Reference Point Based Multi-objective Evolutionary Algorithm for DNA Sequence Design

DNA computing relies on biochemical reactions of DNA molecules and may result in incorrect or undesirable computations. Therefore, much work has focused on designing the DNA sequences to make the molecular computation more reliable. Sequence design involves with a number of heterogeneous and conflicting design criteria and traditional optimization methods may face difficulties. In this paper, we formulate the DNA sequence design as a multiobjective optimization problem and solve it using a constrained multiobjective evolutionary algorithm (EA). The method is implemented into the DNA sequence design system, NACST/Seq, with a suite of sequence-analysis tools to help choose the best solutions among many alternatives. The performance of NACST/Seq is compared with other sequence design methods, and analyzed on a traveling salesman problem solved by bio-lab experiments. Our experimental results show that the evolutionary sequence design by NACST/Seq outperforms in its reliability the existing sequence design techniques such as conventional EAs, simulated annealing, and specialized heuristic methods.

Since the quantity and quality of DNA sequence directly affect the accuracy and efficiency of computation, the design of DNA sequence is critical to DNA computing. In order to improve the reliability of DNA computing, there is a rich literature targeting at making DNA sequences specifically hybridize at a lower melting temperature, no non-complementary bases pairs or mismatch hybridization in the reformed double helix. However, most of them are not good enough to control the melting temperature, because DNA sequence design problem under the constraints of hamming distance, secondary structure, molecular thermodynamic is known to be NP-hard. For the sake of achieving the lower and similar melting temperature for each DNA sequence, we proposed a DNA sequence coding method based on Bacterial Foraging Algorithm (BFA). An evaluation criterion is particularly proposed to assess the quality of DNA sequence in the optimization process. With BFA, high-quality DNA strands are replicated to avoid the participation of inferior strands in the operation. Experiments show our proposed approach significantly outperforms existing methods in terms of continuity and melting temperature.

DNA computing is a new method that uses biological molecule DNA as computing medium and biochemical reaction as computing tools. The design of DNA sequence plays an important role in improving the reliability of DNA computation. The sequence design is an approach of the control, which aims to design of DNA sequences satisfying some constraints to avoid such unexpected molecular reactions .To deal these constraints ,we convert the design of DNA sequences into a multi-objective optimize problems, and then an example is illustrated to show the efficiency of our method given here.

A membrane evolutionary algorithm for DNA sequence design in DNA computing

In DNA computing, the design of reliable DNA sequences is one of the most important tasks, because sequences used for that purpose have to fulfill several biochemical restrictions to avoid undesirable reactions. These reactions usually cause incorrect computations, so it is necessary to avoid this circumstance by generating robust and reliable sets of sequences. In this paper, we consider six different conflicting design criteria to solve the problem by using the multiobjective fast non dominated sorting genetic algorithm (NSGA-II). Our proposal is compared against other sequence design methods, and our results show that our version of NSGA-II outperforms in reliability other existing techniques published in the literature.

Molecular computing has proved its possibility to solve weighted graph problem such as Hamiltonian Path Problem (HPP), Traveling Salesman Problem (TSP) and the Shortest Path Problem (SPP). Normally, in molecular computation, the DNA sequences used for the computation should be critically designed in order to reduce error that could occur during computation. In the previous paper, we have proposed a readout method tailored specifically to HPP in DNA Computing using real-time PCR for output visualization. Six nodes of HPP was considered. Based on the example instance, the method requires 11 oligonucleotides, where 6 oligonucleotides are for the nodes and 5 oligonucleotides are for the edges. Three TaqMan probes and five primers are required as well. In this study, a procedure for DNA sequence design is presented in order to obtain good sequences for those nodes, primers, and probes. The experiment is done based on the generated DNA sequences and the Hamiltonian Path can be determined successfully.

The process of designing a set of good DNA sequences is an essential problem and one of the most practical and important research topics in DNA-based computing. In this field of research, a DNA sequence design problem is defined as a multi-objective problem, and it is evaluated using four objective functions, which are h-measure, similarity, continuity, and hairpin. In addition, two constraints, GC content and melting temperature, are used to maintain uniform chemical characteristics of the sequences. In the authors' previous research, ant colony system (ACS) was proposed to solve the DNA sequence design problem and the Watson-Crick base pair ÄGo37 was used as the distance between nodes in the computational model of DNA sequence design. In the current study, the use of other parameters, namely, unified near estneighbour thermodynamic parameters, are investigated and the result is compared with the previous work.

