Chaos-based image encryption using a hybrid cellular automata and a DNA sequence

A novel chaotic based image encryption using a hybrid model of deoxyribonucleic acid and cellular automata

a novel block image encryption algorithm based on Deoxyribonucleic Acid (DNA) dynamic encoding and chaotic system is proposed. First, the image is converted into a DNA sequence by random encoding rules, and the global scrambling at DNA level is performed on it. The change of the DNA base position causes the image pixel value and the pixel position to change, so the scrambling is performed at the same time the effect of diffusion. Then the image is split into blocks, and the image is diffused by the combination of intra-block DNA sequence operation and inter-block pixel operation. In the scrambling and diffusion process, the quantification related to plaintext and ciphertext feedback mechanisms are introduced respectively, so that the algorithm can effectively resist the choice of plaintext attacks. Theoretical analysis and simulation experiments show that the algorithm can resist various typical attacks, has high security and is easy to implement.

Technology is transforming rapidly. Security during data transmission is an increasingly critical and essential factor for the integrity and confidentiality of data in the financial domain, such as e-commerce transactions and bank transactions, etc. We cannot overestimate the importance of encryption/decryption of information in the digital economy. The need to strengthen and secure the digital economy is urgent. Cryptography maintains the security and integrity of data kept on computers and data communicated over the internet using encryption/decryption. A new concept in cryptography named DNA cryptography has attracted the interest of information security professionals. The DNA cryptography method hides data using a DNA sequence, with DNA encryption converting binary data into the DNA sequence. Deoxy Ribonucleic Acid (DNA) is a long polymer strand having nitrogen bases adenine (A), thymine (T), cytosine (C), and guanine (G), which play an important role in plain text encoding and decoding. DNA has high storage capacity, fast processing, and high computation capacity, and is more secure than other cryptography algorithms. DNA cryptography supports both symmetric and asymmetric cryptography. DNA cryptography can encrypt numeric values, English language and unicast. The main aim of this paper is to explain different aspects of DNA cryptography and how it works. We also compare different DNA algorithms/methods proposed in a previous paper, and implement DNA cryptography using one-time pad (OTP) and amino acid sequence using java language. OTP is used for symmetric key generation and the DNA sequence is converted to an amino acid sequence to create confusion.

Image data has been generated massively by devices in medical imaging modalities, cameras, and even by artificial intelligence. Encryption is the powerful method to keep the image content confidential, in which an encryption algorithm must include the confusion and diffusion properties. For massive images, a efficient method of encryption must be chosen to meet the demands of encryption speed and confidentiality. So far, chaos-based image encryption has been an active topic of research because it is considered an effective method to remove the correlation in image data as well as to keep confidential by the involvement of chaotic system in the encryption process. Besides, multiple image encryption algorithms encrypt multiple images in parallel, and it provides highly efficient performance in term of speed if it is implemented on a parallel computing platform such as multiple core processing as well as digital hardware design. Chaos-based multiple image encryption is constructed by integrating a chaotic system into multiple image encryption. Recently, many algorithms of chaos-based multiple image encryption have been proposed, and they are proved to have high efficient in terms of both speed and confidentiality. However, all the existing algorithms of chaos-based multiple image encryption require images of the same size and of the same number of bits representing pixels. Further, they encrypt a cohort of plain images at the same time, and all ciphertext images of a cohort must also be decrypted at the same time. It means that if it does not allow to decrypt one or some selected ciphertext images from a cohort separately; and as a result, it wastes time and energy to decrypt unwanted images. In this paper, three novel structures of chaos-based multiple image encryption are proposed which overcome the drawbacks of existing algorithms. That is, the proposed cryptosystems accept cohort images of different sizes; pixels of images can be represented by different numbers of bits; and any selected ciphertext images from a cohort can be decrypted separately. The security is improved by using the session keys of image-content dependency. The proposed structures of multiple image encryption consist of permutation, substitution, and diffusion processes. The difference between three structures is the order of such processes. A perturbed chaotic map and a linear-feedback shift register are employed to generate pseudo-random bit sequences for session keys. The simulation results for the exemplar designs using the proposed structures show the effectiveness by means of the statistical analysis for the session keys using the NIST randomness test, information entropy, histogram, and correlation coefficients of adjacent pixels in ciphertext images, and security analysis by means of space and sensitivity of the secret key. The hardware implementation on the FPGA platform demonstrates the feasibility of the proposed structures by means of throughput and hardware efficiency.

