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A review on genetic algorithm: past, present, and future

• Published: 31 October 2020
• Volume 80 , pages 8091–8126, ( 2021 )

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• Sourabh Katoch 1 ,
• Sumit Singh Chauhan 1 &
• Vijay Kumar   ORCID: orcid.org/0000-0002-3460-6989 1  

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In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

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1 Introduction

In the recent years, metaheuristic algorithms are used to solve real-life complex problems arising from different fields such as economics, engineering, politics, management, and engineering [ 113 ]. Intensification and diversification are the key elements of metaheuristic algorithm. The proper balance between these elements are required to solve the real-life problem in an effective manner. Most of metaheuristic algorithms are inspired from biological evolution process, swarm behavior, and physics’ law [ 17 ]. These algorithms are broadly classified into two categories namely single solution and population based metaheuristic algorithm (Fig.  1 ). Single-solution based metaheuristic algorithms utilize single candidate solution and improve this solution by using local search. However, the solution obtained from single-solution based metaheuristics may stuck in local optima [ 112 ]. The well-known single-solution based metaheuristics are simulated annealing, tabu search (TS), microcanonical annealing (MA), and guided local search (GLS). Population-based metaheuristics utilizes multiple candidate solutions during the search process. These metaheuristics maintain the diversity in population and avoid the solutions are being stuck in local optima. Some of well-known population-based metaheuristic algorithms are genetic algorithm (GA) [ 135 ], particle swarm optimization (PSO) [ 101 ], ant colony optimization (ACO) [ 47 ], spotted hyena optimizer (SHO) [ 41 ], emperor penguin optimizer (EPO) [ 42 ], and seagull optimization (SOA) [ 43 ].

Classification of metaheuristic Algorithms

Among the metaheuristic algorithms, Genetic algorithm (GA) is a well-known algorithm, which is inspired from biological evolution process [ 136 ]. GA mimics the Darwinian theory of survival of fittest in nature. GA was proposed by J.H. Holland in 1992. The basic elements of GA are chromosome representation, fitness selection, and biological-inspired operators. Holland also introduced a novel element namely, Inversion that is generally used in implementations of GA [ 77 ]. Typically, the chromosomes take the binary string format. In chromosomes, each locus (specific position on chromosome) has two possible alleles (variant forms of genes) - 0 and 1. Chromosomes are considered as points in the solution space. These are processed using genetic operators by iteratively replacing its population. The fitness function is used to assign a value for all the chromosomes in the population [ 136 ]. The biological-inspired operators are selection, mutation, and crossover. In selection, the chromosomes are selected on the basis of its fitness value for further processing. In crossover operator, a random locus is chosen and it changes the subsequences between chromosomes to create off-springs. In mutation, some bits of the chromosomes will be randomly flipped on the basis of probability [ 77 , 135 , 136 ]. The further development of GA based on operators, representation, and fitness has diminished. Therefore, these elements of GA are focused in this paper.

The main contribution of this paper are as follows:

The general framework of GA and hybrid GA are elaborated with mathematical formulation.

The various types of genetic operators are discussed with their pros and cons.

The variants of GA with their pros and cons are discussed.

The applicability of GA in multimedia fields is discussed.

The main aim of this paper is two folds. First, it presents the variants of GA and their applicability in various fields. Second, it broadens the area of possible users in various fields. The various types of crossover, mutation, selection, and encoding techniques are discussed. The single-objective, multi-objective, parallel, and hybrid GAs are deliberated with their advantages and disadvantages. The multimedia applications of GAs are elaborated.

The remainder of this paper is organized as follows: Section 2 presents the methodology used to carry out the research. The classical genetic algorithm and genetic operators are discussed in Section 3 . The variants of genetic algorithm with pros and cons are presented in Section 4 . Section 5 describes the applications of genetic algorithm. Section 6 presents the challenges and future research directions. The concluding remarks are drawn in Section 7 .

2 Research methodology

PRISMA’s guidelines were used to conduct the review of GA [ 138 ]. A detailed search has been done on Google scholar and PubMed for identification of research papers related to GA. The important research works found during the manual search were also added in this paper. During search, some keywords such as “Genetic Algorithm” or “Application of GA” or “operators of GA” or “representation of GA” or “variants of GA” were used. The selection and rejection of explored research papers are based on the principles, which is mentioned in Table 1 .

Total 27,64,792 research papers were explored on Google Scholar, PubMed and manual search. The research work related to genetic algorithm for multimedia applications were also included. During the screening of research papers, all the duplicate papers and papers published before 2007 were discarded. 4340 research papers were selected based on 2007 and duplicate entries. Thereafter, 4050 research papers were eliminated based on titles. 220 research papers were eliminated after reading of abstract. 70 research papers were left after third round of screening. 40 more research papers were discarded after full paper reading and facts found in the papers. After the fourth round of screening, final 30 research papers are selected for review.

Based on the relevance and quality of research, 30 papers were selected for evaluation. The relevance of research is decided through some criteria, which is mentioned in Table 1 . The selected research papers comprise of genetic algorithm for multimedia applications, advancement of their genetic operators, and hybridization of genetic algorithm with other well-established metaheuristic algorithms. The pros and cons of genetic operators are shown in preceding section.

3 Background

In this section, the basic structure of GA and its genetic operators are discussed with pros and cons.

3.1 Classical GA

Genetic algorithm (GA) is an optimization algorithm that is inspired from the natural selection. It is a population based search algorithm, which utilizes the concept of survival of fittest [ 135 ]. The new populations are produced by iterative use of genetic operators on individuals present in the population. The chromosome representation, selection, crossover, mutation, and fitness function computation are the key elements of GA. The procedure of GA is as follows. A population ( Y ) of n chromosomes are initialized randomly. The fitness of each chromosome in Y is computed. Two chromosomes say C1 and C2 are selected from the population Y according to the fitness value. The single-point crossover operator with crossover probability (C p ) is applied on C1 and C2 to produce an offspring say O . Thereafter, uniform mutation operator is applied on produced offspring ( O ) with mutation probability (M p ) to generate O′ . The new offspring O′ is placed in new population. The selection, crossover, and mutation operations will be repeated on current population until the new population is complete. The mathematical analysis of GA is as follows [ 126 ]:

GA dynamically change the search process through the probabilities of crossover and mutation and reached to optimal solution. GA can modify the encoded genes. GA can evaluate multiple individuals and produce multiple optimal solutions. Hence, GA has better global search capability. The offspring produced from crossover of parent chromosomes is probable to abolish the admirable genetic schemas parent chromosomes and crossover formula is defined as [ 126 ]:

where g is the number of generations, and G is the total number of evolutionary generation set by population. It is observed from Eq.( 1 ) that R is dynamically changed and increase with increase in number of evolutionary generation. In initial stage of GA, the similarity between individuals is very low. The value of R should be low to ensure that the new population will not destroy the excellent genetic schema of individuals. At the end of evolution, the similarity between individuals is very high as well as the value of R should be high.

According to Schema theorem, the original schema has to be replaced with modified schema. To maintain the diversity in population, the new schema keep the initial population during the early stage of evolution. At the end of evolution, the appropriate schema will be produced to prevent any distortion of excellent genetic schema [ 65 , 75 ]. Algorithm 1 shows the pseudocode of classical genetic algorithm.

Algorithm 1: Classical Genetic Algorithm (GA)

3.2 Genetic operators

GAs used a variety of operators during the search process. These operators are encoding schemes, crossover, mutation, and selection. Figure 2 depicts the operators used in GAs.

Operators used in GA

3.2.1 Encoding schemes

For most of the computational problems, the encoding scheme (i.e., to convert in particular form) plays an important role. The given information has to be encoded in a particular bit string [ 121 , 183 ]. The encoding schemes are differentiated according to the problem domain. The well-known encoding schemes are binary, octal, hexadecimal, permutation, value-based, and tree.