The process of designing a set of good DNA sequences is an essential problem and one of the most practical and important research topics in DNA-based computing. In this field of research, a DNA sequence design problem is defined as a multi-objective problem, and it is evaluated using four objective functions, which are h-measure, similarity, continuity, and hairpin. In addition, two constraints, GC content and melting temperature, are used to maintain uniform chemical characteristics of the sequences. In the authors' previous research, ant colony system (ACS) was proposed to solve the DNA sequence design problem and the Watson-Crick base pair ΔGo37 was used as the distance between nodes in the computational model of DNA sequence design. In the current study, the use of other parameters, namely, Breslauer nearest-neighbour thermodynamic parameters are investigated and the result is compared with the previous work.

Multi-objective optimization problems (MOP) are frequently encountered in practice. In some cases, different computationally expensive analyses may be independently used for computing different objectives of the MOP. Additionally, the analyses may be executed to obtain estimates with different fidelity, typically higher fidelity requiring a longer run-time. For instance, in automotive design, the aerodynamic drag is computed using computational fluid dynamic (CFD) analysis and its crashworthiness/strength is assessed using finite element analysis (FEA). Both the objectives can be independently computed and the underlying fidelity of each analysis can also be controlled using different mesh sizes/thresholds on the residual errors. While there exist a number of generic MOP benchmark problems in the literature, there is scarce work on constructing MOPs with multi-fidelity (MF) analyses to support the development of multi-fidelity, multi-objective optimization algorithms. The existing MF benchmarks are limited to unconstrained, single-objective optimization problems only. Towards addressing this gap, in this paper, we introduce a test suite for multi-objective, multi-fidelity optimization (MOMF). The problems are derived by combining existing unconstrained, multi-objective design optimization problems with resolution/stochastic/instability errors that are common manifestations of MF simulations. The method allows for the construction of any number of low-fidelity functions with desired level of correlations for a given high-fidelity objective function. We hope that the test suite would motivate novel algorithmic developments to support optimization involving computationally expensive and independently evaluable objectives.

Although various optimization methods for solving single-objective problems have been developed in the last few decades, these methods have lost their eligibility due to the fact that today’s engineering problems are toward multiple objective optimization problems, in real applications. For single-objective optimization problems, for example, in case of a minimization problem, this value is the decision vector giving the smallest objective that can be achieved within the specified constraints. Hence the minimum decision vector within all possible (feasible) solution vectors is the so-called optimal solution and/or optimal design. However, in multi-objective optimization problems, since a different objective value is generated against each decision vector, the superiority of the solutions over each other is determined by considering the trade-off among the objective values. Therefore, the solution of multi-objective optimization problems, unlike single-objective problems, is a set of vectors rather than a single decision vector. In multi-objective optimization problems, especially if there are intricate objectives, the computational cost of the problem increases. In other words, while synchronously trying to maximize one of the objectives and to minimize another one makes it difficult to find the global optimum design. One of the important techniques used in multi-objective optimization problems is Pareto optimality which enables to select the global optimum solution taking into account the trade-off among all objectives. In this context, using of derivative-based methods has decreased, but the use of metaheuristic methods has increased due to the rapid availability of global optimum solution. This is because the improvements in the field of optimization are progressing in proportion to technology and varying according to the needs. In this chapter, one of the recent metaheuristic optimization methods based on swarm intelligence that is so-called a Pareto-based bat algorithm inspired by the behavior of determining the direction and distance of an object using the echo of the sound called the echolocation of bats is used in order to obtain optimum solutions for multi-objective engineering design problems. In this regard, a four-bar planar truss, a real-sized welded steel beam as well as a multi-layer radar absorber are selected as multi-objective engineering design optimization problems. In case the obtained results (optimal designs) are examined, the potency and the reliability of the proposed multi-objective Pareto-based bat algorithm are demonstrated.

