE scheme from NTRU and (R)LWE, achieving better bootstrapping performance. To that end, we adapt Chen et al.'s bootstrapping algorithm to the NTRU setting and construct an efficient conversion from multi-key NTRU to multi-key RLWE ciphertexts. With appropriate parameter choice, our scheme avoids the sublattice attacks that broke previous NTRU-based MKFHE schemes. Finally, we implement a proof-of-concept of our construction and conduct experiments to test its performance. Our experiments indicate that gate bootstrapping in our scheme for 2 parties takes 0.18 seconds, which is faster than with Chen et al. by around 5.3 times, but unfortunately does not scale beyond that. It is however practical for uses such as oblivious neural network inference.

Choose your tools carefully: a comparative evaluation of deterministic vs. stochastic and binary vs. analog neuron models for implementing emerging computing paradigms

Neuromorphic computing, commonly understood as a computing approach built upon neurons, synapses, and their dynamics, as opposed to Boolean gates, is gaining large mindshare due to its direct application in solving current and future computing technological problems, such as smart sensing, smart devices, self-hosted and self-contained devices, artificial intelligence (AI) applications, etc. In a largely software-defined implementation of neuromorphic computing, it is possible to throw enormous computational power or optimize models and networks depending on the specific nature of the computational tasks. However, a hardware-based approach needs the identification of well-suited neuronal and synaptic models to obtain high functional and energy efficiency, which is a prime concern in size, weight, and power (SWaP) constrained environments. In this work, we perform a study on the characteristics of hardware neuron models (namely, inference errors, generalizability and robustness, practical implementability, and memory capacity) that have been proposed and demonstrated using a plethora of emerging nano-materials technology-based physical devices, to quantify the performance of such neurons on certain classes of problems that are of great importance in real-time signal processing like tasks in the context of reservoir computing. We find that the answer on which neuron to use for what applications depends on the particulars of the application requirements and constraints themselves, i.e., we need not only a hammer but all sorts of tools in our tool chest for high efficiency and quality neuromorphic computing.

Integration of multiple stress signals in plants using synthetic Boolean logic gates.

As photosynthetic organisms, plants have a potential role in the sustainable production of high-value products such as medicines, biofuels, and chemical feedstocks. With effective engineering using synthetic biology approaches, plant-based platforms could conceivably be designed to minimize the costs and waste of production for materials that would otherwise be uneconomical. Additionally, modern agricultural crops could be engineered to be more productive, resilient, or restorative in different or rapidly changing environments and climates. Information-processing genetic devices and circuits containing multiple interacting parts that behave predictably must be developed to achieve these complex goals. A genetic Boolean AND logic gate is a device that computes the presence or absence of 2 inputs (signals and stimuli) and produces an output (response) only when both inputs are present. We optimized individual genetic components and used synthetic protein heterodimerizing domains to rationally assemble genetic AND logic gates that integrate 2 hormonal inputs in transgenic Arabidopsis thaliana plants. These AND gates produce an output only in the presence of both abscisic acid and auxin but not when either or neither hormone is present. The AND logic gate can also integrate signals resulting from 2 plant stresses, cold temperature and bacterial infection, to produce a response. The design principles used here are generalizable, and, therefore, multiple orthogonal AND gates could be assembled and rationally layered to process complex genetic information in plants. These layered logic gates may be used in genetic circuits to probe fundamental questions in plant biology, such as hormonal crosstalk, in addition to plant engineering for bioproduction.

CRISPR-associated type V proteins as a tool for controlling mRNA stability in S. cerevisiae synthetic gene circuits.

Type V-A CRISPR-(d)Cas system has been used in multiplex genome editing and transcription regulation in both eukaryotes and prokaryotes. However, mRNA degradation through the endonuclease activity of Cas12a has never been studied. In this work, we present an efficient and powerful tool to induce mRNA degradation in the yeast Saccharomyces cerevisiae via the catalytic activity of (d)Cas12a on pre-crRNA structure. Our results point out that dFnCas12a, (d)LbCas12a, denAsCas12a and two variants (which carry either NLSs or NESs) perform significant mRNA degradation upon insertion of pre-crRNA fragments into the 5'- or 3' UTR of the target mRNA. The tool worked well with two more Cas12 proteins-(d)MbCas12a and Casϕ2-whereas failed by using type VI LwaCas13a, which further highlights the great potential of type V-A Cas proteins in yeast. We applied our tool to the construction of Boolean NOT, NAND, and IMPLY gates, whose logic operations are fully based on the control of the degradation of the mRNA encoding for a reporter protein. Compared to other methods for the regulation of mRNA stability in yeast synthetic gene circuits (such as RNAi and riboswitches/ribozymes), our system is far easier to engineer and ensure very high performance.

Logic Circuits Based on 2A Peptide Sequences in the Yeast Saccharomyces cerevisiae.

