Turing, von Neumann, and the computational architecture of biological machines.

In the mid-1930s, the English mathematician and logician Alan Turing invented an imaginary machine which could emulate the process of manipulating finite symbolic configurations by human computers. His machine launched the field of computer science and provided a foundation for the modern-day programmable computer. A decade later, building on Turing's machine, the American-Hungarian mathematician John von Neumann invented an imaginary self-reproducing machine capable of open-ended evolution. Through his machine, von Neumann answered one of the deepest questions in Biology: Why is it that all living organisms carry a self-description in the form of DNA? The story behind how two pioneers of computer science stumbled on the secret of life many years before the discovery of the DNA double helix is not well known, not even to biologists, and you will not find it in biology textbooks. Yet, the story is just as relevant today as it was eighty years ago: Turing and von Neumann left a blueprint for studying biological systems as if they were computing machines. This approach may hold the key to answering many remaining questions in Biology and could even lead to advances in computer science.

Programming Non-Nucleic Acid Molecules into Computational Nucleic Acid Systems.

Nucleic acid (NA) computation has been widely developed in the past years to solve kinds of logic and mathematic issues in both information technologies and biomedical analysis. However, the difficulty to integrate non-NA molecules limits its power as a universal platform for molecular computation. Here, we report a versatile prototype of hybridized computation integrated with both nucleic acids and non-NA molecules. Employing the conformationally controlled ligand converters, we demonstrate that non-NA molecules, including both small molecules and proteins, can be computed as nucleic acid strands to construct the circuitry with increased complexity and scalability, and can be even programmed to solve arithmetical calculations within the computational nucleic acid system. This study opens a new door for molecular computation in which all-NA circuits can be expanded with integration of various ligands, and meanwhile, ligands can be precisely programmed by the nuclei acid computation.

Logical Analysis of Multiple Single-Nucleotide-Polymorphisms with Programmable DNA Molecular Computation for Clinical Diagnostics.

Analyzing complex single-nucleotide-polymorphism (SNP) combinations in the genome is important for research and clinical applications, given that different SNP combinations can generate different phenotypic consequences. Recent works have shown that DNA-based molecular computing is powerful for simultaneously sensing and analyzing complex molecular information. Here, we designed a switching circuit-based DNA computational scheme that can integrate the sensing of multiple SNPs and simultaneously perform logical analysis of the detected SNP information to directly report clinical outcomes. As a demonstration, we successfully achieved automatic and accurate identification of 21 different blood group genotypes from 83 clinical blood samples with 100 % accuracy compared to sequencing data in a more rapid manner (3 hours). Our method enables a new mode of automatic and logical sensing and analyzing subtle molecular information for clinical diagnosis, as well as guiding personalized medication.

RNA-Cleaving DNA Thresholder Controlled by Concentrations of miRNA Cancer Marker.

Oligonucleotide gene therapy (OGT) agents suppress specific mRNAs in cells and thus reduce the expression of targeted genes. The ability to unambiguously distinguish cancer from healthy cells can solve the low selectivity problem of OGT agents. Cancer RNA markers are expressed in both healthy and cancer cells with a higher expression level in cancer cells. We have designed a DNA-based construct, named DNA thresholder (DTh) that cleaves targeted RNA only at high concentrations of cancer marker RNA and demonstrates low cleavage activity at low marker concentrations. The RNA-cleaving activity can be adjusted within one order of magnitude of the cancer marker RNA concentration by simply redesigning DTh. Importantly, DTh recognizes cancer marker RNA, while cleaving targeted RNA; this offers a possibility to suppress vital genes exclusively in cancer cells, thus triggering their death. DTh is a prototype of computation-inspired molecular device for controlling gene expression and cancer treatment.

Manufacturing Reusable NAND Logic Gates and Their Initial Circuits for DNA Nanoprocessors.

DNA-based computers can potentially analyze complex sets of biological markers, thereby advancing diagnostics and the treatment of diseases. Despite extensive efforts, DNA processors have not yet been developed due, in part, to limitations in the ability to integrate available logic gates into circuits. We have designed a NAND gate, which is one of the functionally complete set of logic connectives. The gate's design avoids stem-loop-folded DNA fragments, and is capable of reusable operations in RNase H-containing buffer. The output of the gate can be translated into RNA-cleaving activity or a fluorescent signal produced either by a deoxyribozyme or a molecular beacon probe. Furthermore, three NAND-gate-forming DNA strands were crosslinked by click chemistry and purified in a simple procedure that allowed ≈1013 gates to be manufactured in 16 h, with a hands-on time of about 30 min. Two NAND gates can be joined into one association that performs a new logic function simply by adding a DNA linker strand. Approaches developed in this work could contribute to the development of biocompatible DNA logic circuits for biotechnological and medical applications.

