Another expert system rule inference based on DNA molecule logic gates

With the help of silicon industry microfluidic processors were invented utilizing nano membrane valves, pumps and mi-croreactors. These so called lab-on-a-chips combined together with molecular computing create molecular-systems-on-a-chips. This work presents a new approach to implementation of molecular inference systems. It requires the unique representation of signals by DNA molecules. The main part of this work includes the concept of logic gates based on typical genetic engineering reactions. The presented method allows for constructing logic gates with many inputs and for executing them at the same quantity of elementary operations, regardless of a number of input signals. Every microreactor of the lab-on-a-chip performs one unique operation on input molecules and can be connected by dataflow output-input connections to other ones

A Study on DNA Memory Encoding Architecture

The amount of raw generated data is growing at an exponential rate due to the greatly increasing number of sensors in electronic systems. While the majority of this data is never used, it is often kept for cases such as failure analysis. As such, archival memory storage, where data can be stored at an extremely high density at the cost of read latency, is becoming more popular than ever for long term storage. In biological organisms, Deoxyribonucleic Acid (DNA) is used as a method of storing information in terms of simple building blocks, as to allow for larger and more complicated struc- tures in a density much higher than can currently be realized on modern memory devices. Given the ability for organisms to store this information in a set of four bases for an extremely long amounts of time with limited degradation, DNA presents itself as a possible way to store data in a manner similar to binary data. This work investigates the use of DNA strands as a storage regime, where system-level data is translated into an efficient encoding to minimize base pair errors both at a local level and at the chain level. An encoding method using a Bose-Chaudhuri-Hocquenghem (BCH) pre-coded Raptor scheme is implemented in conjunction with an 8 to 6 bi- nary to base translation, yielding an informational density of 1.18 bits/base pair. A Field-Programmable Gate Array (FPGA) is then used in conjunction with a soft-core processor to verify address and key translation abilities, providing strong support that a strand-pool DNA model is reasonable for archival storage

A forensics software toolkit for DNA steganalysis.

Recent advances in genetic engineering have allowed the insertion of artificial DNA strands into the living cells of organisms. Several methods have been developed to insert information into a DNA sequence for the purpose of data storage, watermarking, or communication of secret messages. The ability to detect, extract, and decode messages from DNA is important for forensic data collection and for data security. We have developed a software toolkit that is able to detect the presence of a hidden message within a DNA sequence, extract that message, and then decode it. The toolkit is able to detect, extract, and decode messages that have been encoded with a variety of different coding schemes. The goal of this project is to enable our software toolkit to determine with which coding scheme a message has been encoded in DNA and then to decode it. The software package is able to decode messages that have been encoded with every variation of most of the coding schemes described in this document. The software toolkit has two different options for decoding that can be selected by the user. The first is a frequency analysis approach that is very commonly used in cryptanalysis. This approach is very fast, but is unable to decode messages shorter than 200 words accurately. The second option is using a Genetic Algorithm (GA) in combination with a Wisdom of Artificial Crowds (WoAC) technique. This approach is very time consuming, but can decode shorter messages with much higher accuracy

Devices for safety-critical molecular programmed systems

The behavior of matter at the molecular level can be programmed to create nanoscale molecular components that accomplish desired tasks. Many molecular components are developed with intended uses that are safety-critical, such as medical applications. Ensuring the correctness and fault tolerance of such devices is paramount. Techniques to develop robustly correct programs have been widely studied in software systems and many devices have been constructed to aid in the safe operation of systems. We seek to demonstrate the effectiveness of software and safety engineering techniques in the molecular programming domain. In this thesis, we present the design of five new devices to aid in the development of safetycritical molecular programmed systems. We introduce a Runtime Fault Detection device (RFD) to robustly detect faults and initiate recovery actions in response to a failed system. We present the Concentration Monitor, a device that can detect changes, major and minor, in concentrations in real-time and demonstrate its utility. We also describe methods for constructing chemical reaction networks that can robustly simulate any combinational logic gate. Finally, we present two devices to log the state of a molecular program, where the first device logs a state upon receiving a request, and the second device ensures that the current state meets a defined validity property before allowing a log to be taken. All devices have been formally verified using model checking, simulations, or formal proof techniques. The methods used to construct and verify these devices can be adapted to the design of future molecular systems to assist in ensuring their correctness

Advance the DNA computing

It has been previously shown that DNA computing can solve those problems currently intractable on even the fastest electronic computers. The algorithm design for DNA computing, however, is not straightforward. A strong background in both the DNA molecule and computer engineering are required to develop efficient DNA computing algorithms. After Adleman solved the Hamilton Path Problem using a combinatorial molecular method, many other hard computational problems were investigated with the proposed DNA computer. The existing models from which a few DNA computing algorithms have been developed are not sufficiently powerful and robust, however, to attract potential users. This thesis has described research performed to build a new DNA computing model based on various new algorithms developed to solve the 3-Coloring problem. These new algorithms are presented as vehicles for demonstrating the advantages of the new model, and they can be expanded to solve other NP-complete problems. These new algorithms can significantly speed up computation and therefore achieve a consistently better time performance. With the given resource, these algorithms can also solve problems of a much greater size, especially as compared to existing DNA computation algorithms. The error rate can also be greatly reduced by applying these new algorithms. Furthermore, they have the advantage of dynamic updating, so an answer can be changed based on modifications made to the initial condition. This new model makes use of the huge possible memory by generating a ``lookup table'' during the implementation of the algorithms. If the initial condition changes, the answer changes accordingly. In addition, the new model has the advantage of decoding all the strands in the final pool both quickly and efficiently. The advantages provided by the new model make DNA computing an efficient and attractive means of solving computationally intense problems