Binary encoding is the commonly used encoding scheme. Each gene or chromosome is represented as a string of 1 or 0 [ 187 ]. In binary encoding, each bit represents the characteristics of the solution. It provides faster implementation of crossover and mutation operators. However, it requires extra effort to convert into binary form and accuracy of algorithm depends upon the binary conversion. The bit stream is changed according the problem. Binary encoding scheme is not appropriate for some engineering design problems due to epistasis and natural representation.

In octal encoding scheme, the gene or chromosome is represented in the form of octal numbers (0–7). In hexadecimal encoding scheme, the gene or chromosome is represented in the form of hexadecimal numbers (0–9, A-F) [ 111 , 125 , 187 ]. The permutation encoding scheme is generally used in ordering problems. In this encoding scheme, the gene or chromosome is represented by the string of numbers that represents the position in a sequence. In value encoding scheme, the gene or chromosome is represented using string of some values. These values can be real, integer number, or character [ 57 ]. This encoding scheme can be helpful in solving the problems in which more complicated values are used. As binary encoding may fail in such problems. It is mainly used in neural networks for finding the optimal weights.

In tree encoding, the gene or chromosome is represented by a tree of functions or commands. These functions and commands can be related to any programming language. This is very much similar to the representation of repression in tree format [ 88 ]. This type of encoding is generally used in evolving programs or expressions. Table 2 shows the comparison of different encoding schemes of GA.

3.2.2 Selection techniques

Selection is an important step in genetic algorithms that determines whether the particular string will participate in the reproduction process or not. The selection step is sometimes also known as the reproduction operator [ 57 , 88 ]. The convergence rate of GA depends upon the selection pressure. The well-known selection techniques are roulette wheel, rank, tournament, boltzmann, and stochastic universal sampling.

Roulette wheel selection maps all the possible strings onto a wheel with a portion of the wheel allocated to them according to their fitness value. This wheel is then rotated randomly to select specific solutions that will participate in formation of the next generation [ 88 ]. However, it suffers from many problems such as errors introduced by its stochastic nature. De Jong and Brindle modified the roulette wheel selection method to remove errors by introducing the concept of determinism in selection procedure. Rank selection is the modified form of Roulette wheel selection. It utilizes the ranks instead of fitness value. Ranks are given to them according to their fitness value so that each individual gets a chance of getting selected according to their ranks. Rank selection method reduces the chances of prematurely converging the solution to a local minima [ 88 ].

Tournament selection technique was first proposed by Brindle in 1983. The individuals are selected according to their fitness values from a stochastic roulette wheel in pairs. After selection, the individuals with higher fitness value are added to the pool of next generation [ 88 ]. In this method of selection, each individual is compared with all n-1 other individuals if it reaches the final population of solutions [ 88 ]. Stochastic universal sampling (SUS) is an extension to the existing roulette wheel selection method. It uses a random starting point in the list of individuals from a generation and selects the new individual at evenly spaced intervals [ 3 ]. It gives equal chance to all the individuals in getting selected for participating in crossover for the next generation. Although in case of Travelling Salesman Problem, SUS performs well but as the problem size increases, the traditional Roulette wheel selection performs relatively well [ 180 ].

Boltzmann selection is based on entropy and sampling methods, which are used in Monte Carlo Simulation. It helps in solving the problem of premature convergence [ 118 ]. The probability is very high for selecting the best string, while it executes in very less time. However, there is a possibility of information loss. It can be managed through elitism [ 175 ]. Elitism selection was proposed by K. D. Jong (1975) for improving the performance of Roulette wheel selection. It ensures the elitist individual in a generation is always propagated to the next generation. If the individual having the highest fitness value is not present in the next generation after normal selection procedure, then the elitist one is also included in the next generation automatically [ 88 ]. The comparison of above-mentioned selection techniques are depicted in Table 3 .

3.2.3 Crossover operators

Crossover operators are used to generate the offspring by combining the genetic information of two or more parents. The well-known crossover operators are single-point, two-point, k-point, uniform, partially matched, order, precedence preserving crossover, shuffle, reduced surrogate and cycle.

In a single point crossover, a random crossover point is selected. The genetic information of two parents which is beyond that point will be swapped with each other [ 190 ]. Figure 3 shows the genetic information after swapping. It replaced the tail array bits of both the parents to get the new offspring.

Swapping genetic information after a crossover point

In a two point and k-point crossover, two or more random crossover points are selected and the genetic information of parents will be swapped as per the segments that have been created [ 190 ]. Figure 4 shows the swapping of genetic information between crossover points. The middle segment of the parents is replaced to generate the new offspring.

Swapping genetic information between crossover points

In a uniform crossover, parent cannot be decomposed into segments. The parent can be treated as each gene separately. We randomly decide whether we need to swap the gene with the same location of another chromosome [ 190 ]. Figure 5 depicts the swapping of individuals under uniform crossover operation.

Swapping individual genes

Partially matched crossover (PMX) is the most frequently used crossover operator. It is an operator that performs better than most of the other crossover operators. The partially matched (mapped) crossover was proposed by D. Goldberg and R. Lingle [ 66 ]. Two parents are choose for mating. One parent donates some part of genetic material and the corresponding part of other parent participates in the child. Once this process is completed, the left out alleles are copied from the second parent [ 83 ]. Figure 6 depicts the example of PMX.

Partially matched crossover (PMX) [ 117 ]

Order crossover (OX) was proposed by Davis in 1985. OX copies one (or more) parts of parent to the offspring from the selected cut-points and fills the remaining space with values other than the ones included in the copied section. The variants of OX are proposed by different researchers for different type of problems. OX is useful for ordering problems [ 166 ]. However, it is found that OX is less efficient in case of Travelling Salesman Problem [ 140 ]. Precedence preserving crossover (PPX) preserves the ordering of individual solutions as present in the parent of offspring before the application of crossover. The offspring is initialized to a string of random 1’s and 0’s that decides whether the individuals from both parents are to be selected or not. In [ 169 ], authors proposed a modified version of PPX for multi-objective scheduling problems.

Shuffle crossover was proposed by Eshelman et al. [ 20 ] to reduce the bias introduced by other crossover techniques. It shuffles the values of an individual solution before the crossover and unshuffles them after crossover operation is performed so that the crossover point does not introduce any bias in crossover. However, the utilization of this crossover is very limited in the recent years. Reduced surrogate crossover (RCX) reduces the unnecessary crossovers if the parents have the same gene sequence for solution representations [ 20 , 139 ]. RCX is based on the assumption that GA produces better individuals if the parents are sufficiently diverse in their genetic composition. However, RCX cannot produce better individuals for those parents that have same composition. Cycle crossover was proposed by Oliver [ 140 ]. It attempts to generate an offspring using parents where each element occupies the position by referring to the position of their parents [ 140 ]. In the first cycle, it takes some elements from the first parent. In the second cycle, it takes the remaining elements from the second parent as shown in Fig.  7 .

Cycle Crossover (CX) [ 140 ]

Table 4 shows the comparison of crossover techniques. It is observed from Table 4 that single and k-point crossover techniques are easy to implement. Uniform crossover is suitable for large subsets. Order and cycle crossovers provide better exploration than the other crossover techniques. Partially matched crossover provides better exploration. The performance of partially matched crossover is better than the other crossover techniques. Reduced surrogate and cycle crossovers suffer from premature convergence.

3.2.4 Mutation operators

Mutation is an operator that maintains the genetic diversity from one population to the next population. The well-known mutation operators are displacement, simple inversion, and scramble mutation. Displacement mutation (DM) operator displaces a substring of a given individual solution within itself. The place is randomly chosen from the given substring for displacement such that the resulting solution is valid as well as a random displacement mutation. There are variants of DM are exchange mutation and insertion mutation. In Exchange mutation and insertion mutation operators, a part of an individual solution is either exchanged with another part or inserted in another location, respectively [ 88 ].

