Logic Circuits Based on Extended Molecular Spider Systems

Spatial locality brings the advantages of computation speed-up and sequence reuse to molecular computing. In particular, molecular walkers that undergo localized re- actions are of interest for implementing logic computations at the nanoscale. We use molecular spider walkers to implement logic circuits. We develop an extended multi- spider model with a dynamic environment wherein signal transmission is triggered via localized reactions, and use this model to implement three basic gates (AND, OR, and NOT) and a cascading mechanism. We develop an algorithm to automatically generate the layout of the circuit. We use a kinetic Monte Carlo algorithm to simulate circuit computations, and we analyze circuit complexity: our design scales linearly with formula size and has a logarithmic time complexity

Design Of Dna Strand Displacement Based Circuits

DNA is the basic building block of any living organism. DNA is considered a popular candidate for future biological devices and circuits for solving genetic disorders and several other medical problems. With this objective in mind, this research aims at developing novel approaches for the design of DNA based circuits. There are many recent developments in the medical field such as the development of biological nanorobots, SMART drugs, and CRISPR-Cas9 technologies. There is a strong need for circuits that can work with these technologies and devices. DNA is considered a suitable candidate for designing such circuits because of the programmability of the DNA strands, small size, lightweight, known thermodynamics, higher parallelism, and exponentially reducing the cost of synthesizing techniques. The DNA strand displacement operation is useful in developing circuits with DNA strands. The circuit can be either a digital circuit, in which the logic high and logic low states of the DNA strand concentrations are considered as the signal, or it can be an analog circuit in which the concentration of the DNA strands itself will act as the signal. We developed novel approaches in this research for the design of digital, as well as analog circuits keeping in view of the number of DNA strands required for the circuit design. Towards this goal in the digital domain, we developed spatially localized DNA majority logic gates and an inverter logic gate that can be used with the existing seesaw based logic gates. The majority logic gates proposed in this research can considerably reduce the number of strands required in the design. The introduction of the logic inverter operation can translate the dual rail circuit architecture into a monorail architecture for the seesaw based logic circuits. It can also reduce the number of unique strands required for the design into approximately half. The reduction in the number of unique strands will consequently reduce the leakage reactions, circuit complexity, and cost associated with the DNA circuits. The real world biological inputs are analog in nature. If we can use those analog signals directly in the circuits, it can considerably reduce the resources required. Even though analog circuits are highly prone to noise, they are a perfect candidate for performing computations in the resource-limited environments, such as inside the cell. In the analog domain, we are developing a novel fuzzy inference engine using analog circuits such as the minimum gate, maximum gate, and fan-out gates. All the circuits discussed in this research were designed and tested in the Visual DSD software. The biological inputs are inherently fuzzy in nature, hence a fuzzy based system can play a vital role in future decision-making circuits. We hope that our research will be the first step towards realizing these larger goals. The ultimate aim of our research is to develop novel approaches for the design of circuits which can be used with the future biological devices to tackle many medical problems such as genetic disorders

Integrated Control of Microfluidics – Application in Fluid Routing, Sensor Synchronization, and Real-Time Feedback Control

Microfluidic applications range from combinatorial chemical synthesis to high-throughput screening, with platforms integrating analog perfusion components, digitally controlled microvalves, and a range of sensors that demand a variety of communication protocols. A comprehensive solution for microfluidic control has to support an arbitrary combination of microfluidic components and to meet the demand for easy-to-operate system as it arises from the growing community of unspecialized microfluidics users. It should also be an easy to modify and extendable platform, which offer an adequate computational resources, preferably without a need for a local computer terminal for increased mobility. Here we will describe several implementation of microfluidics control technologies and propose a microprocessor-based unit that unifies them. Integrated control can streamline the generation process of complex perfusion sequences required for sensor-integrated microfluidic platforms that demand iterative operation procedures such as calibration, sensing, data acquisition, and decision making. It also enables the implementation of intricate optimization protocols, which often require significant computational resources. System integration is an imperative developmental milestone for the field of microfluidics, both in terms of the scalability of increasingly complex platforms that still lack standardization, and the incorporation and adoption of emerging technologies in biomedical research. Here we describe a modular integration and synchronization of a complex multicomponent microfluidic platform

