Yanrui Ding

A Review of Quantum-behaved Particle Swarm Optimization

Abstract Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm and it has attracted a large number of widespread researchers. As a branch of PSO, a probabilistic PSO algorithm, which is quantum-behaved PSO (QPSO), was proposed on the quantum mechanics and trajectory analysis of PSO. QPSO shines for its simplicity, easy implementation, and fine search ability. QPSO has also gained many researchers on its improvements and has been shown to offer good performance in a variety of applications. This paper attempts to give a compendious and timely review on QPSO by categorizing the publications on the improvements and applications.

Optimizing the codon usage of synthetic gene with QPSO algorithm

Molecular Biology makes it possible to express foreign genes in microorganism, plants and animals. To improve the heterologous expression, it is important that the codon usage of sequence be optimized to make it adaptive to host organism. In this paper, a novel method based on Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is developed to optimize the codon usage of synthetic gene. Compared to the existing probability methods, QPSO is able to generate better results when DNA/RNA sequence length is less than 6Kb which is the commonly used range. While the software or web service based on probability method may not exclude all defined restriction sites when there are many undesired sites in the sequence, our proposed method can remove the undesired site efficiently during the optimization process.

Induction of hypocrellin production by Triton X-100 under submerged fermentation with Shiraia sp. SUPER-H168.

Hypocrellins are important photodynamic therapy compounds for cancer disease. The effect of surfactants on hypocrellin production of Shiraia sp. SUPER-H168 was evaluated under submerged fermentation condition. The production of hypocrellins could reach 780.6 mg/l with the addition of Triton X-100, confirmed by color reaction, high performance liquid chromatography, electrospray ionization mass spectrometry and nuclear magnetic resonance experiments. According to our observation, treatment of the culture at the beginning of the fermentation was most effective, and the yield of hypocrellins was much lower with the addition of Triton X-100 during the log phase and stationary phase. Shiraia sp. SUPER-H168 could not produce hypocrellin with the addition of other tested surfactants, such as Tween 40, Triton X-114 and SDS. The experimental results indicated that Shiraia sp. SUPER-H168 could not produce hypocrellins without Triton X-100 under submerged fermentation condition.

Multiple sequence alignment by improved hidden Markov model training and quantum-behaved particle swarm optimization

Multiple sequence alignment (MSA), known as NP-complete problem, is one of the basic problems in computational biology. Presently, profile hidden Markov model (HMM) is widely used for multiple sequence alignment. In this paper, Quantum-behaved Particle Swarm Optimization (QPSO) is used to train profile HMM. Furthermore, an integration algorithm based on the profile HMM and QPSO for the MSA is proposed. In order to evaluate the approach protein sequences are taken. Finally, compared with other algorithms, the results show that the proposed algorithm not only finds out perfect profile HMM, but also produces the optimal alignment of multiple sequences.

Comparison of Protein-water Interactions in Psychrophilic, Mesophilic, and Thermophilic Fe-SOD

Iron superoxide dismutase (Fe-SOD) can eliminate superoxide anion radicals and is widely used in pharmaceuticals, cosmetics and foodstuff. Its significant to determine the factors that influence Fe-SOD thermostability. Previous studies have focused on the relationship between the structural parameters and thermostability of Fe-SOD while the contribution of water molecules was overlooked. In this study, we examined the relationship between hydration waters and Fe-SOD thermostability. The Voronoi polyhedra method was used to explore the distribution of hydration waters around the Fe-SODs and it was interesting to find that the distribution of hydration waters is related to the B-factor of amino acids, i.e., the flexibility of residues can affect the distribution of waters. Protein-water and water-water hydrogen bonds were examined. We found that the hydrogen bond density in thermophilic Fe-SOD was higher than that in mesophilic Fe- SOD. In addition, larger hydrogen bond networks that involve more waters covered the thermophilic Fe-SOD.