The simple inversion mutation operator (SIM) reverses the substring between any two specified locations in an individual solution. SIM is an inversion operator that reverses the randomly selected string and places it at a random location [ 88 ]. The scramble mutation (SM) operator places the elements in a specified range of the individual solution in a random order and checks whether the fitness value of the recently generated solution is improved or not [ 88 ]. Table 5 shows the comparison of different mutation techniques.

Table 6 shows the best combination of encoding scheme, mutation, and crossover techniques. It is observed from Table 6 that uniform and single-point crossovers can be used with most of encoding and mutation operators. Partially matched crossover is used with inversion mutation and permutation encoding scheme provides the optimal solution.

4 Variants of GA

Various variants of GA’s have been proposed by researchers. The variants of GA are broadly classified into five main categories namely, real and binary coded, multiobjective, parallel, chaotic, and hybrid GAs. The pros and cons of these algorithms with their application has been discussed in the preceding subsections.

4.1 Real and binary coded GAs

Based on the representation of chromosomes, GAs are categorized in two classes, namely binary and real coded GAs.

4.1.1 Binary coded GAs

The binary representation was used to encode GA and known as binary GA. The genetic operators were also modified to carry out the search process. Payne and Glen [ 153 ] developed a binary GA to identify the similarity among molecules. They used binary representation for position of molecule and their conformations. However, this method has high computational complexity. Longyan et al. [ 203 ] investigated three different method for wind farm design using binary GA (BGA). Their method produced better fitness value and farm efficiency. Shukla et al. [ 185 ] utilized BGA for feature subset selection. They used mutual information maximization concept for selecting the significant features. BGAs suffer from Hamming cliffs, uneven schema, and difficulty in achieving precision [ 116 , 199 ].

4.1.2 Real-coded GAs

Real-coded GAs (RGAs) have been widely used in various real-life applications. The representation of chromosomes is closely associated with real-life problems. The main advantages of RGAs are robust, efficient, and accurate. However, RGAs suffer from premature convergence. Researchers are working on RGAs to improve their performance. Most of RGAs are developed by modifying the crossover, mutation and selection operators.

Crossover operators

The searching capability of crossover operators are not satisfactory for continuous search space. The developments in crossover operators have been done to enhance their performance in real environment. Wright [ 210 ] presented a heuristics crossover that was applied on parents to produce off-spring. Michalewicz [ 135 ] proposed arithmetical crossover operators for RGAs. Deb and Agrawal [ 34 ] developed a real-coded crossover operator, which is based on characteristics of single-point crossover in BGA. The developed crossover operator named as simulated binary crossover (SBX). SBX is able to overcome the Hamming cliff, precision, and fixed mapping problem. The performance of SBX is not satisfactory in two-variable blocked function. Eshelman et al. [ 53 ] utilized the schemata concept to design the blend crossover for RGAs. The unimodal normal distribution crossover operator (UNDX) was developed by Ono et al. [ 144 ]. They used ellipsoidal probability distribution to generate the offspring. Kita et al. [ 106 ] presented a multi-parent UNDX (MP-UNDX), which is the extension of [ 144 ]. However, the performance of RGA with MP-UNDX is much similar to UNDX. Deep and Thakur [ 39 ] presented a Laplace crossover for RGAs, which is based on Laplacian distribution. Chuang et al. [ 27 ] developed a direction based crossover to further explore the all possible search directions. However, the search directions are limited. The heuristic normal distribution crossover operator was developed by Wang et al. [ 207 ]. It generates the cross-generated offspring for better search operation. However, the better individuals are not considered in this approach. Subbaraj et al. [ 192 ] proposed Taguchi self-adaptive RCGA. They used Taguchi method and simulated binary crossover to exploit the capable offspring.

Mutation operators

Mutation operators generate diversity in the population. The two main challenges have to tackle during the application of mutation. First, the probability of mutation operator that was applied on population. Second, the outlier produced in chromosome after mutation process. Michalewicz [ 135 ] presented uniform and non-uniform mutation operators for RGAs. Michalewicz and Schoenauer [ 136 ] developed a special case of uniform mutation. They developed boundary mutation. Deep and Thakur [ 38 ] presented a novel mutation operator based on power law and named as power mutation. Das and Pratihar [ 30 ] presented direction-based exponential mutation operator. They used direction information of variables. Tang and Tseng [ 196 ] presented a novel mutation operator for enhancing the performance of RCGA. Their approach was fast and reliable. However, it stuck in local optima for some applications. Deb et al. [ 35 ] developed polynomial mutation that was used in RCGA. It provides better exploration. However, the convergence speed is slow and stuck in local optima. Lucasius et al. [ 129 ] proposed a real-coded genetic algorithm (RCGA). It is simple and easy to implement. However, it suffers from local optima problem. Wang et al. [ 205 ] developed multi-offspring GA and investigated their performance over single point crossover. Wang et al. [ 206 ] stated the theoretical basis of multi-offspring GA. The performance of this method is better than non-multi-offspring GA. Pattanaik et al. [ 152 ] presented an improvement in the RCGA. Their method has better convergence speed and quality of solution. Wang et al. [ 208 ] proposed multi-offspring RCGA with direction based crossover for solving constrained problems.

Table 7 shows the mathematical formulation of genetic operators in RGAs.

4.2 Multiobjective GAs

Multiobjective GA (MOGA) is the modified version of simple GA. MOGA differ from GA in terms of fitness function assignment. The remaining steps are similar to GA. The main motive of multiobjective GA is to generate the optimal Pareto Front in the objective space in such a way that no further enhancement in any fitness function without disturbing the other fitness functions [ 123 ]. Convergence, diversity, and coverage are main goal of multiobjective GAs. The multiobjective GAs are broadly categorized into two categories namely, Pareto-based, and decomposition-based multiobjective GAs [ 52 ]. These techniques are discussed in the preceding subsections.

4.2.1 Pareto-based multi-objective GA

The concept of Pareto dominance was introduced in multiobjective GAs. Fonseca and Fleming [ 56 ] developed first multiobjective GA (MOGA). The niche and decision maker concepts were proposed to tackle the multimodal problems. However, MOGA suffers from parameter tuning problem and degree of selection pressure. Horn et al. [ 80 ] proposed a niched Pareto genetic algorithm (NPGA) that utilized the concept of tournament selection and Pareto dominance. Srinivas and Deb [ 191 ] developed a non-dominated sorting genetic algorithm (NSGA). However, it suffers from lack of elitism, need of sharing parameter, and high computation complexity. To alleviate these problems, Deb et al. [ 36 ] developed a fast elitist non-dominated sorting genetic algorithm (NSGA-II). The performance of NSGA-II may be deteriorated for many objective problems. NSGA-II was unable to maintain the diversity in Pareto-front. To alleviate this problem, Luo et al. [ 130 ] introduced a dynamic crowding distance in NSGA-II. Coello and Pulido [ 28 ] developed a multiobjective micro GA. They used an archive for storing the non-dominated solutions. The performance of Pareto-based approaches may be deteriorated in many objective problems [ 52 ].

4.2.2 Decomposition-based multiobjective GA

Decomposition-based MOGAs decompose the given problem into multiple subproblems. These subproblems are solved simultaneously and exchange the solutions among neighboring subproblems [ 52 ]. Ishibuchi and Murata [ 84 ] developed a multiobjective genetic local search (MOGLS). In MOGLS, the random weights were used to select the parents and local search for their offspring. They used generation replacement and roulette wheel selection method. Jaszkiewicz [ 86 ] modified the MOGLS by utilizing different selection mechanisms for parents. Murata and Gen [ 141 ] proposed a cellular genetic algorithm for multiobjective optimization (C-MOGA) that was an extension of MOGA. They added cellular structure in MOGA. In C-MOGA, the selection operator was performed on the neighboring of each cell. C-MOGA was further extended by introducing an immigration procedure and known as CI-MOGA. Alves and Almeida [ 11 ] developed a multiobjective Tchebycheffs-based genetic algorithm (MOTGA) that ensures convergence and diversity. Tchebycheff scalar function was used to generate non-dominated solution set. Patel et al. [ 151 ] proposed a decomposition based MOGA (D-MOGA). They integrated opposition based learning in D-MOGA for weight vector generation. D-MOGA is able to maintain the balance between diversity of solutions and exploration of search space.