The regulation of gene expression is controlled by multiple mechanisms. The primary level of control is embedded within the sequence itself, as the transcription factors bind specific deoxyribonucleic acid (DNA) sequences at the gene regulatory regions and either activate or silence the genes. A secondary level of control includes enhancer sequences, which may lie megabases away from the gene and are necessary for the induction of gene expression in the human genome 1. A third level of control is the three-dimensional structure of chromatin, which can bring enhancers and promoters into contact with one another and can be restricted by the CCCTC-binding factor (CTCF) that insulates chromatin segments into topologically associated domains. A number of novel methodological approaches have recently hastened characterisation of the regulatory elements in the genome 2. One commonly used approach is to couple the chromatin immunoprecipitation sequencing of the transcription factors with gene expression profiling to study the transcription factor fusion targets in leukaemia 3, 4. In comparison, the assay named global nuclear run-on sequencing allows simultaneous exploration of the dynamics of gene expression and regulatory element activity. In a cell model with inducible ETS variant 6-runt-related transcription factor 1 (ETV6-RUNX1) fusion, we were able to follow up the interplay of the enhancer elements and their host genes that occur downstream of this structural variation, which is the most common one found in childhood leukaemias 5. The ETV6-RUNX1 fusion bound the predominantly RUNX1 motif containing DNA sequences and caused repression of the enhancer activity as measured by the enhancer ribonucleic acid (RNA) levels. Interestingly, in most cases, the repression at the enhancers led to lower transcription at the nearby gene loci. In particular, the ETV6-RUNX1 aberration predominantly altered the expression of genes regulating B-cell adhesion and signalling. Furthermore, we noted a significantly higher repression of cluster of differentiation (CD)19/20 associated super-enhancers, in comparison with stem cell-linked CD34 super-enhancers 5. Super-enhancers are regions of the genome with clusterings of multiple enhancer sites and are deemed essential for lineage specification 6. They could provide a possible explanation for the disruption of B-cell differentiation, which is a hallmark of precursor leukaemias. A significant number of somatic mutations in leukaemia are located outside the coding regions of the genome. Mansour et al. 7 identified a small insertional mutation in a regulatory element at the T-cell acute lymphocytic leukaemia protein 1 (TAL1) gene locus in patients with T-cell acute lymphoblastic leukaemia. The mutation introduced a novel transcription factor binding site, for transcription factor myeloblastosis (MYB), thereby creating an aberrant super-enhancer that led to gross overexpression of the TAL1 gene. Such changes at the binding sites may also be inherited, as exemplified by the risk of neuroblastomas associated with a common single nucleotide polymorphism that alters a site where GATA transcription factors bind with DNA in a super-enhancer region of the LIM domain-only 1 (LMO1) gene 8. Moreover, the regulatory boundaries at the oncogene loci may be lost during disruption of the CTCF binding sites by mutations, as shown in T-cell acute lymphoblastic leukaemia 9, which leads to aberrant transcriptional activity. In addition to explaining the aberrant activation of oncogenes in leukaemic cells, highly active enhancers may create genetic instability. We explored the transcriptional features associated with structural variation sites in precursor B-cell leukaemias, which are represented by deletions, insertions, translocations and inversions. Structural variation sites tended to accumulate at certain loci in the leukaemia genomes and were more prevalent at actively transcribed regions. We discovered that simultaneous transcription from both DNA strands was often associated with these recurring structural variation sites 10. Enhancers that overlapped gene regions displayed this feature, which was accompanied by polymerase II stalling, access to DNA cleavage motifs and R-loop formation, a DNA:RNA hybrid formed during the transcription, which could lead to double-stranded DNA breaks that promote off-target cleavage by recombination-activating genes and activation-induced cytidine deaminase complexes. Stalled polymerases were found within the first intron of many key regulators of cell differentiation and tumour suppressor genes, including paired box protein 5 (PAX5) and B-cell translocation gene 1 (BTG1) protein 10, 11. The preleukaemic cells arrested at the pre-B stage are thereby vulnerable to secondary lesions at such genomic regions. Taken together, these observations highlighted that altered activity or mutations in both coding and noncoding regions can have a profound effect on the regulatory network that governs lymphoid cell differentiation. With the power of ever-developing sequencing techniques, these alterations, which may lead to childhood leukaemia, are now being elucidated at a genomewide scale. It is also tempting to speculate that the detection of changes in the regulatory elements of the genome might guide us to put patients in more specific stratifications as well as increase our understanding of the leukaemia as a disease.