Gene digital circuits are the subject of many studies in Synthetic Biology due to their various applications from pollutant detection to medical diagnostics and biocomputing. Complex logic functions are calculated via small genetic components that mimic Boolean gates, i.e., they implement basic logic operations. Gates interact by exchanging proteins or noncoding RNAs. To carry out logic operations in the yeast Saccharomyces cerevisiae, we chose three bacterial repressors commonly used for proofs of concept in Synthetic Biology, namely, TetR, LexA, and LacI. We coexpressed them via synthetic polycistronic cassettes based on 2A peptide sequences. Our initial results highlighted the successful application of four 2A peptides─from Equine rhinitis B virus-1 (ERBV-1 2A), Operophtera brumata cypovirus 18 (OpbuCPV18 2A), Ljungan virus (LV2A), and Thosea asigna virus (T2A)─to the construction of single and two-input Boolean gates. In order to improve protein coexpression, we modified the original 2A peptides with the addition of the glycine-serine-glycine (GSG) prefix or by using two different 2As sequences in tandem. Remarkably, we finally realized a well-working tri-cistronic vector that carried LexA-HBD(hER), TetR, and LacI separated, in the order, by GSG-T2A and ERBV-1 2A. This plasmid led to the implementation of three-input circuits containing AND and OR gates. Taken together, polycistronic constructs simplify the cloning and coexpression of multiple proteins with a dramatic reduction in the complexity of gene digital circuits.

Logical computation with self-assembling electric circuits.

Inspired by self-assembled biological growth, the Circuit Tile Assembly Model (cTAM) was developed to provide insights into signal propagation, information processing, and computation in bioelectric networks. The cTAM is an abstract model that produces a family of circuits of different sizes that is amenable to exact analysis. Here, the cTAM is extended to the Boolean Circuit Tile Assembly Model (bcTAM) that implements a computationally complete set of Boolean gates through self-assembled and self-controlled growth. The proposed model approximates axonal growth in neural networks and thus, investigates the computational capability of dynamic biological networks, for example, in growing networks of axons. Thus, the bcTAM models the effect of electrical activity on growth and shows how that growth might implement Boolean computations. In this sense, given a set of input voltages, the bcTAM is a system that is able to monitor and make decisions about its own growth.

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins.

Synthetic gene Boolean gates and digital circuits have a broad range of applications, from medical diagnostics to environmental care. The discovery of the CRISPR-Cas systems and their natural inhibitors-the anti-CRISPR proteins (Acrs)-provides a new tool to design and implement in vivo gene digital circuits. Here, we describe a protocol that follows the idea of the "Design-Build-Test-Learn" biological engineering cycle and makes use of dCas9/dCas12a together with their corresponding Acrs to establish small transcriptional networks, some of which behave like Boolean gates, in Saccharomyces cerevisiae. These results point out the properties of dCas9/dCas12a as transcription factors. In particular, to achieve maximal activation of gene expression, dSpCas9 needs to interact with an engineered scaffold RNA that collects multiple copies of the VP64 activation domain (AD). In contrast, dCas12a shall be fused, at the C terminus, with the strong VP64-p65-Rta (VPR) AD. Furthermore, the activity of both Cas proteins is not enhanced by increasing the amount of sgRNA/crRNA in the cell. This article also explains how to build Boolean gates based on the CRISPR-dCas-Acr interaction. The AcrIIA4 fused hormone-binding domain of the human estrogen receptor is the core of a NOT gate responsive to β-estradiol, whereas AcrVAs synthesized by the inducible GAL1 promoter permits to mimic both YES and NOT gates with galactose as an input. In the latter circuits, AcrVA5, together with dLbCas12a, showed the best logic behavior.

Design of Gene Boolean Gates and Circuits with Convergent Promoters.

Gene digital circuits are the subject of many research works due to their various potential applications, from hazard detection to medical diagnostic. Moreover, a remarkable number of techniques, developed in electronics, can be used for the construction of biological digital systems. In our previous works, we showed how to automatize the design and modeling of gene digital circuits whose gates were based on transcription and translation regulation. In this chapter, we illustrate how Boolean gates could be implemented by following a particular architecture, the convergent promoter one, rather diffuse in nature but seldom adopted in Synthetic Biology. Beside gate design, we also explain how to extend our previous modeling approach, based on composable parts and pools of molecules, to quantitatively describe and simulate this particular kind of digital biological devices.

Implementing and morphing Boolean gates with adaptive synchronization: The case of spiking neurons

Boolean logic is the paradigm through which modern computation is performed in silica. When nonlinear dynamical systems are interacting in a directed graph, we show that computation abilities emerge spontaneously from adaptive synchronization, which actually can emulate Boolean logic. Precisely, we demonstrate that a single dynamical unit, a spiking neuron modeled by the Hodgkin-Huxley model, can be used as the basic computational unit for realizing all the 16 Boolean logical gates with two inputs and one output, when it is coupled adaptively in a way that depends on the synchronization level between the two input signals. This is realized by means of a set of parameters, whose tuning offers even the possibility of constructing a morphing gate, i.e., a logical gate able to switch efficiently from one to another of such 16 Boolean gates. Extensive simulations demonstrate the efficiency and the accuracy of the proposed computational paradigm.