In a DNA sequence, we have the celebrated Watson-Crick complement $\overline{T}=A, \overline{A}= {T}, \overline{C}= G$, and $\overline{G}= C$. The phenomenon of secondary structure refers to the tendency of a single stranded DNA sequence to fold back upon itself, and it is usually caused by the existence of two non-overlapping reverse complement consecutive subsequences, defined as follows. Given an integer $m \geq 2$, two non-overlapping consecutive subsequences of length m, denoted as $x=(x_{1}, x_{m})$ and $y=(y_{1}, y_{m})$, are called reverse complement if $x_{i}=\overline{y_{m-i+1}}$ for 1$\leq i \leq m$. The property of secondary structure avoidance (SSA) forbids a sequence to contain such reverse complement subsequences, and it is a key criterion in the design of single-stranded DNA sequences for DNA computing and storage. In this paper, we improve on a recent result of Nguyen et al., by introducing explicit constructions of secondary structure avoidance codes and analyzing the capacity for any given m. In particular, our constructions have optimal rate 1.1679bits/nt and 1.5515bits/nt when m = 2 and m + 3, respectively.

In the field of preference-based evolutionary multiobjective optimization, optimization algorithms are required to search for the Pareto optimal solutions preferred by the decision maker (DM). The reference point is a type of techniques that effectively describe the preferences of DM. So far, the reference point is either static or interactive with the evolutionary process. However, the existing reference point techniques do not cover all application scenarios. A novel case, i.e., the reference point changes over time due to the environment change, has not been considered. This paper focuses on the multiobjective optimization problems with dynamic preferences of the DM. First, we propose a change model of the reference point to simulate the change of the preference by the DM over time. Then, a dynamic preference-based multiobjective evolutionary algorithm framework with a clonal selection algorithm (ĝa-NSCSA) and a genetic algorithm (ĝa-NSGA-II) is designed to solve such kind of optimization problems. In addition, in terms of practical applications, the experiments on the portfolio optimization problems with the dynamic reference point model are tested. Experimental results on the benchmark problems and the practical applications show that ĝa-NSCSA exhibits better performance among the compared optimization algorithms.

The design of DNA sequences is critical for many research fields such as DNA self-assembly, DNA hybridization arrays, DNA computing, and PCR-based applications. DNA secondary structure prediction is the key part for these DNA nanotechnologies. In this paper, we present a dynamic programming algorithm to predict the secondary structure of single-stranded DNA tiles. The algorithm calculates all possible maximum matches based on the nearest-neighbour model and global energy minimization. Experimental results show that the algorithm performers significantly to predict secondary str uctures for single-stranded DNA tiles.

Rational design of plant cis-regulatory DNA sequences without expert intervention or prior domain knowledge is still a daunting task. Here, we developed PhytoExpr, a deep learning framework capable of predicting both mRNA abundance and plant species using the proximal regulatory sequence as the sole input. PhytoExpr was trained over 17 species representative of major clades of the plant kingdom to enhance its generalizability. Via input perturbation, quantitative functional annotation of the input sequence was achieved at single-nucleotide resolution, revealing an abundance of predicted high-impact nucleotides in conserved noncoding sequences and transcription factor binding sites. Evaluation of maize HapMap3 single-nucleotide polymorphisms (SNPs) by PhytoExpr demonstrates an enrichment of predicted high-impact SNPs in cis-eQTL. Additionally, we provided two algorithms that harnessed the power of PhytoExpr in designing functional cis-regulatory variants, and de novo creation of species-specific cis-regulatory sequences through in silico evolution of random DNA sequences. Our model represents a general and robust approach for functional variant discovery in population genetics and rational design of regulatory sequences for genome editing and synthetic biology.

This paper deals with the multi-objective optimization of tire reinforcement structures such as the tread belt and the carcass path. The multi-objective functions are defined in terms of the discrete-type design variables and approximated by artificial neutral network, and the sensitivity analyses of these functions are replaced with the iterative genetic evolution. The multi-objective optimization algorithm introduced in this paper is not only highly CPU-time-efficient but it can also be applicable to other multi-objective optimization problems in which the objective function, the design variables and the constraints are not continuous but discrete. Through the illustrative numerical experiments, the fiber-reinforced tire belt structure is optimally tailored. The proposed multi-objective optimization algorithm is not limited to the tire reinforcement structure, but it can be applicable to the generalized multi-objective structural optimization problems in various engineering applications.