Solid nanopore-based deoxyribonucleic acid (DNA) sequencing has led to low-cost, fast, reliable, controlled, and amplified or label-free and high-resolution recognition and identification of DNA nucleotides. Solid-state materials and biological nanopores have a low signal-to-noise ratio (SNR) and generally are too thick to read at single-nucleotide resolution. The issue with solid-state nanopores is that the DNA strands stick to the nanopore sides and on the surface during the translocation process. The coexistence of DNA nucleotides on the surface and the nanopore sides will complicate the ionic current signals, making nucleotide detection difficult. Therefore, different sized nanogaps can be promising to overcome some of these issues. Using all-atom molecular dynamics (MD) simulations, we have studied the translocation of single-stranded (ss) DNA through solid-state nanogaps embedded in a graphene membrane device. A nucleotide-specific DNA sequencing technique is proposed based on unique differences in the ionic current responses for all the four ssDNA nucleotides (dAMP16, dGMP16, dTMP16, and dCMP16). As the individual homogeneous ssDNA translocate through the nanogaps, characteristic changes are observed in the ionic current. Our results show that ssDNA nucleotides can translocate through the proposed graphene nanogap devices by applying an external electric field. In addition, the sticking issue can be resolved using graphene nanogaps during the ssDNA translocation processes. Therefore, the significant difference in ionic current sensitivity and the translocation event/time yielded by the graphene nanogap-based devices reveal possibilities for utilizing it for ultrafast nanogap-based DNA sequencing.

Deoxyribonucleic acid (DNA) has become one of the most examined molecules on the planet. Scientist around the world have been trying to unravel its secrets for many purposes. For example, genetic information is currently used to raise better plants and animals, create enhanced pharmaceuticals for humans, and for gene therapy in medicine. Science as a whole has benefited from the study of genetics because of the increased understanding of biological process that all organisms share. In recent decades, a significant amount of research has been directed towards sequencing and understanding the entire human genome through the Human Genome Project (HGP) launched in 1986. The goal of the HGP was to find the location of the approximately 1×105 human genes, and read all the sequence of human genome (about 3×109 base pairs, bp). An exponential grow rate of that research has resulted in reaching the goal by 2003. Similarly, the speed of finding genes and their locations is also increasing rapidly. On the other hand, the traditional methods of finding genes and their location at chromatosomes through testing their biological function have been inherently slow. Although numerous faster techniques have been developed, there is still a need to augment them with new approaches. Therefore, robust computational solutions to the gene-finding problem could provide a valuable resource for the HGP and for the molecular-biology community. Most of the current research in the deciphering the meaning of DNA sequences is approached from the lowest base-pair level. Its main objective is to search for patterns or correlations existing in the DNA sequence related to codons, amino acids, and proteins. A number of gene-finding systems have been developed in recent decades. These systems use a variety of sophisticated computational data-miming techniques, including neural networks, dynamic programming, rule-based methods, decision trees, probability reasoning, hidden Markov chains, genetic programming, and support vector machines. Most of these approaches are based on local measures only. In addition, many of the techniques rely on the statistical qualities of exons in the gene, thus using only the known gene pool as a training set for their classification. Although the techniques have demonstrated limited success, better techniques should be developed. An approach to finding such improved techniques is to consider long-range relations (in addition to short-range relations) in the DNA sequence, spanning 104 nucleotides. If we had a good technique to measure such long-range relations, we would be able to estimate any existing self-affinity (fractality) in the DNA sequence, without any a priori assumptions about its structure. This would be a data-driven approach, rather than the common modeldriven approach. Along those lines, preliminary results have already been reported in the literature on a local self-similarity with a 180 bp periodicity in mammalian nuclear DNA sequence. Other publications have provided evidence that the long-range fractal correlations appear in DNA sequences with different values in different regions of the sequence. This paper describes such a multiscale approach, together with an algorithm based on a multifractal analysis, and demonstrates that multifractal estimates can be used to characterize DNA sequences [1], [2], [3]. This multifractal approach appears to be new, and may provide a key to cognitive analysis of DNA sequences. It should be clear that the DNA sequencing and gene finding techniques constitute a subset of bioinformatics, the science of using information to understand biology, with its numerous tools. In turn, bioinformatics is a subset of computational biology which is the application of quantitative analytical techniques in modelling biological systems. Very often, for structural biologists, DNA is not just a sequence of symbols, but implies 3D structures, molecular shapes and conformational changes, active sites, chemical reactions, and intermolecular interactions, each with a hierarchy of importance. What might be missing from this web of various approaches to solving life-related problems is cognitive informatics with its new approaches [4].