Fluorescent Molecular Logic Gates Driven by Temperature and by Protons in Solution and on Solid.

Temperature-driven fluorescent NOT logic is demonstrated by exploiting predissociation in a 1,3,5-trisubstituted Δ2 -pyrazoline on its own and when grafted onto silica microparticles. Related Δ2 -pyrazolines become proton-driven YES and NOT logic gates on the basis of fluorescent photoinduced electron transfer (PET) switches. Additional PASS 1 and YES+PASS 1 logic gates on silica are also demonstrated within the same family. Beside these small-molecule systems, a polymeric molecular thermometer based on a benzofurazan-derivatized N-isopropylacrylamide copolymer is attached to silica to produce temperature-driven fluorescent YES logic.

Processing two environmental chemical signals with a synthetic genetic IMPLY gate, a 2-input-2-output integrated logic circuit, and a process pipeline to optimize its systems chemistry in Escherichia coli.

Synthetic genetic devices can perform molecular computation in living bacteria, which may sense more than one environmental chemical signal, perform complex signal processing in a human-designed way, and respond in a logical manner. IMPLY is one of the four fundamental logic functions and unlike others, it is an "IF-THEN" constraint-based logic. By adopting physical hierarchy of electronics in the realm of in-cell systems chemistry, a full-spectrum transcriptional cascaded synthetic genetic IMPLY gate, which senses and integrates two environmental chemical signals, is designed, fabricated, and optimized in a single Escherichia coli cell. This IMPLY gate is successfully integrated into a 2-input-2-output integrated logic circuit and showed higher signal-decoding efficiency. Further, we showed simple application of those devices by integrating them with an inherent cellular process, where we controlled the cell morphology and color in a logical manner. To fabricate and optimize the genetic devices, a new process pipeline named NETWORK Brick is developed. This pipeline allows fast parallel kinetic optimization and reduction in the unwanted kinetic influence of one DNA module over another. A mathematical model is developed and it shows that response of the genetic devices are digital-like and are mathematically predictable. This single-cell IMPLY gate provides the fundamental constraint-based logic and completes the in-cell molecular logic processing toolbox. The work has significance in the smart biosensor, artificial in-cell molecular computation, synthetic biology, and microbiorobotics.

An Intelligent DNA Nanorobot for Autonomous Anticoagulation.

Artificial nanorobots that can recognize molecular triggers and respond with programable operations provide an inspiring proof-of-principle for personalized theragnostic applications. We have constructed an intelligent DNA nanorobot for autonomous blood anticoagulation in human plasma. The DNA nanorobot comprises a barrel-shaped DNA nanostructure as the framework and molecular reaction cascades embedded as the computing core. This nanorobot can intelligently sense the concentration of thrombin in the local environment and trigger an autonomous anticoagulation when excess thrombin is present. The triggering concentration of thrombin at which the nanorobot responds can be tuned arbitrarily to avoid possible side effects induced by excess thrombin. This makes the nanorobot useful for autonomous anticoagulation in various medical scenarios and inspires a more efficient and safer strategy for future personalized medicine.

Anion Sensors as Logic Gates: A Close Encounter?

Computers have become smarter, smaller, and more efficient due to the downscaling of silicon-based components. Top-down miniaturisation of silicon-based computer components is fast reaching its limitations because of physical constraints and economical non-feasibility. Therefore, the possibility of a bottom-up approach that uses molecules to build nano-sized devices has been initiated. As a result, molecular logic gates based on chemical inputs and measurable optical outputs have captured significant attention very recently. In addition, it would be interesting if such molecular logic gates could be developed by making use of ion sensors, which can give significantly sensitive output information. This review provides a brief introduction to anion receptors, molecular logic gates, a comprehensive review on describing recent advances and progress on development of ion receptors for molecular logic gates, and a brief idea about the application of molecular logic gates.

Towards a DNA Nanoprocessor: Reusable Tile-Integrated DNA Circuits.

Modern electronic microprocessors use semiconductor logic gates organized on a silicon chip to enable efficient inter-gate communication. Here, arrays of communicating DNA logic gates integrated on a single DNA tile were designed and used to process nucleic acid inputs in a reusable format. Our results lay the foundation for the development of a DNA nanoprocessor, a small and biocompatible device capable of performing complex analyses of DNA and RNA inputs.

Bridging the Two Worlds: A Universal Interface between Enzymatic and DNA Computing Systems.