Chemical Wave Computing from Labware to Electrical Systems

Unconventional and, specifically, wave computing has been repeatedly studied in laboratory based experiments by utilizing chemical systems like a thin film of Belousov–Zhabotinsky (BZ) reactions. Nonetheless, the principles demonstrated by this chemical computer were mimicked by mathematical models to enhance the understanding of these systems and enable a more detailed investigation of their capacity. As expected, the computerized counterparts of the laboratory based experiments are faster and less expensive. A further step of acceleration in wave-based computing is the development of electrical circuits that imitate the dynamics of chemical computers. A key component of the electrical circuits is the memristor which facilitates the non-linear behavior of the chemical systems. As part of this concept, the road-map of the inspiration from wave-based computing on chemical media towards the implementation of equivalent systems on oscillating memristive circuits was studied here. For illustration reasons, the most straightforward example was demonstrated, namely the approximation of Boolean gates.

Sorting Short Keys in Circuits of Size ${o(n \log n)}$

We consider the classical problem of sorting an input array containing $n$ elements, where each element is described with a $k$-bit comparison-key and a $w$-bit payload. A long-standing open problem is whether there exist $(k + w) \cdot o(n \log n)$-sized boolean circuits for sorting. We show that one can overcome the $n\log n$ barrier when the keys to be sorted are short. Specifically, we prove that there is a circuit with $(k + w) \cdot O(n k) \cdot \poly(\log^*n - \log^* (w + k))$ boolean gates capable of sorting any input array containing $n$ elements, each described with a $k$-bit key and a $w$-bit payload. Therefore, if the keys to be sorted are short, say, $k < o(\log n)$, our result is asymptotically better than the classical AKS sorting network (ignoring $\poly\log^*$ terms); and we also overcome the $n \log n$ barrier in such cases. Such a result might be surprising initially because it is long known that comparator-based techniques must incur $\Omega(n \log n)$ comparator gates even when the keys to be sorted are only $1$-bit long (e.g., see Knuth's Art of Programming textbook). To the best of our knowledge, we are the first to achieve non-trivial results for sorting circuits using non-comparison-based techniques. We also show that if the Li-Li network coding conjecture is true, our upper bound is optimal, barring $\poly\log^*$ terms, for every $k$ as long as $k = O(\log n)$.

NOT Gates Based on Protein Degradation as a Case Study for a New Modular Modeling via SBML Level 3-Comp Package.

In 2008, we were among the first to propose a method for the visual design and modular modeling of synthetic gene circuits, mimicking the way electronic circuits are realized in silico. Basic components were DNA sequences that could be composed, first, into transcription units (TUs) and, then, circuits by exchanging fluxes of molecules, such as PoPS (polymerase per second) and RiPS (ribosomes per seconds) as suggested by Drew Endy. However, it became clear soon that such fluxes were not measurable, which highlighted the limit of using some concepts from electronics to represent biological systems. SBML Level 3 with the comp package permitted us to revise circuit modularity, especially for the modeling of eukaryotic networks. By using the libSBML Python API, TUs—rather than single parts—are encoded in SBML Level 3 files that contain species, reactions, and ports, i.e., the interfaces that permit to wire TUs into circuits. A circuit model consists of a collection of SBML Level 3 files associated with the different TUs plus a “main” file that delineates the circuit structure. Within this framework, there is no more need for any flux of molecules. Here, we present the SBML Level 3-based models and the wet-lab implementations of Boolean NOT gates that make use, in the yeast Saccharomyces cerevisiae, of the bacterial ClpX-ClpP system for protein degradation. This work is the starting point towards a new piece of software for the modular design of eukaryotic gene circuits and shows an alternative way to build genetic Boolean gates.

Fuzzy probability and α‐cut based‐fault tree analysis approach to evaluate the reliability and safety of complex engineering systems

AbstractReliability and safety are very critical in complex engineering systems such as chemical and nuclear industries. Fault tree analysis (FTA) has been widely applied to evaluate the reliability and safety of complex engineering systems. This approach assumes that systems always have exact component failure probabilities. However, this assumption is not always true in real‐world applications. To deal with this, a number of scholars have integrated fuzzy approach into FTA of engineering systems whose component exact probabilities cannot be collected. Furthermore, simple fuzzy arithmetic operations on membership function have also been proposed to quantify Boolean gates for propagating uncertainties from basic event fuzzy probabilities into the top event fuzzy probabilities. In this study, fuzzy arithmetic operations on α‐cuts, as opposed to simple fuzzy arithmetic operations, are proposed. The significance of the α‐cut is that each α‐cut can uniquely represents each fuzzy probability. In order to verify the applicability of the proposed approach, the reliability of the long‐term cooling system of the Westinghouse AP1000 to mitigate a large break loss of coolant accident is evaluated and the results are compared to the reliability of the same system quantified by conventional FTA published in qualified scientific journals. The results of the benchmarking confirm that the proposed approach can be practicably implemented to estimate the reliability and safety of complex engineering systems whose basic event reliability are characterized by fuzzy probabilities. To be applied in other complex engineering systems, safety analysts have to concern for some steps, which are particularly developed for a specific application, such as failure possibility distribution and its corresponding fuzzy probability distribution and the selection of a defuzzification technique.