A promoter is a brief stretch of DNA (100–1,000 bp) where RNA polymerase starts to transcribe a gene. A DNA (Deoxyribonucleic Acid) base pair is a fundamental unit of DNA structure and represents the pairing of two complementary nucleotide bases within the DNA double helix. The four DNA nucleotide bases are adenine (A), thymine (T), cytosine (C), and guanine (G). DNA base pairs are the building blocks of the DNA molecule, and their complementary pairing is central to the storage and transmission of genetic information in all living organisms. Normally, a promoter is found at the 5′ end of the transcription initiation site or immediately upstream. Numerous human disorders, particularly diabetes, cancer, and Huntington’s disease, have been shown to have DNA promoter as their root cause. The scientific community has long been interested in learning crucial information about protein-coding genes. Finding the promoters is therefore the first step in finding genes in DNA sequences. The scientific world has always been attracted by the effort to glean crucial knowledge about protein-coding genes. Consequently, identifying promoters has emerged as an intriguing challenge that has caught the interest of numerous researchers in the field of bioinformatics. We proposed Gaussian Decision Boundary Estimation in machine learning models to detect transcription start sites (promoters) in the DNA sequences of a common bacteria, Escherichia coli. The best features are identified through a score-based function to select relevant nucleotides that are directly responsible for promoter recognition, in order maximise the models’ performance. The Gaussian Decision Boundary Estimation based support-vector-machine model is trained with these features and finds the best hyperplane that separates the data into different classes. Throughout this study, promoter regions could be identified with high accuracy 99.9% which is better compared to other state of art algorithms. The comparison of machine learning classification models is another major emphasis of this paper in order to identify the model that most accurately predicts DNA sequence promoters. It provides analysis for further biological research as well as precision medicine.

Deoxyribonucleic acid (DNA) profiling uses various molecular biological techniques to identify, classify, and study the ecology and evolution of organisms based on differences in DNA sequence. These differences in DNA sequence are known as polymorphisms, which may be as simple as a single nucleotide at a given nucleotide position within the genome or as complex as variant numbers of tandem repeat sequences or the presence of an interspersed repetitive element. Individuals, populations or species‐specific DNA polymorphisms are used as targets to generate fingerprint patterns suitable for the differentiation of individuals and populations. Data generated by DNA profiling methods are used to provide quantitative or qualitative information for population genetics, human diseases, diversity, phylogeny, epidemiological, forensics and conservation management purposes. Key Concepts: DNA profiling methodologies allow for the classification of organisms based on polymorphisms in DNA sequences at a given loci. DNA profiling methods that can be used to detect single nucleotide and repetitive element polymorphisms include: amplified and restriction fragment length polymorphisms (AFLP/RFLP), multilocus fingerprinting using hybridisation, random amplified polymorphic DNA and denaturing gradient and pulse field gel electrophoresis (DGGE/PFGE). DNA profiling patterns can be used to identify individuals, determine kinship and genealogy, examine inbreeding, and bottlenecks and differentiate populations.

