DNA Computing and Implementations

DNA Computing aims to harness the molecules at the Nano level for computational purpose. DNA Computing features high data density and massive storage capability therefore, its approach can be used to solve various combinatorial Problems like solving Non Deterministic Problems (i.e. NP- Complete and NP-Hard). This Molecular Level Computational involve input and output both in the molecule form. Since DNA has already been explored as an exquisite material and is a fundamental block for manufacturing large scale Nano mechanical devices. DNA Computing is an approach towards the Biomolecular Computation where the aim is not only to process the information but also to transfer it to other molecular structures for utilization. DNA Computing is slower when an individual DNA Computes in compare to silica based chips. Its Efficiently and throughput increases as the number of DNA increase. DNA provides the possibility of massive parallelism. Starting with the Introduction about the DNA Structure, followed up by DNA Computers, this paper will discuss some recent advancements and challenges of DNA Computing. We will also discuss the possible future scope and implementation as well how the Artificial Intelligence approach can be used with DNA Based Computers to achieve a better and efficient Machine Learning

Optically Active Dye-Based Systems Templated by DNA Exhibiting Excitonic Delocalization

The concept of quantum computing was first developed in the early 1980’s. The attraction of quantum computers is their potential capacity to solve extremely complex problems, such as factorization, on a timescale far faster than that of classical computers. However, realization of quantum computation is currently in its infancy, and recent implementations possess serious drawbacks that reduce their appeal. Some challenges of current designs include the necessity to cool the systems using liquid helium to near absolute zero temperatures (15 mK) in order to maintain sufficiently long-lifetimes of the Qbits (i.e., unit of quantum information), difficulty with scaling up the processing systems, and prohibitively high manufacturing costs. Fundamentally, the key physical effect that enables high processing speeds in quantum computers is quantum superposition, which allows a single qbit to have two (or more) definite states (e.g., 0 and 1) simultaneously. Maintaining a superposition of states at room temperature, however, has proven difficult with silicon-based technology. Coherent exciton delocalization, which involves the superposition of excitonic states characterized by the delocalization of excitons (i.e., electron-hole pairs) across spatially proximate but separated molecules, has been observed in biological photosynthetic systems at ambient temperatures (295 K). Natural photosynthetic systems are composed of protein scaffolds that encompass and elegantly arrange an aggregate of optically active dye molecules (i.e., cluster of chromophores) with nanometer-scale precision in a manner that promotes coherence despite the inherently warm and “noisy” (i.e., rapidly fluctuating) environment inside a plant. As a result, light energy absorbed from the sun is quickly and efficiently transferred through the dye aggregate in a wavelike manner that both optimizes the transfer pathway and minimizes energy loss. Thus, exploiting excitonic delocalization, as inspired by biology, offers a potential path forward towards realizing quantum computing at room temperature. Here, we demonstrate coherent exciton delocalization in systems that utilize DNA, a biological material that affords atomically precise arrangement of dyes (e.g., Cy5) with nanometer proximity, as a scaffold. Leveraging the inherent programmability and functionality of DNA, which undergoes Watson-Crick base-pairing to enable simple structural control than the complex folding mechanisms involved with proteins, we have designed two dye-DNA complexes that are described in two journal manuscripts contained within this dissertation (Chapters 2 and 3). The first manuscript, which described the behavior and spectral properties of a relatively simple linear dye-DNA complex, achieved two milestones towards quantum information processing: (i) the identification of Cy5 dyes as promising candidates for the development of exciton-based devices and quantum gates due to the large Davydov splitting observed spectrally (i.e., a manifestation of dye-dye coupling and coherent exciton delocalization), and (ii) the data necessary to determine the physical parameters for a phenomenological theoretical model of exciton transport between Cy5 dyes within a DNA complex. The second manuscript, which encompassed a larger, more rigid, two-dimensional Holliday junction structure designed to form dye aggregates of a pre-determined size including dimers, trimers, and tetramers, validated the physical parameters used in the theoretical work for the first manuscript, showing that the same parameters can be used for other dye-DNA configurations. It also demonstrated that large Davydov splitting in dye aggregates can be achieved using a larger, more rigid two-dimensional Holliday junction structure. Taken together, the two manuscripts combined give confidence to the phenomenological theoretical model, which can be used as a predictive engineering tool for designing dye-DNA based excitonic devices and quantum gates, or as an analysis tool for determining dye configurations based on spectral data

A Variable Neighborhood Search Approach for Solving the Maximum Set Splitting Problem

This paper presents a Variable neighbourhood search (VNS) approach for solving the Maximum Set Splitting Problem (MSSP). The algorithm forms a system of neighborhoods based on changing the component for an increasing number of elements. An efficient local search procedure swaps the components of pairs of elements and yields a relatively short running time. Numerical experiments are performed on the instances known in the literature: minimum hitting set and Steiner triple systems. Computational results show that the proposed VNS achieves all optimal or best known solutions in short times. The experiments indicate that the VNS compares favorably with other methods previously used for solving the MSSP. ACM Computing Classification System (1998): I.2.8