4.3 Parallel GAs

The motivation behind the parallel GAs is to improve the computational time and quality of solutions through distributed individuals. Parallel GAs are categorized into three broad categories such as master-slave parallel GAs, fine grained parallel GAs, and multi-population coarse grained parallel Gas [ 70 ]. In master-slave parallel GA, the computation of fitness functions is distributed over the several processors. In fine grained GA, parallel computers are used to solve the real-life problems. The genetic operators are bounded to their neighborhood. However, the interaction is allowed among the individuals. In coarse grained GA, the exchange of individuals among sub-populations is performed. The control parameters are also transferred during migration. The main challenges in parallel GAs are to maximize memory bandwidth and arrange threads for utilizing the power of GPUs [ 23 ]. Table 8 shows the comparative analysis of parallel GAs in terms of hardware and software. The well-known parallel GAs are studied in the preceding subsections.

4.3.1 Master slave parallel GA

The large number of processors are utilized in master-slave parallel GA (MS-PGA) as compared to other approaches. The computation of fitness functions may be increased by increasing the number of processors. Hong et al. [ 79 ] used MS-PGA for solving data mining problems. Fuzzy rules are used with parallel GA. The evaluation of fitness function was performed on slave machines. However, it suffers from high computational time. Sahingzo [ 174 ] implemented MS-PGA for UAV path finding problem. The genetic operators were executed on processors. They used multicore CPU with four cores. Selection and fitness evaluation was done on slave machines. MS-PGA was applied on traffic assignment problem in [ 127 ]. They used thirty processors to solve this problem at National University of Singapore. Yang et al. [ 213 ] developed a web-based parallel GA. They implemented the master slave version of NSGA-II in distributed environment. However, the system is complex in nature.

4.3.2 Fine grained parallel GA

In last few decades, researchers are working on migration policies of fine grained parallel GA (FG-PGA). Porta et al. [ 161 ] utilized clock-time for migration frequency, which is independent of generations. They used non-uniform structure and static configuration. The best solution was selected for migration and worst solution was replaced with migrant solution. Kurdi [ 115 ] used adaptive migration frequency. The migration procedure starts until there is no change in the obtained solutions after ten successive generations. The non-uniform and dynamic structure was used. In [ 209 ], local best solutions were synchronized and formed a global best solutions. The global best solutions were transferred to all processors for father execution. The migration frequency depends upon the number of generation. They used uniform structure with fixed configuration. Zhang et al. [ 220 ] used parallel GA to solve the set cover problem of wireless networks. They used divide-and-conquer strategy to decompose the population into sub-populations. Thereafter, the genetic operators were applied on local solutions and Kuhn-Munkres was used to merge the local solutions.

4.3.3 Coarse grained parallel GA

Pinel et al. [ 158 ] proposed a GraphCell. The population was initialized with random values and one solution was initialized with Min-min heuristic technique. 448 processors were used to implement the proposed approach. However, coarse grained parallel GAs are less used due to complex in nature. The hybrid parallel GAs are widely used in various applications. Shayeghi et al. [ 182 ] proposed a pool-based Birmingham cluster GA. Master node was responsible for managing global population. Slave node selected the solutions from global population and executed it. 240 processors are used for computation. Roberge et al. [ 170 ] used hybrid approach to optimize switching angle of inverters. They used four different strategies for fitness function computation. Nowadays, GPU, cloud, and grid are most popular hardware for parallel GAs [ 198 ].

4.4 Chaotic GAs

The main drawback of GAs is premature convergence. The chaotic systems are incorporated into GAs to alleviate this problem. The diversity of chaos genetic algorithm removes premature convergence. Crossover and mutation operators can be replaced with chaotic maps. Tiong et al. [ 197 ] integrated the chaotic maps into GA for further improvement in accuracy. They used six different chaotic maps. The performance of Logistic, Henon and Ikeda chaotic GA performed better than the classical GA. However, these techniques suffer from high computational complexity. Ebrahimzadeh and Jampour [ 48 ] used Lorenz chaotic for genetic operators of GA to eliminate the local optima problem. However, the proposed approach was unable to find relationship between entropy and chaotic map. Javidi and Hosseinpourfard [ 87 ] utilized two chaotic maps namely logistic map and tent map for generating chaotic values instead of random selection of initial population. The proposed chaotic GA performs better than the GA. However, this method suffers from high computational complexity. Fuertes et al. [ 60 ] integrated the entropy into chaotic GA. The control parameters are modified through chaotic maps. They investigated the relationship between entropy and performance optimization.

Chaotic systems have also used in multiobjective and hybrid GAs. Abo-Elnaga and Nasr [ 5 ] integrated chaotic system into modified GA for solving Bi-level programming problems. Chaotic helps the proposed algorithm to alleviate local optima and enhance the convergence. Tahir et al. [ 193 ] presented a binary chaotic GA for feature selection in healthcare. The chaotic maps were used to initialize the population and modified reproduction operators were applied on population. Xu et al. [ 115 ] proposed a chaotic hybrid immune GA for spectrum allocation. The proposed approach utilizes the advantages of both chaotic and immune operator. However, this method suffers from parameter initialization problem.

4.5 Hybrid GAs

Genetic Algorithms can be easily hybridized with other optimization methods for improving their performance such as image denoising methods, chemical reaction optimization, and many more. The main advantages of hybridized GA with other methods are better solution quality, better efficiency, guarantee of feasible solutions, and optimized control parameters [ 51 ]. It is observed from literature that the sampling capability of GAs is greatly affected from population size. To resolve this problem, local search algorithms such as memetic algorithm, Baldwinian, Lamarckian, and local search have been integrated with GAs. This integration provides proper balance between intensification and diversification. Another problem in GA is parameter setting. Finding appropriate control parameters is a tedious task. The other metaheuristic techniques can be used with GA to resolve this problem. Hybrid GAs have been used to solve the issues mentioned in the preceding subsections [ 29 , 137 , 186 ].

4.5.1 Enhance search capability

GAs have been integrated with local search algorithms to reduce the genetic drift. The explicit refinement operator was introduced in local search for producing better solutions. El-Mihoub et al. [ 54 ] established the effect of probability of local search on the population size of GA. Espinoza et al. [ 50 ] investigated the effect of local search for reducing the population size of GA. Different search algorithms have been integrated with GAs for solving real-life applications.

4.5.2 Generate feasible solutions

In complex and high-dimensional problems, the genetic operators of GA generate infeasible solutions. PMX crossover generates the infeasible solutions for order-based problems. The distance preserving crossover operator was developed to generate feasible solutions for travelling salesman problem [ 58 ]. The gene pooling operator instead of crossover was used to generate feasible solution for data clustering [ 19 ]. Konak and Smith [ 108 ] integrated a cut-saturation algorithm with GA for designing the communication networks. They used uniform crossover to produce feasible solutions.

4.5.3 Replacement of genetic operators

There is a possibility to replace the genetic operators which are mentioned in Section 3.2 with other search techniques. Leng [ 122 ] developed a guided GA that utilizes the penalties from guided local search. These penalties were used in fitness function to improve the performance of GA. Headar and Fukushima [ 74 ] used simplex crossover instead of standard crossover. The standard mutation operator was replaced with simulated annealing in [ 195 ]. The basic concepts of quantum computing are used to improve the performance of GAs. The heuristic crossover and hill-climbing operators can be integrated into GA for solving three-matching problem.

