Finite state machines implementation using DNA Techniques

Abstract A finite-state machine (FSM) is an abstract mathematical model of computation used to design both computer programs and sequential logic circuits. Considered as an abstract model of computation, the finite state machine is weak; it has less computational power than some other models of computation such as the Turing machine. This paper overview the finite-state automata based on Deoxyribonucleic Acid (DNA). Such automata uses massive parallel processing offered by molecular approach for computation and exhibits a number of advantages over traditional electronic implementations

DNA-BASED SELF-ASSEMBLY AND NANOROBOTICS: THEORY AND EXPERIMENTS

We study the following fundamental questions in DNA-based self-assembly and nanorobotics: How to control errors in self-assembly? How to construct complex nanoscale objects in simpler ways? How to transport nanoscale objects in programmable manner? Fault tolerance in self-assembly: Fault tolerant self-assembly is important for nanofab-rication and nanocomputing applications. It is desirable to design compact error-resilient schemes that do not result in the increase in the original size of the assemblies. We present a comprehensive theory of compact error-resilient schemes for algorithmic self-assembly in two and three dimensions, and discuss the limitations and capabilities of redundancy based compact error correction schemes. New and powerful self-assembly model: We develop a reversible self-assembly model in which the glue strength between two juxtaposed tiles is a function of the time they have been in neighboring positions. Under our time-dependent glue model, we can rigorously study and demonstrate catalysis and self-replication in the tile assembly. We can assemble thin rectangles of size k × N using O

Problems

I. Definition of the Subject and Its Importanc

Analyzing large-scale DNA Sequences on Multi-core Architectures

Rapid analysis of DNA sequences is important in preventing the evolution of different viruses and bacteria during an early phase, early diagnosis of genetic predispositions to certain diseases (cancer, cardiovascular diseases), and in DNA forensics. However, real-world DNA sequences may comprise several Gigabytes and the process of DNA analysis demands adequate computational resources to be completed within a reasonable time. In this paper we present a scalable approach for parallel DNA analysis that is based on Finite Automata, and which is suitable for analyzing very large DNA segments. We evaluate our approach for real-world DNA segments of mouse (2.7GB), cat (2.4GB), dog (2.4GB), chicken (1GB), human (3.2GB) and turkey (0.2GB). Experimental results on a dual-socket shared-memory system with 24 physical cores show speed-ups of up to 17.6x. Our approach is up to 3x faster than a pattern-based parallel approach that uses the RE2 library.Comment: The 18th IEEE International Conference on Computational Science and Engineering (CSE 2015), Porto, Portugal, 20 - 23 October 201

Improving the reliability in bio-nanosensor modules using hardware redundancy techniques

A nano-robot is a controlled robotic system at the nanoscale. Nowadays, nanorobotics has become of particular interest in medicine and pharmacy. The accurate diagnosis of the diseases as well as their rapid treatment will make everyone surprised and will significantly reduce the associated risks. The modeling of reliability in biosensors is studied for the first time in this paper. The use of practical hardware redundancy has turned into the most cost-effective to improve the reliability of a system. Additionally, the Markov model is used to design fault-tolerant systems in nanotechnology. The proposed method is compared with some existing methods, such as triple modular redundancy and non-fault-tolerant systems; it is shown that using this method, a larger number of faults between 3-5 can be tolerated. Using the proposed method, the number of modules can be increased to nine. However, a larger number than 9 MR is not recommended because of an increased delay and requiring more hardware. As the scale of components used in digital systems has gotten smaller, the use of hardware redundancy has become cost-effective. But there is a trade-off between the amount of used hardware and fault tolerance, which can also be investigated

www.springerreference.com/docs/html/chapterdbid/60497.html Mechanical Computing: The Computational Complexity of Physical Devices

- Mechanism: A machine or part of a machine that performs a particular task computation: the use of a computer for calculation.- Computable: Capable of being worked out by calculation, especially using a computer.- Simulation: Used to denote both the modeling of a physical system by a computer as well as the modeling of the operation of a computer by a mechanical system; the difference will be clear from the context. Definition of the Subject Mechanical devices for computation appear to be largely displaced by the widespread use of microprocessor-based computers that are pervading almost all aspects of our lives. Nevertheless, mechanical devices for computation are of interest for at least three reasons: (a) Historical: The use of mechanical devices for computation is of central importance in the historical study of technologies, with a history dating back thousands of years and with surprising applications even in relatively recent times. (b) Technical & Practical: The use of mechanical devices for computation persists and has not yet been completely displaced by widespread use of microprocessor-based computers. Mechanical computers have found applications in various emerging technologies at the micro-scale that combine mechanical functions with computational and control functions not feasible by purely electronic processing. Mechanical computers also have been demonstrated at the molecular scale, and may also provide unique capabilities at that scale. The physical designs for these modern micro and molecular-scale mechanical computers may be based on the prior designs of the large-scale mechanical computers constructed in the past. (c) Impact of Physical Assumptions on Complexity of Motion Planning, Design, and Simulation: The study of computation done by mechanical devices is also of central importance in providing lower bounds on the computational resources such as time and/or space required to simulate a mechanical syste