Molecular computing based on enzymes or nucleic acids has attracted a great deal of attention due to the perspectives of controlling living systems in the way we control electronic computers. Enzyme-based computational systems can respond to a great variety of small molecule inputs. They have the advantage of signal amplification and highly specific recognition. DNA computing systems are most often controlled by oligonucleotide inputs/outputs and are capable of sophisticated computing as well as controlling gene expressions. Here, we developed an interface that enables communication of otherwise incompatible nucleic-acid and enzyme-computational systems. The enzymatic system processes small molecules as inputs and produces NADH as an output. The NADH output triggers electrochemical release of an oligonucleotide, which is accepted by a DNA computational system as an input. This interface is universal because the enzymatic and DNA computing systems are independent of each other in composition and complexity.

DNA-programmed dynamic assembly of quantum dots for molecular computation.

Despite the widespread use of quantum dots (QDs) for biosensing and bioimaging, QD-based bio-interfaceable and reconfigurable molecular computing systems have not yet been realized. DNA-programmed dynamic assembly of multi-color QDs is presented for the construction of a new class of fluorescence resonance energy transfer (FRET)-based QD computing systems. A complete set of seven elementary logic gates (OR, AND, NOR, NAND, INH, XOR, XNOR) are realized using a series of binary and ternary QD complexes operated by strand displacement reactions. The integration of different logic gates into a half-adder circuit for molecular computation is also demonstrated. This strategy is quite versatile and straightforward for logical operations and would pave the way for QD-biocomputing-based intelligent molecular diagnostics.

Protein-to-DNA Converter with High Signal Gain.

DNA isothermal amplification techniques have been applied extensively for evaluating nucleic acid inputs but cannot be implemented directly on other types of biomolecules. In this work, we designed a proximity activation mechanism that converts protein input into DNA barcodes for the DNA exponential amplification reaction, which we termed PEAR. Several design parameters were identified and experimentally verified, which included the choice of enzymes, sequences of proximity probes and template strand via the NUPACK design tool, and the implementation of a hairpin lock on the proximity probe structure. Our PEAR system was surprisingly more robust against nonspecific DNA amplification, which is a major challenge faced in existing formats of the DNA-based exponential amplification reaction. The as-designed PEAR exhibited good target responsiveness for three protein models with a dynamic range of 4-5 orders of magnitude down to femtomolar input concentration. Overall, our proposed protein-to-DNA converter module led to the development of a stable and robust configuration of the DNA exponential amplification reaction to achieve high signal gain. We foresee this enabling the use of protein inputs for more complex molecular evaluation as well as ultrasensitive protein detection.

Thresholding DNA Nanomachine as a Highly Cooperative and Efficient Enzyme-Like System for Controlled RNA Cleavage.

Therapeutic nucleic acid agents (TNA) can be activated by a marker RNA sequence followed by initiation of targeted RNA cleavage. This property can be used in conditional cell suppression, e. g., cancer marker-dependent cell death. However, healthy cells often express lower levels of cancer markers, thus jeopardizing TNA activation exclusively in cancer cells. Earlier, we developed a conditionally activated split deoxyribozyme construct (DNA thresholder or DTh) that can be activated by high but not by low concentrations of cancer markers. It's activity, however, was suppressed by very high marker concentrations. Herein, we combine the DTh functional units in a single DNA association (Thresholding DNA nanomachine or Th-DNM). Th-DNM maintains a high level of RNA cleavage activity in the presence of marker concentrations above the threshold level. Th-DNM differentiated fully complementary miR17 markers sequence from double base mismatched miR-20. Th-DNM can become a building block of DNA nanorobots for cancer treatment.

The computational capabilities of many-to-many protein interaction networks.

Many biological circuits comprise sets of protein variants that interact with one another in a many-to-many, or promiscuous, fashion. These architectures can provide powerful computational capabilities that are especially critical in multicellular organisms. Understanding the principles of biochemical computations in these circuits could allow more precise control of cellular behaviors. However, these systems are inherently difficult to analyze, due to their large number of interacting molecular components, partial redundancies, and cell context dependence. Here, we discuss recent experimental and theoretical advances that are beginning to reveal how promiscuous circuits compute, what roles those computations play in natural biological contexts, and how promiscuous architectures can be applied for the design of synthetic multicellular behaviors.

The computational power of the human brain.