4.5.4 Optimize control parameters

The control parameters of GA play a crucial role in maintaining the balance between intensification and diversification. Fuzzy logic has an ability to estimate the appropriate control parameters of GA [ 167 ]. Beside this, GA can be used to optimize the control parameters of other techniques. GAs have been used to optimize the learning rate, weights, and topology of neutral networks [ 21 ]. GAs can be used to estimate the optimal value of fuzzy membership in controller. It was also used to optimize the control parameters of ACO, PSO, and other metaheuristic techniques [ 156 ]. The comparative analysis of well-known GAs are mentioned in Table 9 .

5 Applications

Genetic Algorithms have been applied in various NP-hard problems with high accuracy rates. There are a few application areas in which GAs have been successfully applied.

5.1 Operation management

GA is an efficient metaheuristic for solving operation management (OM) problems such as facility layout problem (FLP), supply network design, scheduling, forecasting, and inventory control.

5.1.1 Facility layout

Datta et al. [ 32 ] utilized GA for solving single row facility layout problem (SRFLP). For SRFLP, the modified crossover and mutation operators of GA produce valid solutions. They applied GA to large sized problems that consists of 60–80 instances. However, it suffers from parameter dependency problem. Sadrzadeh [ 173 ] proposed GA for multi-line FLP have multi products. The facilities were clustered using mutation and heuristic operators. The total cost obtained from the proposed GA was decreased by 7.2% as compared to the other algorithms. Wu et al. [ 211 ] implemented hierarchical GA to find out the layout of cellular manufacturing system. However, the performance of GA is greatly affected from the genetic operators. Aiello et al. [ 7 ] proposed MOGA for FLP. They used MOGA on the layout of twenty different departments. Palomo-Romero et al. [ 148 ] proposed an island model GA to solve the FLP. The proposed technique maintains the population diversity and generates better solutions than the existing techniques. However, this technique suffers from improper migration strategy that can be utilized for improving the population. GA and its variants has been successfully applied on FLP [ 103 , 119 , 133 , 201 ].

5.1.2 Scheduling

GA shows the superior performance for solving the scheduling problems such as job-shop scheduling (JSS), integrated process planning and scheduling (IPPS), etc. [ 119 ]. To improve the performance in the above-mentioned areas of scheduling, researchers developed various genetic representation [ 12 , 159 , 215 ], genetic operators, and hybridized GA with other methods [ 2 , 67 , 147 , 219 ].

5.1.3 Inventory control

Besides the scheduling, inventory control plays an important role in OM. Backordering and lost sales are two main approaches for inventory control [ 119 ]. Hiassat et al. [ 76 ] utilized the location-inventory model to find out the number and location of warehouses. Various design constraints have been added in the objective functions of GA and its variants for solving inventory control problem [].

5.1.4 Forecasting and network design

Forecasting is an important component for OM. Researchers are working on forecasting of financial trading, logistics demand, and tourist arrivals. GA has been hybridized with support vector regression, fuzzy set, and neural network (NN) to improve their forecasting capability [ 22 , 78 , 89 , 178 , 214 ]. Supply network design greatly affect the operations planning and scheduling. Most of the research articles are focused on capacity constraints of facilities [ 45 , 184 ]. Multi-product multi-period problems increases the complexity of supply networks. To resolve the above-mentioned problem, GA has been hybridized with other techniques [ 6 , 45 , 55 , 188 , 189 ]. Multi-objective GAs are also used to optimize the cost, profit, carbon emissions, etc. [ 184 , 189 ].

5.2 Multimedia

GAs have been applied in various fields of multimedia. Some of well-known multimedia fields are encryption, image processing, video processing, medical imaging, and gaming.

5.2.1 Information security

Due to development in multimedia applications, images, videos and audios are transferred from one place to another over Internet. It has been found in literature that the images are more error prone during the transmission. Therefore, image protection techniques such as encryption, watermarking and cryptography are required. The classical image encryption techniques require the input parameters for encryption. The wrong selection of input parameters will generate inadequate encryption results. GA and its variants have been used to select the appropriate control parameters. Kaur and Kumar [ 96 ] developed a multi-objective genetic algorithm to optimize the control parameters of chaotic map. The secret key was generated using beta chaotic map. The generated key was use to encrypt the image. Parallel GAs were also used to encrypt the image [ 97 ].

5.2.2 Image processing

The main image processing tasks are preprocessing, segmentation, object detection, denoising, and recognition. Image segmentation is an important step to solve the image processing problems. Decomposing/partitioning an image requires high computational time. To resolve this problem, GA is used due to their better search capability [ 26 , 102 ]. Enhancement is a technique to improve the quality and contrast of an image. The better image quality is required to analyze the given image. GAs have been used to enhance natural contrast and magnify image [ 40 , 64 , 99 ]. Some researchers are working on hybridization of rough set with adaptive genetic algorithm to merge the noise and color attributes. GAs have been used to remove the noise from the given image. GA can be hybridized with fuzzy logic to denoise the noisy image. GA based restoration technique can be used to remove haze, fog and smog from the given image [ 8 , 110 , 146 , 200 ]. Object detection and recognition is a challenging issue in real-world problem. Gaussian mixture model provides better performance during detection and recognition process. The control parameters are optimized through GA [ 93 ].

5.2.3 Video processing

Video segmentation has been widely used in pattern recognition, and computer vision. There are some critical issues that are associated with video segmentation. These are distinguishing object from the background and determine accurate boundaries. GA can be used to resolve these issues [ 9 , 105 ]. GAs have been implemented for gesture recognition successfully by Chao el al. [ 81 ] used GA for gesture recognition. They applied GAs and found an accuracy of 95% in robot vision. Kaluri and Reddy [ 91 ] proposed an adaptive genetic algorithm based method along with fuzzy classifiers for sign gesture recognition. They reported an improved recognition rate of 85% as compared to the existing method that provides 79% accuracy. Beside the gesture recognition, face recognition play an important role in criminal identification, unmanned vehicles, surveillance, and robots. GA is able to tackle the occlusion, orientations, expressions, pose, and lighting condition [ 69 , 95 , 109 ].

5.2.4 Medical imaging

Genetic algorithms have been applied in medical imaging such as edge detection in MRI and pulmonary nodules detection in CT scan images [ 100 , 179 ]. In [ 120 ], authors used a template matching technique with GA for detecting nodules in CT images. Kavitha and Chellamuthu [ 179 ] used GA based region growing method for detecting the brain tumor. GAs have been applied on medical prediction problems captured from pathological subjects. Sari and Tuna [ 176 ] used GA used to solve issues arises in biomechanics. It is used to predict pathologies during examination. Ghosh and Bhattachrya [ 62 ] implemented sequential GA with cellular automata for modelling the coronavirus disease 19 (COVID-19) data. GAs can be applied in parallel mode to find rules in biological datasets [ 31 ]. The authors proposed a parallel GA that runs by dividing the process into small sub-generations and evaluating the fitness of each individual solution in parallel. Genetic algorithms are used in medicine and other related fields. Koh et al. [ 61 ] proposed a genetic algorithm based method for evaluation of adverse effects of a given drug.

5.2.5 Precision agriculture

GAs have been applied on various problems that are related to precision agriculture. The main issues are crop yield, weed detection, and improvement in farming equipment. Pachepsky and Acock [ 145 ] implemented GA to analyze the water capacity in soil using remote sensing images. The crop yield can be predicted through the capacity of water present in soil. The weed identification was done through GA in [ 142 ]. They used aerial image for classification of plants. In [ 124 ], color image segmentation was used to discriminate the weed and plant. Peerlink et al. [ 154 ] determined the appropriate rate of fertilizer for various portions of agriculture field. They GA for determining the nitrogen in wheat field. The energy requirements in water irrigation systems can be optimized by viewing it as a multi-objective optimization problem. The amount of irrigation required and thus power requirements change continuously in a SMART farm. Therefore, GA can be applied in irrigation systems to reduce the power requirements [ 33 ].