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

Inductive Pattern Formation

With the extended computational limits of algorithmic recursion, scientific investigation is transitioning away from computationally decidable problems and beginning to address computationally undecidable complexity. The analysis of deductive inference in structure-property models are yielding to the synthesis of inductive inference in process-structure simulations. Process-structure modeling has examined external order parameters of inductive pattern formation, but investigation of the internal order parameters of self-organization have been hampered by the lack of a mathematical formalism with the ability to quantitatively define a specific configuration of points. This investigation addressed this issue of quantitative synthesis. Local space was developed by the Poincare inflation of a set of points to construct neighborhood intersections, defining topological distance and introducing situated Boolean topology as a local replacement for point-set topology. Parallel development of the local semi-metric topological space, the local semi-metric probability space, and the local metric space of a set of points provides a triangulation of connectivity measures to define the quantitative architectural identity of a configuration and structure independent axes of a structural configuration space. The recursive sequence of intersections constructs a probabilistic discrete spacetime model of interacting fields to define the internal order parameters of self-organization, with order parameters external to the configuration modeled by adjusting the morphological parameters of individual neighborhoods and the interplay of excitatory and inhibitory point sets. The evolutionary trajectory of a configuration maps the development of specific hierarchical structure that is emergent from a specific set of initial conditions, with nested boundaries signaling the nonlinear properties of local causative configurations. This exploration of architectural configuration space concluded with initial process-structure-property models of deductive and inductive inference spaces. In the computationally undecidable problem of human niche construction, an adaptive-inductive pattern formation model with predictive control organized the bipartite recursion between an information structure and its physical expression as hierarchical ensembles of artificial neural network-like structures. The union of architectural identity and bipartite recursion generates a predictive structural model of an evolutionary design process, offering an alternative to the limitations of cognitive descriptive modeling. The low computational complexity of these models enable them to be embedded in physical constructions to create the artificial life forms of a real-time autonomously adaptive human habitat

Protein nanopores as a platform for transmembrane nanodevices

Nanopore sensing has seen vast development over the past four decades. The technique originally looked to use electrophysiological methods to study native protein channels. However, it is now possible to exploit these proteins for sensing applications. Herein, we explore methods for covalent and non-covalent modification of a biological nanopore to achieve new functionality. Chapter 1 summarises the history of nanopore technology; from its inception as a method for studying native channels, to its deployment in nanopore sensing. To achieve effective sensing, native proteins have undergone a broad range of chemical modification to achieve enhanced functionality. This chapter explores the amalgamation of biological and solid-state nanopores. Chapter 2 seeks to the monitor the binding and catalytic turnover of substrates within a single cucurbituril molecule captured within a protein nanopore. Previous work has shown that cucurbiturils and cyclodextrins can transiently interact with an α-hemolysin channel. Capture of a single cucurbituril within a protein nanopore was achieved, and the dwell time of the binding events was optimised. Following this, it was demonstrated that observations of the catalysed Diels-Alder could be made at the single-molecule level. However, further optimisation of the resolution would be required to elucidate mechanistic information. Chapter 3 presents methods for in situ chemical functionalisation of a biological nanopore. Here, the focus is upon the chemical modification of a wild-type protein thereby to circumventing the need for mutagenesis. Three target residues are discussed: lysine, methionine and tyrosine. Successful modification was achieved at both the lysine and methionine sites of α-hemolysin. While some provisional success was recorded with tyrosine, the modifications were not reproducible. Chapter 4 introduces preliminary work towards the development of transmembrane molecular machines. This utilises the lysine modification discussed in Chapter 2 to covalently attach established synthetic molecular machines to the channel. Molecular switches, motors and pumps were all explored. Some success was achieved attaching the molecular machines to a protein channel. However, issues with pore stability limited the progress and true machine-like behaviour was not observed

Simulating Boolean Circuits on a DNA Computer

We demonstrate that DNA computers can simulate Boolean circuits with a small overhead. Boolean circuits embody the notion of massively parallel signal processing and are frequently encountered in many parallel algorithms. Many important problems such as sorting, integer arithmetic, and matrix multiplication are known to be computable by small size Boolean circuits much faster than by ordinary sequential digital computers. This paper shows that DNA chemistry allows one to simulate large semi-unbounded fan-in Boolean circuits with a logarithmic slowdown in computation time. Also, for the class NC 1 , the slowdown can be reduced to a constant. In this algorithm we have encoded the inputs, the Boolean AND gates, and the OR gates to DNA oligonucleotide sequences. We operate on the gates and the inputs by standard molecular techniques of sequence-specific annealing, ligation, separation by size, amplification, sequence-specific cleavage, and detection by size. Additional steps of amplifica..