Changjun Zhou

DNA Word Set Design Based on Minimum Free Energy

The problem of data and information encoding on DNA bears an increasing interest for both biological and nonbiological applications of biomolecular computing. Recent experimental and theoretical advances have produced and tested to obtain large code sets of oligonucleotides to support virtually any kind of application. In this paper, we have developed an algorithm to design DNA short-word sets based on minimum free energy (MFE) criteria. The MFE constraint is the minimum value among free energies of all the possible structures and the effective approach to control the generation of unexpected secondary structure of DNA sequences may cause error. The algorithm is constructive and directly produces the actual DNA words of the sets, unlike other academic and statistical numbers. According to the previous values, our experimental results can succeed in generating better DNA word sets based on MFE constraint. More importantly, using our results could decrease the emergence of false hybridization reaction, and improve the reliability and the scale of DNA computing.

Protein folding optimization based on 3D off-lattice model via an improved artificial bee colony algorithm

AbstractProtein folding is a fundamental topic in molecular biology. Conventional experimental techniques for protein structure identification or protein folding recognition require strict laboratory requirements and heavy operating burdens, which have largely limited their applications. Alternatively, computer-aided techniques have been developed to optimize protein structures or to predict the protein folding process. In this paper, we utilize a 3D off-lattice model to describe the original protein folding scheme as a simplified energy-optimal numerical problem, where all types of amino acid residues are binarized into hydrophobic and hydrophilic ones. We apply a balance-evolution artificial bee colony (BE-ABC) algorithm as the minimization solver, which is featured by the adaptive adjustment of search intensity to cater for the varying needs during the entire optimization process. In this work, we establish a benchmark case set with 13 real protein sequences from the Protein Data Bank database and evaluate the convergence performance of BE-ABC algorithm through strict comparisons with several state-of-the-art ABC variants in short-term numerical experiments. Besides that, our obtained best-so-far protein structures are compared to the ones in comprehensive previous literature. This study also provides preliminary insights into how artificial intelligence techniques can be applied to reveal the dynamics of protein folding. Graphical AbstractProtein folding optimization using 3D off-lattice model and advanced optimization techniques

Improved hybrid optimization algorithm for 3D protein structure prediction

A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.

A Summarization on Image Encryption

Abstract With the fast development of the computer technology and information processing technology, the problem of information security is becoming more and more important. Information hiding is usually used to protect the important information from disclosing when it is transmitting over an insecure channel. Digital image encryption is one of the most important methods of image information hiding and camouflage. The image encryption techniques mainly include compression methodology, modern cryptography mechanism, chaos techniques, DNA techniques, and so on. In this paper, we summarize the main encryption algorithms and classify them based on the means. In particular, chaos-based and DNA cryptography-based image encryption algorithms are illustrated and analyzed in detail. Finally, the future direction in this field is discussed.

Parallel DNA Arithmetic Operation With One Error Detection Based on 3-Moduli Set

The redundant residue number system is introduced into DNA computing in order to overcome the negative effect caused by the instability of the biochemical reactions and the error hybridizations. Based on the Adleman-Lipton model and a special 3-moduli set, the DNA encoding scheme for redundant residue numbers is presented and the DNA algorithm of one-digit error detection is proposed. The parallel DNA arithmetic operation can be realized in redundant residue number system with error detection, and which can improve the reliability of DNA computing and simplify the DNA encoding scheme.

Enhanced hybrid search algorithm for protein structure prediction using the 3D-HP lattice model

The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper presents a method for the application of an enhanced hybrid search algorithm to the problem of protein folding prediction, using the three dimensional (3D) HP lattice model. The enhanced hybrid search algorithm is a combination of the particle swarm optimizer (PSO) and tabu search (TS) algorithms. Since the PSO algorithm entraps local minimum in later evolution extremely easily, we combined PSO with the TS algorithm, which has properties of global optimization. Since the technologies of crossover and mutation are applied many times to PSO and TS algorithms, so enhanced hybrid search algorithm is called the MCMPSO-TS (multiple crossover and mutation PSO-TS) algorithm. Experimental results show that the MCMPSO-TS algorithm can find the best solutions so far for the listed benchmarks, which will help comparison with any future paper approach. Moreover, real protein sequences and Fibonacci sequences are verified in the 3D HP lattice model for the first time. Compared with the previous evolutionary algorithms, the new hybrid search algorithm is novel, and can be used effectively to predict 3D protein folding structure. With continuous development and changes in amino acids sequences, the new algorithm will also make a contribution to the study of new protein sequences.

Constructing DNA Barcode Sets Based on Particle Swarm Optimization

Following the completion of the human genome project, a large amount of high-throughput bio-data was generated. To analyze these data, massively parallel sequencing, namely next-generation sequencing, was rapidly developed. DNA barcodes are used to identify the ownership between sequences and samples when they are attached at the beginning or end of sequencing reads. Constructing DNA barcode sets provides the candidate DNA barcodes for this application. To increase the accuracy of DNA barcode sets, a particle swarm optimization (PSO) algorithm has been modified and used to construct the DNA barcode sets in this paper. Compared with the extant results, some lower bounds of DNA barcode sets are improved. The results show that the proposed algorithm is effective in constructing DNA barcode sets.

On the simulation of expressional animation based on facial MoCap

MoCap (motion capture)-based animation is a hot issue in computer animation research currently. Based on the optical MoCap system, this paper proposes a novel cross-mapping based facial expression simulating method. To overcome the problem of the false upper and lower jaw correlation derived from the facial global RBF-based cross-mapping method, we construct a functional partition based RBF cross-mapping method. During model animating, enhanced markers are added and animated by our proposed skin motion mechanism. In addition, based on the enhanced markers, an improved RBF-based animating approach is raised to derive realistic facial animation. Further more, a pre-computing algorithm is presented to reduce computational cost for real-time simulation. The experiments proved that the method can not only map the MoCap data of one subject to different personalized faces but generate realistic facial animation.

A novel constraint for thermodynamically designing DNA sequences.

Biotechnological and biomolecular advances have introduced novel uses for DNA such as DNA computing, storage, and encryption. For these applications, DNA sequence design requires maximal desired (and minimal undesired) hybridizations, which are the product of a single new DNA strand from 2 single DNA strands. Here, we propose a novel constraint to design DNA sequences based on thermodynamic properties. Existing constraints for DNA design are based on the Hamming distance, a constraint that does not address the thermodynamic properties of the DNA sequence. Using a unique, improved genetic algorithm, we designed DNA sequence sets which satisfy different distance constraints and employ a free energy gap based on a minimum free energy (MFE) to gauge DNA sequences based on set thermodynamic properties. When compared to the best constraints of the Hamming distance, our method yielded better thermodynamic qualities. We then used our improved genetic algorithm to obtain lower-bound DNA sequence sets. Here, we discuss the effects of novel constraint parameters on the free energy gap.

Correcting Errors in Image Encryption Based on DNA Coding

As a primary method, image encryption is widely used to protect the security of image information. In recent years, image encryption pays attention to the combination with DNA computing. In this work, we propose a novel method to correct errors in image encryption, which results from the uncertainty of DNA computing. DNA coding is the key step for DNA computing that could decrease the similarity of DNA sequences in DNA computing as well as correct errors from the process of image encryption and decryption. The experimental results show our method could be used to correct errors in image encryption based on DNA coding.