Computers from plants we never made. Speculations

We discuss possible designs and prototypes of computing systems that could be based on morphological development of roots, interaction of roots, and analog electrical computation with plants, and plant-derived electronic components. In morphological plant processors data are represented by initial configuration of roots and configurations of sources of attractants and repellents; results of computation are represented by topology of the roots' network. Computation is implemented by the roots following gradients of attractants and repellents, as well as interacting with each other. Problems solvable by plant roots, in principle, include shortest-path, minimum spanning tree, Voronoi diagram, $\alpha$-shapes, convex subdivision of concave polygons. Electrical properties of plants can be modified by loading the plants with functional nanoparticles or coating parts of plants of conductive polymers. Thus, we are in position to make living variable resistors, capacitors, operational amplifiers, multipliers, potentiometers and fixed-function generators. The electrically modified plants can implement summation, integration with respect to time, inversion, multiplication, exponentiation, logarithm, division. Mathematical and engineering problems to be solved can be represented in plant root networks of resistive or reaction elements. Developments in plant-based computing architectures will trigger emergence of a unique community of biologists, electronic engineering and computer scientists working together to produce living electronic devices which future green computers will be made of.Comment: The chapter will be published in "Inspired by Nature. Computing inspired by physics, chemistry and biology. Essays presented to Julian Miller on the occasion of his 60th birthday", Editors: Susan Stepney and Andrew Adamatzky (Springer, 2017

Principles of genetic circuit design

Cells navigate environments, communicate and build complex patterns by initiating gene expression in response to specific signals. Engineers seek to harness this capability to program cells to perform tasks or create chemicals and materials that match the complexity seen in nature. This Review describes new tools that aid the construction of genetic circuits. Circuit dynamics can be influenced by the choice of regulators and changed with expression 'tuning knobs'. We collate the failure modes encountered when assembling circuits, quantify their impact on performance and review mitigation efforts. Finally, we discuss the constraints that arise from circuits having to operate within a living cell. Collectively, better tools, well-characterized parts and a comprehensive understanding of how to compose circuits are leading to a breakthrough in the ability to program living cells for advanced applications, from living therapeutics to the atomic manufacturing of functional materials.National Institute of General Medical Sciences (U.S.) (Grant P50 GM098792)National Institute of General Medical Sciences (U.S.) (Grant R01 GM095765)National Science Foundation (U.S.). Synthetic Biology Engineering Research Center (EEC0540879)Life Technologies, Inc. (A114510)National Science Foundation (U.S.). Graduate Research FellowshipUnited States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant 4500000552

Simulation Approach for Timing Analysis of Genetic Logic Circuits

Constructing genetic logic circuits is an application of synthetic biology in which parts of the DNA of a living cell are engineered to perform a dedicated Boolean function triggered by an appropriate concentration of certain proteins or by different genetic components. These logic circuits work in a manner similar to electronic logic circuits, but they are much more stochastic and hence much harder to characterize. In this article, we introduce an approach to analyze the threshold value and timing of genetic logic circuits. We show how this approach can be used to analyze the timing behavior of single and cascaded genetic logic circuits. We further analyze the timing sensitivity of circuits by varying the degradation rates and concentrations. Our approach can be used not only to characterize the timing behavior but also to analyze the timing constraints of cascaded genetic logic circuits, a capability that we believe will be important for design automation in synthetic biology

Computational aerodynamics and artificial intelligence

The general principles of artificial intelligence are reviewed and speculations are made concerning how knowledge based systems can accelerate the process of acquiring new knowledge in aerodynamics, how computational fluid dynamics may use expert systems, and how expert systems may speed the design and development process. In addition, the anatomy of an idealized expert system called AERODYNAMICIST is discussed. Resource requirements for using artificial intelligence in computational fluid dynamics and aerodynamics are examined. Three main conclusions are presented. First, there are two related aspects of computational aerodynamics: reasoning and calculating. Second, a substantial portion of reasoning can be achieved with artificial intelligence. It offers the opportunity of using computers as reasoning machines to set the stage for efficient calculating. Third, expert systems are likely to be new assets of institutions involved in aeronautics for various tasks of computational aerodynamics

Control Theory for Synthetic Biology: Recent Advances in System Characterization, Control Design, and Controller Implementation for Synthetic Biology