5.2.6 Gaming

GAs have been successfully used in games such as gomoku. In [ 202 ], the authors shown that the GA based approach finds the solution having the highest fitness than the normal tree based methods. However, in real-time strategy based games, GA based solutions become less practical to implement [ 82 ]. GAs have been implemented for path planning problems considering the environment constraints as well as avoiding the obstacles to reach the given destination. Burchardt and Salomon [ 18 ] described an implementation for path planning for soccer games. GA can encode the path planning problems via the coordinate points of a two-dimensional playing field, hence resulting in a variable length solution. The fitness function in path planning considers length of path as well as the collision avoiding terms for soccer players.

5.3 Wireless networking

Due to adaptive, scalable, and easy implementation of GA, it has been used to solve the various issues of wireless networking. The main issues of wireless networking are routing, quality of service, load balancing, localization, bandwidth allocation and channel assignment [ 128 , 134 ]. GA has been hybridized with other metaheuristics for solving the routing problems. Hybrid GA not only producing the efficient routes among pair of nodes, but also used for load balancing [ 24 , 212 ].

5.3.1 Load balancing

Nowadays, multimedia applications require Quality-of-Service (QoS) demand for delay and bandwidth. Various researchers are working on GAs for QoS based solutions.GA produces optimal solutions for complex networks [ 49 ]. Roy et al. [ 172 ] proposed a multi-objective GA for multicast QoS routing problem. GA was used with ACO and other search algorithms for finding optimal routes with desired QoS metrics. Load balancing is another issue in wireless networks. Scully and Brown [ 177 ] used MicroGAs and MacroGAs to distribute the load among various components of networks. He et al. [ 73 ] implemented GA to determine the balance load in wireless sensor networks. Cheng et al. [ 25 ] utilized distributed GA with multi-population scheme for load balancing. They used load balancing metric as a fitness function in GA.

5.3.2 Localization

The process of determining the location of wireless nodes is called as localization. It plays an important role in disaster management and military services. Yun et al. [ 216 ] used GA with fuzzy logic to find out the weights, which are assigned according to the signal strength. Zhang et al. [ 218 ] hybridized GA with simulated annealing (SA) to determine the position of wireless nodes. SA is used as local search to eliminate the premature convergence.

5.3.3 Bandwidth and channel allocation

The appropriate bandwidth allocation is a complex task. GAs and its variants have been developed to solve the bandwidth allocation problem [ 92 , 94 , 107 ]. GAs were used to investigate the allocation of bandwidth with QoS constraints. The fitness function of GAs may consists of resource utilization, bandwidth distribution, and computation time [ 168 ]. The channel allocation is an important issue in wireless networks. The main objective of channel allocation is to simultaneously optimize the number of channels and reuse of allocated frequency. Friend et al. [ 59 ] used distributed island GA to resolve the channel allocation problem in cognitive radio networks. Zhenhua et al. [ 221 ] implemented a modified immune GA for channel assignment. They used different encoding scheme and immune operators. Pinagapany and Kulkarni [ 157 ] developed a parallel GA to solve both static and dynamic channel allocation problem. They used decimal encoding scheme. Table 10 summarizes the applications of GA and its variants.

6 Challenges and future possibilities

In this section, the main challenges faced during the implementation of GAs are discussed followed by the possible research directions.

6.1 Challenges

Despite the several advantages, there are some challenges that need to be resolved for future advancements and further evolution of genetic algorithms. Some major challenges are given below:

6.1.1 Selection of initial population

Initial population is always considered as an important factor for the performance of genetic algorithms. The size of population also affects the quality of solution [ 160 ]. The researchers argue that if a large population is considered, then the algorithm takes more computation time. However, the small population may lead to poor solution [ 155 ]. Therefore, finding the appropriate population size is always a challenging issue. Harik and Lobo [ 71 ] investigated the population using self-adaption method. They used two approaches such as (1) use of self-adaption prior to execution of algorithm, in which the size of population remains the same and (2) in which the self-adaption used during the algorithm execution where the population size is affected by fitness function.

6.1.2 Premature convergence

Premature convergence is a common issue for GA. It can lead to the loss of alleles that makes it difficult to identify a gene [ 15 ]. Premature convergence states that the result will be suboptimal if the optimization problem coincides too early. To avoid this issue, some researchers suggested that the diversity should be used. The selection pressure should be used to increase the diversity. Selection pressure is a degree which favors the better individuals in the initial population of GA’s. If selection pressure (SP1) is greater than some selection pressure (SP2), then population using SP1 should be larger than the population using SP2. The higher selection pressure can decrease the population diversity that may lead to premature convergence [ 71 ].

Convergence property has to be handled properly so that the algorithm finds global optimal solution instead of local optimal solution (see Fig. 8 ). If the optimal solution lies in the vicinity of an infeasible solution, then the global nature of GA can be combined with local nature of other algorithms such as Tabu search and local search. The global nature of genetic algorithms and local nature of Tabu search provide the proper balance between intensification and diversification.

Local and global optima [ 149 ]

6.1.3 Selection of efficient fitness functions

Fitness function is the driving force, which plays an important role in selecting the fittest individual in every iteration of an algorithm. If the number of iterations are small, then a costly fitness function can be adjusted. The number of iterations increases may increase the computational cost. The selection of fitness function depends upon the computational cost as well as their suitability. In [ 46 ], the authors used Davies-Bouldin index for classification of documents.

6.1.4 Degree of mutation and crossover

Crossover and mutation operators are the integral part of GAs. If the mutation is not considered during evolution, then there will be no new information available for evolution. If crossover is not considered during evolution, then the algorithm can result in local optima. The degree of these operators greatly affect the performance of GAs [ 72 ]. The proper balance between these operators are required to ensure the global optima. The probabilistic nature cannot determine the exact degree for an effective and optimal solution.

6.1.5 Selection of encoding schemes

GAs require a particular encoding scheme for a specific problem. There is no general methodology for deciding whether the particular encoding scheme is suitable for any type of real-life problem. If there are two different problems, then two different encoding schemes are required. Ronald [ 171 ] suggested that the encoding schemes should be designed to overwhelm the redundant forms. The genetic operators should be implemented in a manner that they are not biased towards the redundant forms.

6.2 Future research directions

GAs have been applied in different fields by modifying the basic structure of GA. The optimality of a solution obtained from GA can be made better by overcoming the current challenges. Some future possibilities for GA are as follows:

There should be some way to choose the appropriate degree of crossover and mutation operators. For example Self-Organizing GA adapt the crossover and mutation operators according to the given problem. It can save computation time that make it faster.

Future work can also be considered for reducing premature convergence problem. Some researchers are working in this direction. However, it is suggested that new methods of crossover and mutation techniques are required to tackle the premature convergence problem.

Genetic algorithms mimic the natural evolution process. There can be a possible scope for simulating the natural evolution process such as the responses of human immune system and the mutations in viruses.

In real-life problems, the mapping from genotype to phenotype is complex. In this situation, the problem has no obvious building blocks or building blocks are not adjacent groups of genes. Hence, there is a possibility to develop novel encoding schemes to different problems that does not exhibit same degree of difficulty.

7 Conclusions

This paper presents the structured and explained view of genetic algorithms. GA and its variants have been discussed with application. Application specific genetic operators are discussed. Some genetic operators are designed for representation. However, they are not applicable to research domains. The role of genetic operators such as crossover, mutation, and selection in alleviating the premature convergence is studied extensively. The applicability of GA and its variants in various research domain has been discussed. Multimedia and wireless network applications were the main attention of this paper. The challenges and issues mentioned in this paper will help the practitioners to carry out their research. There are many advantages of using GAs in other research domains and metaheuristic algorithms.

The intention of this paper is not only provide the source of recent research in GAs, but also provide the information about each component of GA. It will encourage the researchers to understand the fundamentals of GA and use the knowledge in their research problems.