Biophysics of DNA based Nanosystems Probed by Optical Nanoscopy

A dynamic DNA nanosystem exploits the programmable structure and energy landscape of DNA self-assembly to encode designed processes in a fuctuating molecular environment. One type of such a dynamic system, DNA walker, is reminiscent of biological motor proteins that convert chemical energy into mechanical translocation. Typical DNA walker travels tens of nanometers at a speed orders of magnitude slower than motor proteins. Two major challenges limited the development of functional DNA walkers. First, there are no suitable characterization methods that o˙er adequate spatial and temporal resolution to extract walker kinetics. Second, no guidelines have been established for the design and development of DNA walkers with specifed properties. In this work, an enzymatic DNA walker system that integrate oligonucleotides with nanomaterials is designed. This approach takes advantage of novel optical properties of nanomaterials and sub-di˙raction imaging techniques to study the kinetics and biophysical nature of synthetic DNA walkers. Design principles are extracted from walker kinetics for constructing functional walkers that can rival motor proteins. Multiple schemes are explored to regulate the walker motility so that various behaviors can be encoded into the system. This work demonstrates novel methods to design and construct molecular systems with programmed functions, which will pave the road for creating synthetic systems with encoded behaviors from the bottom up

Molecular robots guided by prescriptive landscapes

Traditional robots rely for their function on computing, to store internal representations of their goals and environment and to coordinate sensing and any actuation of components required in response. Moving robotics to the single-molecule level is possible in principle, but requires facing the limited ability of individual molecules to store complex information and programs. One strategy to overcome this problem is to use systems that can obtain complex behaviour from the interaction of simple robots with their environment. A first step in this direction was the development of DNA walkers, which have developed from being non-autonomous, to being capable of directed but brief motion on one-dimensional tracks. Here we demonstrate that previously developed random walkers—so-called molecular spiders that comprise a streptavidin molecule as an inert ‘body’ and three deoxyribozymes as catalytic ‘legs’—show elementary robotic behaviour when interacting with a precisely defined environment. Single-molecule microscopy observations confirm that such walkers achieve directional movement by sensing and modifying tracks of substrate molecules laid out on a two-dimensional DNA origami landscape. When using appropriately designed DNA origami, the molecular spiders autonomously carry out sequences of actions such as ‘start’, ‘follow’, ‘turn’ and ‘stop’. We anticipate that this strategy will result in more complex robotic behaviour at the molecular level if additional control mechanisms are incorporated. One example might be interactions between multiple molecular robots leading to collective behaviour; another might be the ability to read and transform secondary cues on the DNA origami landscape as a means of implementing Turing-universal algorithmic behaviour

Using the Features of Brownian Motion to Characterize the Nuclear Pore Complex, Molecular Robots, and Antimony-Doped Tin Oxide.

Brownian motion is the apparently random motion of small particles in a solution that results from the bombardment of molecules within the solution. The theoretical understanding of this motion was developed by Einstein in the early 1900s. Since then, features of Brownian motion, such as the fact that Brownian motion can be modeled using a random walk, or the fact that ensemble mean squared displacement (MSD) can be used to determine a diffusion coefficient and type of diffusive behavior, have been utilized to characterize a vast array of systems that are both naturally occurring and synthetic. In this thesis, I characterize three different types of systems using features of Brownian motion: naturally occurring nuclear pore complexes, synthetic molecular robots that are based on naturally occurring bipedal molecular walkers, and synthetic conductive nanoporous antimony-doped tin oxide (ATO). For the nuclear pore complex, the diffusion of particles through each region of the complex was modeled using a random walk in order to help determine the relative diffusion coefficients of the three regions. For the molecular robots, the movement of the robots was modeled using a more advanced random-walk simulation that utilizes the Gillespie algorithm; the movement of the robots was evaluated based on the MSDs, and the results were used to characterize the directional bias in the walking mechanism of the robots. For the ATO, fluorescent particles were monitored as they underwent Brownian motion while diffusing through the nanopores; MSDs were used to determine that these particles are embedded in the nanopores and that the diffusion coefficient depended in an unexpected way on the potential applied across the material.PhDPhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99910/1/michelot_1.pd