DNA as a medium for storing digital signals

Motivated by the storage capacity and efficiency of the DNA molecule in this paper we propose to utilize DNA molecules to store digital signals. We show that hybridization of DNA molecules can be used as a similarity criterion for retrieving digital signals encoded and stored in a DNA database. Since retrieval is achieved through hybridization of query and data carrying DNA molecules, we present a mathematical model to estimate hybridization efficiency (also known as selectivity annealing). We show that selectivity annealing is inversely proportional to the mean squared error (MSE) of the encoded signal values. In addition, we show that the concentration of the molecules plays the same role as the decision threshold employed in digital signal matching algorithms. Finally, similarly to the digital domain, we define a DNA signal-to-noise ratio (SNR) measure to assess the performance of the DNA-based retrieval scheme. Simulations are presented to validate our arguments

In silico estimation of annealing specificity of query searches in DNA databases

We consider DNA implementations of databases for digital signals with retrieval and mining capabilities. Digital signals are encoded in DNA sequences and retrieved through annealing between query DNA primers and data carrying DNA target sequences. The hybridization between query and target can be non-specific containing multiple mismatches thus implementing similarity-based searches. In this paper we examine theoretically and by simulation the efficiency of such a system by estimating the concentrations of query-target duplex formations at equilibrium. A coupled kinetic model is used to estimate the concentrations. We offer a derivation that results in an equation that is guaranteed to have a solution and can be easily and accurately solved computationally with bi-section root-finding methods. Finally, we also provide an approximate solution at dilute query concentrations that results in a closed form expression. This expression is used to improve the speed of the bi-section algorithm and also to find a closed form expression for the specificity ratios

Retrieval accuracy of very large DNA-Based databases of digital signals

In this paper a simulation of single query searches in very large DNA-based databases that are capable of storing and retrieving digital signals is presented. Similarly to the digital domain, a signal-to-noise ratio (SNR) measure to assess the performance of theDNA-based retrieval scheme in terms of database size and source statistics is defined. With approximations, it is shown that the SNR of any finite sizeDNA-based database is upper bounded by the SNR of an infinitely large one with the same source distribution. Computer simulations are presented to validate the theoretical outcomes

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

APPLICATION OF INFORMATION SYSTEMS AND TOOLS IN BIOINFORMATICS

The pace at which scientific data is produced and disseminated has never been as high as it is currently. Modern sequencing technologies make it possible to obtain the genome of a specific organism in a few days, and the genome of a bacterial organism in less than a day, and therefore researchers from the field of life science are faced with a huge amount of data that needs to be analyzed. In this connection, various fields of science are converging with each other, giving rise to new disciplines. So, bioinformatics is one of these fields, it is a scientific discipline that has been actively developing over the past decades and uses IT tools and methods to solve problems related to the study of biological processes. In particular, a crucial role in the field of bioinformatics is played by the development of new algorithms, tools and the creation of new databases, as well as the integration of extremely large amounts of data. The rapid development of bioinformatics has made it possible to conduct modern biological research. Bioinformatics can help a biologist to extract valuable information from biological data by using tools to process them. Despite the fact that bioinformatics is a relatively new discipline, various web and computer tools already exist, most of which are freely available. This is a review article that provides an exhaustive overview of some of the tools for biological analysis available to a biologist, as well as describes the key role of information systems in this interdisciplinary field

An application in bioinformatics : a comparison of affymetrix and compugen human genome microarrays

The human genome microarrays from Compugen® and Affymetrix® were compared in the context of the emerging field of computational biology. The two premier database servers for genomic sequence data, the National Center for Biotechnology Information and the European Bioinformatics Institute, were described in detail. The various databases and data mining tools available through these data servers were also discussed. Microarrays were examined from a historical perspective and their main current applications-expression analysis, mutation analysis, and comparative genomic hybridization-were discussed. The two main types of microarrays, cDNA spotted microarrays and high-density spotted microarrays were analyzed by exploring the human genome microarray from Compugen® and the HGU133 Set from Affymetrix® respectively. Array design issues, sequence collection and analysis, and probe selection processes for the two representative types of arrays were described. The respective chip design of the two types of microarrays was also analyzed. It was found that the human genome microarray from Compugen 0 contains probes that interrogate 1,119,840 bases corresponding to 18,664 genes, while the HG-U133 Set from Affymetrix® contains probes that interrogate only 825,000 bases corresponding to 33,000 genes. Based on this, the efficiency of the 25-mer probes of the HG-U133 Set from Affymetrix® compared to the 60-mer probes of the microarray from Compugen® was questioned

Rational Design of DNA Sequences with Non-Orthogonal Binding Interactions

Molecular computation involving promiscuous, or non-orthogonal, binding interactions between system components is found commonly in natural biological systems, as well as some proposed human-made molecular computers. Such systems are characterized by the fact that each computational unit, such as a domain within a DNA strand, may bind to several different partners with distinct, prescribed binding strengths. Unfortunately, implementing systems of molecular computation that incorporate non-orthogonal binding is difficult, because researchers lack a robust, general-purpose method for designing molecules with this type of behavior. In this work, we describe and demonstrate a process for the rational design of DNA sequences with prescribed non-orthogonal binding behavior. This process makes use of a model that represents large sets of non-orthogonal DNA sequences using fixed-length binary strings, and estimates the differential binding affinity between pairs of sequences through the Hamming distance between their corresponding binary strings. The real-world applicability of this model is supported by simulations and some experimental data. We then select two previously described systems of molecular computation involving non-orthogonal interactions, and apply our sequence design process to implement them using DNA strand displacement. Our simulated results on these two systems demonstrate both digital and analog computation. We hope that this work motivates the development and implementation of new computational paradigms based on non-orthogonal binding