Deoxyribonucleic acid (DNA) sequencing provides the opportunity to advance prosperity by helping individuals or developing precision medications. The potential application of a zigzag (10, 0) AlN nanotube (AlNNT) as a DNA sequencer is studied herein by using B3LYP-gCP-D3 density functional model chemistry. The energy released after adsorption of DNA nucleobases by the AlNNT lies in the following order: guanine (G, 1.22 eV) > cytosine (C, 1.12) ≈ thymine (T, 1.09) ≫ adenine (A, 0.71). Energy decomposition analysis reveals that the nucleobases that include an oxygen atom (G, C, and T) interact covalently with the AlNNT while the A base adsorbs via a π–π stacking interaction. The adsorption of DNA nucleobases on the AlNNT clearly increases its electrical conductivity, and the sensing response of the AlNNT toward G, C, T, or A is predicted to be 1339, 608, 523, or 248. Thus, an AlNNT could be applied to selectively sense G, C, T, and A, which is conducive to determining the nucleobase sequence of DNA. Short recovery times are also obtained for desorption of the DNA nucleobases.

A New Slant on Old Ideas Do genes define your destiny?The field of epigenetics examines how genes and the environment interact to form the basis of heredity and comes up with some surprising findings.We often think of deoxyribonucleic acid (DNA) as the sole physical basis for heredity, the genetic code that determines everything from eye and hair color to specific personality traits.The growing field of epigenetics suggests that this traditional paradigm is an oversimplification.Epigenetics is the study of factors that are heritable, but which occur by mechanisms other than changes in the DNA code itself.C.H. Waddington first used the term epigenetics in the early 1940s to describe ''the interactions of genes with their environment, which bring the phenotype into being.''Epigenetic marks are susceptible to environmental influencesboth chemical and nonchemical-and can be inherited in ways that may seem counterintuitive (see sidebar, Expanding Ideas About Biological Inheritance).Effects of early life experiences, such as parental care or nutrition, can show up later in life and even be passed on to future generations.This emerging area of research has interesting and important implications for the field of ecotoxicology-from basic science to international policy.In classic genetics, genetic material is in the form of DNA and encodes all of the information necessary for life.

Bacterial deoxyribonucleic acid (DNA) polymerase I is a family of enzymes involved in bacterial DNA synthesis and lesion repair. These enzymes have a multidomain structure, consisting of a single polypeptide chain that encompasses a distinct polymerase domain, a proofreading 3′–5′ exonuclease and/or a 5′–3′ exonuclease activity. Members of the polymerase I family have been investigated extensively, both because of their vital role in replicating and maintaining bacterial chromosomes, and because of their importance as tools in molecular biology, in particular for the polymerase chain reaction (PCR) and DNA sequencing. Studies on the Escherichia coli DNA polymerase I in particular have yielded significant insights into the mechanism of DNA polymerisation which is shared by almost all other nucleotide polymerases. Key Concepts: Bacterial DNA polymerase I enzymes are multidomain proteins with separate polymerisation, proofreading and 5′–3′ exonuclease functions. They perform a vital function in bacteria by carrying out DNA repair and some aspects of chromosomal replication. Polymerases incorporate one nucleotide at a time to the 3′ end of the primer DNA strand in a template‐directed manner. Members of the DNA polymerase I family are able to replicate DNA with far greater accuracy than base pairing alone can account for. The polymerisation reaction is a multistep process during which the enzyme switches between substrate binding and nucleotide incorporation conformations. The 5′–3′ exonuclease allows the polymerase to degrade RNA primers and to assist with DNA repair. Thermostable family I DNA polymerases are used extensively in polymerase chain reaction to amplify DNA and for DNA sequencing.

A novel cryptosystem using DNA sequencing and contextual array splicing system for Medical Internet of Things

An image encryption technology based on multi-dimension chaotic system, spatiotemporal chaotic theory and traditional symmetric encryption algorithm is presented. The scrambling transformation was completed with multi-dimension chaotic system first. Then spatiotemporal chaotic theory was used to deal with gray transformation for each pixel. DES Algorithm was applied to implement image encryption at last. Theoretical analyses and simulated experiment show that the proposed image encryption scheme provides perfect results, larger key space and higher security, so this encryption system has excellent performance against many kinds of attacks.

The chaos based cryptographic algorithms have suggested some new and efficient ways to develop secure image encryption techniques. A new approach to image encryption based on hyperchaotic map which is called coupled map lattices(CML) is proposed in order to meet the requirements of the secure image transfer. The results of several experimental, analysis of correlation of adjacent pixels and key sensitivity tests show that the proposed image encryption scheme provides an efficient and secure way for image encryption and transmission.