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Katoch, S., Chauhan, S.S. & Kumar, V. A review on genetic algorithm: past, present, and future. Multimed Tools Appl 80 , 8091–8126 (2021). https://doi.org/10.1007/s11042-020-10139-6

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Principles of Genetic Engineering

Thomas m. lanigan.

1 Biomedical Research Core Facilities, Vector Core, University of Michigan, Ann Arbor, MI 48109, USA; ude.hcimu@tnaginal (T.M.L.); ude.hcimu@hgnohc (H.C.K.)

2 Department of Internal Medicine, Division of Rheumatology, University of Michigan, Ann Arbor, MI 48109, USA

Huira C. Kopera

3 Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA

Thomas L. Saunders

4 Biomedical Research Core Facilities, Transgenic Animal Model Core, University of Michigan, Ann Arbor, MI 48109, USA

5 Department of Internal Medicine, Division of Genetic Medicine, University of Michigan, Ann Arbor, MI 48109, USA

Genetic engineering is the use of molecular biology technology to modify DNA sequence(s) in genomes, using a variety of approaches. For example, homologous recombination can be used to target specific sequences in mouse embryonic stem (ES) cell genomes or other cultured cells, but it is cumbersome, poorly efficient, and relies on drug positive/negative selection in cell culture for success. Other routinely applied methods include random integration of DNA after direct transfection (microinjection), transposon-mediated DNA insertion, or DNA insertion mediated by viral vectors for the production of transgenic mice and rats. Random integration of DNA occurs more frequently than homologous recombination, but has numerous drawbacks, despite its efficiency. The most elegant and effective method is technology based on guided endonucleases, because these can target specific DNA sequences. Since the advent of clustered regularly interspaced short palindromic repeats or CRISPR/Cas9 technology, endonuclease-mediated gene targeting has become the most widely applied method to engineer genomes, supplanting the use of zinc finger nucleases, transcription activator-like effector nucleases, and meganucleases. Future improvements in CRISPR/Cas9 gene editing may be achieved by increasing the efficiency of homology-directed repair. Here, we describe principles of genetic engineering and detail: (1) how common elements of current technologies include the need for a chromosome break to occur, (2) the use of specific and sensitive genotyping assays to detect altered genomes, and (3) delivery modalities that impact characterization of gene modifications. In summary, while some principles of genetic engineering remain steadfast, others change as technologies are ever-evolving and continue to revolutionize research in many fields.

1. Introduction

Since the identification of DNA as the unit of heredity and the basis for the central dogma of molecular biology [ 1 ] that DNA makes RNA and RNA makes proteins, scientists have pursued experiments and methods to understand how DNA controls heredity. With the discovery of molecular biology tools such as restriction enzymes, DNA sequencing, and DNA cloning, scientists quickly turned to experiments to change chromosomal DNA in cells and animals. In that regard, initial experiments that involved the co-incubation of viral DNA with cultured cell lines progressed to the use of selectable markers in plasmids. Delivery methods for random DNA integration have progressed from transfection by physical co-incubation of DNA with cultured cells, to electroporation and microinjection of cultured cells [ 2 , 3 , 4 ]. Moreover, the use of viruses to deliver DNA to cultured cells has progressed in tandem with physical methods of supplying DNA to cells [ 5 , 6 , 7 ]. Homologous recombination in animal cells [ 8 ] was rapidly exploited by the mouse genetics research community for the production of gene-modified mouse ES cells, and thus gene-modified whole animals [ 9 , 10 ].

This impetus to understand gene function in intact animals was ultimately manifested in the international knockout mouse project, the purpose of which was to knock out every gene in the mouse genome, such that researchers could choose to make knockout mouse models from a library of gene-targeted knockout ES cells [ 11 , 12 , 13 ]. Thousands of mouse models have resulted from that effort and have been used to better understand gene function and the bases of human genetic diseases [ 14 ]. This project required high-throughput pipelines for the construction of vectors, including bacterial artificial chromosome (BAC) recombineering technology [ 13 , 15 , 16 , 17 ]. BACs contain long segments of cloned genomic DNA. For example, the C57BL/6J mouse BAC library, RPCI-23, has an average insert size of 197 kb of genomic DNA per clone [ 18 ]. Because of their size, BACs often carry all of the genetic regulatory elements to faithfully recapitulate the expression of genes contained in them, and thus can be used to generate BAC transgenic mice [ 19 , 20 ]. Recombineering can be used to insert reporters in BACs that are then used to generate transgenic mice to accurately label cells and tissues according to the genes in the BACs [ 21 , 22 , 23 , 24 , 25 , 26 ]. A panoply of approaches to genetic engineering are available for researchers to manipulate the genome. ES cell and BAC transgene engineering have given way to directly editing genes in zygotes, consequently avoiding the need for ES cell or BAC intermediates on the way to an animal model.

Prior to the adaptation of Streptococcus pyogenes Cas9 protein to cause chromosome breaks, three other endonuclease systems were used: (1) rare-cutting meganucleases, (2) zinc finger nucleases (ZFNs), and (3) transcription activator-like effector (TALE) nucleases (TALENs) [ 27 ]. The I-CreI meganuclease recognizes a 22 bp DNA sequence [ 28 , 29 ]. Proof-of-concept experiments demonstrated that the engineered homing endonuclease I-CreI can be used to generate transgenic mice and transgenic rats [ 30 ]. I-CreI specificity can be adjusted to target specific sequences in DNA by protein engineering methodology, although this limits its widespread application to genetic engineering [ 31 ]. Subsequently, ZFN technology was developed to cause chromosome breaks [ 32 ]. A single zinc finger is made up of 30 amino acids that bind three base pairs. Thus, three zinc fingers can be combined to specifically recognize nine base pairs on one DNA strand and a triplet of zinc fingers is made to bind nine base pairs on the opposite strand. Each zinc finger is fused to the DNA-cutting domain of the FokI restriction endonuclease. Because FokI domains only cut DNA when they are present as dimers, a ZFN monomer binding to a chromosome cannot induce a DNA break [ 32 ], instead requiring ZFN heterodimers for sequence-specific chromosome breaks. It is estimated that 1 in every 500 genomic base pairs can be cleaved by ZNFs [ 33 ]. Compared with meganucleases, ZFNs are easier to construct because of publicly available resources [ 34 ]. Additionally, the value of ZFNs in mouse and rat genome engineering was demonstrated in several studies that produced knockout, knockin, and floxed (described below) animal models [ 35 , 36 , 37 ]. The development of transcription activator-like effector nucleases (TALENs) followed after ZFN technology [ 38 ]. TALENs are made up of tandem repeats of 34 amino acids. The central amino acids at positions 12 and 13, named repeat variable di-residues (NVDs), determine the base to which the repeat will bind [ 38 ]. To achieve a specific chromosomal break, 15 TALE repeats assembled and fused to the FokI endonuclease domain (TALEN monomer) are required. Thus, one TALEN monomer binds to 15 base pairs on one DNA strand, and a second TALEN monomer binds to bases on the opposite strand [ 38 ]. When the FokI endonuclease domains are brought together, a double-stranded DNA break occurs. In this way, a TALEN heterodimer can be used to cause a sequence-specific chromosome break. It has been estimated that, within the entire genome, TALENs have potential target cleavage sites every 35 bp [ 39 ]. Compared with ZFNs, TALENs are easier to construct with publicly available resources [ 40 , 41 ], and TALENs have been adopted for use in mouse and rat genome engineering in several laboratories that have produced knockout and knockin animal models [ 42 , 43 , 44 , 45 , 46 ].