Living organisms are differentiated by their genetic material-millions to billions of DNA bases encoding thousands of genes. These genes are translated into a vast array of proteins, many of which have functions that are still unknown. Previously, it was believed that simply knowing the genetic sequence of an organism would be the key to unlocking all understanding. However, as DNA sequencing technology has become affordable, it has become clear that living cells are governed by complex, multilayered networks of gene regulation that cannot be deduced from sequence alone. Synthetic biology as a field might best be characterized as a learn-by-building approach, in which scientists attempt to engineer molecular pathways that do not exist in nature. In doing so, they test the limits of both natural and engineered organisms

Quantification of the physiochemical constraints on the export of spider silk proteins by Salmonella type III secretion

<p>Abstract</p> <p>Background</p> <p>The type III secretion system (T3SS) is a molecular machine in gram negative bacteria that exports proteins through both membranes to the extracellular environment. It has been previously demonstrated that the T3SS encoded in <it>Salmonella </it>Pathogenicity Island 1 (SPI-1) can be harnessed to export recombinant proteins. Here, we demonstrate the secretion of a variety of unfolded spider silk proteins and use these data to quantify the constraints of this system with respect to the export of recombinant protein.</p> <p>Results</p> <p>To test how the timing and level of protein expression affects secretion, we designed a hybrid promoter that combines an IPTG-inducible system with a natural genetic circuit that controls effector expression in <it>Salmonella </it>(<it>psicA</it>). LacO operators are placed in various locations in the <it>psicA </it>promoter and the optimal induction occurs when a single operator is placed at the +5nt (234-fold) and a lower basal level of expression is achieved when a second operator is placed at -63nt to take advantage of DNA looping. Using this tool, we find that the secretion efficiency (protein secreted divided by total expressed) is constant as a function of total expressed. We also demonstrate that the secretion flux peaks at 8 hours. We then use whole gene DNA synthesis to construct codon optimized spider silk genes for full-length (3129 amino acids) <it>Latrodectus hesperus </it>dragline silk, <it>Bombyx mori </it>cocoon silk, and <it>Nephila clavipes </it>flagelliform silk and PCR is used to create eight truncations of these genes. These proteins are all unfolded polypeptides and they encompass a variety of length, charge, and amino acid compositions. We find those proteins fewer than 550 amino acids reliably secrete and the probability declines significantly after ~700 amino acids. There also is a charge optimum at -2.4, and secretion efficiency declines for very positively or negatively charged proteins. There is no significant correlation with hydrophobicity.</p> <p>Conclusions</p> <p>We show that the natural system encoded in SPI-1 only produces high titers of secreted protein for 4-8 hours when the natural <it>psicA </it>promoter is used to drive expression. Secretion efficiency can be high, but declines for charged or large sequences. A quantitative characterization of these constraints will facilitate the effective use and engineering of this system.</p

Empirical Analysis of Electron Beam Lithography Optimization Models from a Pragmatic Perspective

Electron Beam (EB) lithography is a process of focussing electron beams on silicon wafers to design different integrated circuits (ICs). It uses an electron gun, a blanking electrode, multiple electron lenses, a deflection electrode, and control circuits for each of these components. But the lithography process causes critical dimension overshoots, which reduces quality of the underlying ICs. This is caused due to increase in beam currents, frequent electron flashes, and reducing re-exposure of chip areas. Thus, to overcome these issues, researchers have proposed a wide variety of optimization models, each of which vary in terms of their qualitative &amp; quantitative performance. These models also vary in terms of their internal operating characteristics, which causes ambiguity in identification of optimum models for application-specific use cases. To reduce this ambiguity, a discussion about application-specific nuances, functional advantages, deployment-specific limitations, and contextual future research scopes is discussed in this text. Based on this discussion, it was observed that bioinspired models outperform linear modelling techniques, which makes them highly useful for real-time deployments. These models aim at stochastically evaluation of optimum electron beam configurations, which improves wafer’s quality &amp; speed of imprinting when compared with other models. To further facilitate selection of these models, this text compares them in terms of their accuracy, throughput, critical dimensions, deployment cost &amp; computational complexity metrics. Based on this discussion, researchers will be able to identify optimum models for their performance-specific use cases. This text also proposes evaluation of a novel EB Lithography Optimization Metric (EBLOM), which combines multiple performance parameters for estimation of true model performance under real-time scenarios. Based on this metric, researchers will be able to identify models that can perform optimally with higher performance under performance-specific constraints