At the end of the 20th century, analog systems in computer science have been widely replaced by digital systems due to their higher computing power. Nevertheless, the question keeps being intriguing until now: is the brain analog or digital? Initially, the latter has been favored, considering it as a Turing machine that works like a digital computer. However, more recently, digital and analog processes have been combined to implant human behavior in robots, endowing them with artificial intelligence (AI). Therefore, we think it is timely to compare mathematical models with the biology of computation in the brain. To this end, digital and analog processes clearly identified in cellular and molecular interactions in the Central Nervous System are highlighted. But above that, we try to pinpoint reasons distinguishing in silico computation from salient features of biological computation. First, genuinely analog information processing has been observed in electrical synapses and through gap junctions, the latter both in neurons and astrocytes. Apparently opposed to that, neuronal action potentials (APs) or spikes represent clearly digital events, like the yes/no or 1/0 of a Turing machine. However, spikes are rarely uniform, but can vary in amplitude and widths, which has significant, differential effects on transmitter release at the presynaptic terminal, where notwithstanding the quantal (vesicular) release itself is digital. Conversely, at the dendritic site of the postsynaptic neuron, there are numerous analog events of computation. Moreover, synaptic transmission of information is not only neuronal, but heavily influenced by astrocytes tightly ensheathing the majority of synapses in brain (tripartite synapse). At least at this point, LTP and LTD modifying synaptic plasticity and believed to induce short and long-term memory processes including consolidation (equivalent to RAM and ROM in electronic devices) have to be discussed. The present knowledge of how the brain stores and retrieves memories includes a variety of options (e.g., neuronal network oscillations, engram cells, astrocytic syncytium). Also epigenetic features play crucial roles in memory formation and its consolidation, which necessarily guides to molecular events like gene transcription and translation. In conclusion, brain computation is not only digital or analog, or a combination of both, but encompasses features in parallel, and of higher orders of complexity.

Atomistic-Scale Energetic Heterogeneity on a Membrane Surface.

Knowing the energetic topology of a surface is important, especially with regard to membrane fouling. In this study, molecular computations were carried out to determine the energetic topology of a polyvinylidene fluoride (PVDF) membrane with different surface wettability and three representative probe molecules (namely argon, carbon dioxide and water) of different sizes and natures. Among the probe molecules, water has the strongest interaction with the PVDF surface, followed by carbon dioxide and then argon. Argon, which only has van der Waals interactions with PVDF, is a good probing molecule to identify crevices and the molecular profile of a surface. Carbon dioxide, which is the largest probing molecule and does not have dipole moment, exhibits similar van der Waals and electrostatic interactions. As for water, the dominant attractive interactions are electrostatics with fluorine atoms of the intrinsically hydrophobic PVDF membrane, but the electrostatic interactions are much stronger for the hydroxyl and carboxyl groups on the hydrophilic PVDF due to strong dipole moment. PVDF only becomes hydrophilic when the interaction energy is approximately doubled when grafted with hydroxyl and carboxyl groups. The energetic heterogeneity and the effect of different probe molecules revealed here are expected to be valuable in guiding membrane modifications to mitigate fouling.

AI in Measurement Science.

Measurement of biological systems containing biomolecules and bioparticles is a key task in the fields of analytical chemistry, biology, and medicine. Driven by the complex nature of biological systems and unprecedented amounts of measurement data, artificial intelligence (AI) in measurement science has rapidly advanced from the use of silicon-based machine learning (ML) for data mining to the development of molecular computing with improved sensitivity and accuracy. This review presents an overview of fundamental ML methodologies and discusses their applications in disease diagnostics, biomarker discovery, and imaging analysis. We next provide the working principles of molecular computing using logic gates and arithmetical devices, which can be employed for in situ detection, computation, and signal transduction for biological systems. This review concludes by summarizing the strengths and limitations of AI-involved biological measurement in fundamental and applied research.

Parallelizing Assignment Problem with DNA Strands.

BACKGROUND: Many problems of combinatorial optimization, which are solvable only in exponential time, are known to be Non-Deterministic Polynomial hard (NP-hard). With the advent of parallel machines, new opportunities have been emerged to develop the effective solutions for NP-hard problems. However, solving these problems in polynomial time needs massive parallel machines and is not applicable up to now. OBJECTIVES: DNA (Deoxyribonucleic acid) computing provides a fantastic method to solve NP-hard problems in polynomial time. Accordingly, one of the famous NP-hard problems is assignment problem, which is designed to find the best assignment of n jobs to n persons in a way that it could maximize the profit or minimize the cost. MATERIAL AND METHODS: Applying bio molecular operations of Adelman Lipton model, a novel parallel DNA algorithm have been proposed for solving the assignment problem. RESULTS: The proposed algorithm can solve the problem in time complexity, and just O(n2) initial DNA strand in comparison with nn initial sequence, which is used by the other methods. CONCLUSIONS: In this article, using DNA computing, we proposed a parallel DNA algorithm to solve the assignment problem in linear time.

DNA-Based Analog Computing.

The field of DNA computation makes use of DNA reactions to do molecular-scale computation. Most works in DNA computation execute digital computations such as evaluation of Boolean circuits. This chapter surveys novel DNA computation methods that execute analog computations, where the inputs and outputs are real values specified by the concentrations of particular DNA strands.