Genomic Methods for Bacterial Infection Identification

Hospital-acquired infections (HAIs) have high mortality rates around the world and are a challenge to medical science due to rapid mutation rates in their pathogens. A new methodology is proposed to identify bacterial species causing HAIs based on sets of universal biomarkers for next-generation microarray designs (i.e., nxh chips), rather than a priori selections of biomarkers. This method allows arbitrary organisms to be classified based on readouts of their DNA sequences, including whole genomes. The underlying models are based on the biochemistry of DNA, unlike traditional edit-distance based alignments. Furthermore, the methodology is fairly robust to genetic mutations, which are likely to reduce accuracy. Standard machine learning methods (neural networks, self-organizing maps, and random forests) produce results to identify HAIs on nxh chips that are very competitive, if not superior, to current standards in the field. The potential feasibility of translating these techniques to a clinical test is also discussed

Inference of single-cell phylogenies from lineage tracing data using Cassiopeia.

The pairing of CRISPR/Cas9-based gene editing with massively parallel single-cell readouts now enables large-scale lineage tracing. However, the rapid growth in complexity of data from these assays has outpaced our ability to accurately infer phylogenetic relationships. First, we introduce Cassiopeia-a suite of scalable maximum parsimony approaches for tree reconstruction. Second, we provide a simulation framework for evaluating algorithms and exploring lineage tracer design principles. Finally, we generate the most complex experimental lineage tracing dataset to date, 34,557 human cells continuously traced over 15 generations, and use it for benchmarking phylogenetic inference approaches. We show that Cassiopeia outperforms traditional methods by several metrics and under a wide variety of parameter regimes, and provide insight into the principles for the design of improved Cas9-enabled recorders. Together, these should broadly enable large-scale mammalian lineage tracing efforts. Cassiopeia and its benchmarking resources are publicly available at www.github.com/YosefLab/Cassiopeia

DNA computation

This is the first ever doctoral thesis in the field of DNA computation. The field has its roots in the late 1950s, when the Nobel laureate Richard Feynman first introduced the concept of computing at a molecular level. Feynman's visionary idea was only realised in 1994, when Leonard Adleman performed the first ever truly molecular-level computation using DNA combined with the tools and techniques of molecular biology. Since Adleman reported the results of his seminal experiment, there has been a flurry of interest in the idea of using DNA to perform computations. The potential benefits of using this particular molecule are enormous: by harnessing the massive inherent parallelism of performing concurrent operations on trillions of strands, we may one day be able to compress the power of today's supercomputer into a single test tube. However, if we compare the development of DNA-based computers to that of their silicon counterparts, it is clear that molecular computers are still in their infancy. Current work in this area is concerned mainly with abstract models of computation and simple proof-of-principle experiments. The goal of this thesis is to present our contribution to the field, placing it in the context of the existing body of work. Our new results concern a general model of DNA computation, an error-resistant implementation of the model, experimental investigation of the implementation and an assessment of the complexity and viability of DNA computations. We begin by recounting the historical background to the search for the structure of DNA. By providing a detailed description of this molecule and the operations we may perform on it, we lay down the foundations for subsequent chapters. We then describe the basic models of DNA computation that have been proposed to date. In particular, we describe our parallel filtering model, which is the first to provide a general framework for the elegant expression of algorithms for NP-complete problems. The implementation of such abstract models is crucial to their success. Previous experiments that have been carried out suffer from their reliance on various error-prone laboratory techniques. We show for the first time how one particular operation, hybridisation extraction, may be replaced by an error-resistant enzymatic separation technique. We also describe a novel solution read-out procedure that utilizes cloning, and is sufficiently general to allow it to be used in any experimental implementation. The results of preliminary tests of these techniques are then reported. Several important conclusions are to be drawn from these investigations, and we report these in the hope that they will provide useful experimental guidance in the future. The final contribution of this thesis is a rigorous consideration of the complexity and viability of DNA computations. We argue that existing analyses of models of DNA computation are flawed and unrealistic. In order to obtain more realistic measures of the time and space complexity of DNA computations we describe a new strong model, and reassess previously described algorithms within it. We review the search for "killer applications": applications of DNA computing that will establish the superiority of this paradigm within a certain domain. We conclude the thesis with a description of several open problems in the field of DNA computation

Research reports: 1990 NASA/ASEE Summer Faculty Fellowship Program

Reports on the research projects performed under the NASA/ASEE Summer Faculty Fellowship Program are presented. The program was conducted by The University of Alabama and MSFC during the period from June 4, 1990 through August 10, 1990. Some of the topics covered include: (1) Space Shuttles; (2) Space Station Freedom; (3) information systems; (4) materials and processes; (4) Space Shuttle main engine; (5) aerospace sciences; (6) mathematical models; (7) mission operations; (8) systems analysis and integration; (9) systems control; (10) structures and dynamics; (11) aerospace safety; and (12) remote sensin