The efficiencies of producing specific double-strand chromosome breaks, using prior technologies such as meganucleases, ZFNs, and TALENs [ 28 , 32 , 38 ], were surpassed when CRISPR/Cas9 technology was shown to be effective in mammalian cells [ 47 , 48 , 49 ]. The essential feature that all of these technologies have in common is the production of a chromosome break at a specific location to facilitate genetic modifications [ 50 ]. In particular, the discovery of bacterial CRISPR-mediated adaptive immunity, and its application to genetic modification of human and mouse cells in 2013 [ 47 , 48 , 49 ], was a watershed event to modern science. Moreover, the introduction of CRISPR/Cas9 methodology has revolutionized transgenic mouse generation. This paradigm shift can be seen by changes in demand for nucleic acid microinjections into zygotes, and ES cell microinjections into blastocysts at the University of Michigan Transgenic Core ( Figure 1 ). While previously established principles of genetic engineering using mouse ES cell technology [ 51 , 52 , 53 ] remain applicable, CRISPR/Cas9 methodologies have made it much easier to produce genetically engineered model organisms in mice, rats, and other species [ 54 , 55 ]. Herein, we discuss principles in genetic engineering for the design and characterization of targeted alleles in mouse and rat zygotes, or in cultured cell lines, for the production of animal and cell culture models for biomedical research.

Recent trends in nucleic acid microinjection in zygotes, and embryonic stem (ES) cell microinjections into blastocysts, for the production of genetically engineered mice at the University of Michigan Transgenic Core. As shown, prior to the introduction of CRISPR/Cas9, the majority of injections were of ES cells, to produce gene-targeted mice, and DNA transgenes, to produce transgenic mice. After CRISPR/Cas9 became available, adoption was slow until 2014, when it was enthusiastically embraced, and the new technology corresponded to a reduced demand for ES cell and DNA microinjections.

2. Principles of Genetic Engineering

2.1. types of genetic modifications.

There are many types of genetic modifications that can be made to the genome. The ability to specifically target locations in the genome has expanded our ability to make changes that include knockouts (DNA sequence deletions), knockins (DNA sequence insertions), and replacements (replacement of DNA sequences with exogenous sequences). Deletions in the genome can be used to knockout gene expression [ 56 , 57 ]. Short deletions in the genome can be used to remove regulatory elements that knockout gene expression [ 58 ], activate gene expression [ 59 ], or change protein structure/function by changing coding sequences [ 60 ].

Insertion of new genomic information can be used to knock in a variety of genetic elements. Knockins are also powerful approaches for modifying genes. Just as genomic deletions can be used to change gene function, knockins can be used to block gene function by inserting fluorescent reporter genes such as eGFP or mCherry, in such a way as to knock out the gene at the insertion point [ 61 , 62 ]. It is also possible to knock in fluorescent protein reporter genes, without knocking out the targeted gene [ 63 , 64 ]. Just as fluorescent proteins can be used to label proteins and cells, short knockins of epitope tags in proteins can be used to label proteins for detection with antibodies [ 64 , 65 ].

Replacement of DNA sequences in the genome can be used to achieve two purposes at the same time, such as blocking gene function, while activating the function of a new gene such as the lacZ reporter [ 66 ]. Large-scale sequence replacements are possible with mouse ES cell technology, such as the replacement of the mouse immunoglobulin locus with the human immunoglobulin locus to produce a “humanized” mouse [ 67 ]. Furthermore, very small replacements of single nucleotides can be used to model point mutations that are suspected of causing human disease [ 68 , 69 , 70 ].

A special type of DNA sequence replacement is the conditional allele. Conditional alleles permit normal gene expression until the site-specific Cre recombinase removes a loxP-flanked critical exon to produce a “floxed” (flanked by loxP) exon. Cre recombinase recognizes 34 bp loxP (locus of recombination) elements, and catalyzes recombination between the two loxP sites [ 71 , 72 ]. Therefore, deletion of the critical exon causes a premature termination codon to occur in the mRNA transcript, triggering its nonsense-mediated decay and failure to make a protein [ 13 , 73 ]. Engineering conditional alleles was the approach used by the international knockout mouse project [ 13 ]. Mice with cell- and tissue-specific Cre recombinase expression are an important resource for the research community [ 74 ].

Other site-specific recombinases, such as FLP, Dre, and Vika, that work on the same principle have also been applied to mouse models [ 75 , 76 , 77 , 78 , 79 , 80 ]. Recombinase knockins can be designed to knock out the endogenous gene or preserve its function [ 81 , 82 ]. A variation in the conditional allele is the inducible allele, which is silent until its expression is activated by Cre recombinase [ 79 ]. For example, reporter models can activate the expression of a fluorescent protein [ 83 ], change fluorescent reporter protein colors from red to green [ 84 ], or use a combinatorial approach to produce up to 90 fluorescent colors [ 85 ]. Another type of inducible allele is the FLEX allele. FLEX genes are Cre-dependent gene switches based on the use of heterotypic loxP sites [ 86 ]. In one application that combined Cre and FLP recombinases, it was demonstrated that a gene inactivated in ES cells by a gene trap could be switched back on and then switched off again [ 87 ]. In another application of heterotypic loxP sites in mouse ES cells, it was demonstrated that genes could be made conditional by inversion (COIN) [ 88 ]. This application has been used to produce mice with conditional genes for point mutations [ 89 ] and has been applied to produce conditional single exon genes that lack critical exons by definition [ 90 ].

2.2. Genetic Engineering with CRISPR/Cas9

The central principle of gene targeting with CRISPR/Cas9, or other directed DNA endonucleases, is that a double-strand DNA break is generated in the cell of interest. Following a chromosomal break, the principal outcomes of interest are nonhomologous end joining (NHEJ) repair [ 91 ] or homology-directed repair (HDR) [ 92 ]. When the break is directed to a coding exon in a gene, the outcome of NHEJ is usually a small insertion or deletion of DNA sequence at the break (indel), causing frame shifts in mRNA transcripts that lead to premature termination codons, causing nonsense-mediated mRNA decay and loss of protein expression [ 73 ]. The HDR pathway copies a template during DNA repair, and thus the insertion of modified genetic sequences in the form of a DNA donor. This DNA donor can introduce new information into the genome flanked by homology arms on either side of the chromosome break. Typical applications of HDR include the use of genetic engineering to abrogate gene expression (gene knockouts), to modify amino acid codons (i.e.; point mutations), to replace genes with new genes (e.g.; knockins of fluorescent reporters, Cre recombinase, cDNA coding sequences), to produce conditional genes (floxed genes that are normally expressed until they are inactivated by Cre recombinase), to produce Cre-inducible genes (genes that are only expressed after Cre recombinase activates them), and to delete DNA from chromosomes (e.g.; delete regulatory elements that control gene expression, delete entire genes, or delete up to a megabase of chromosome segments). The simplest of these modifications is abrogation of gene expression. Multifunctional alleles, such as FLEX alleles, require the cloning or synthesis of multi-element plasmid DNA donors for HDR.

The processes of CRISPR/Cas9-mediated modifications of genes (gene editing) to produce a new cell line or animal model have in common a series of steps to achieve the final product. First, a gene of interest is identified and the final desired allele is specified. The next step is to identify single guide RNA(s) (gRNAs) that will be used to target a chromosomal break in one or more places. There are numerous online websites that can be used for this purpose [ 93 ]. One of the most up-to-date and versatile sites is CRISPOR ( http://crispor.tefor.net ) [ 94 ]. Interestingly, the authors provide evidence that the predictive powers of algorithms vary depending on whether they were based on the analysis of gRNAs delivered as RNA molecules, versus gRNAs delivered as U6-transcribed DNA molecules [ 94 ]. In any event, the selection of a gRNA target (20 nucleotides), adjacent to a protospacer-adjacent motif (PAM; NGG motif), should not be done without the aid of a computer algorithm that minimizes the possibility of off-target hits. After a gRNA target is identified, a decision is made to obtain gRNAs. While it is possible to produce in vitro-transcribed gRNAs, this may be inadvisable in so much as in vitro-transcribed RNAs can trigger innate immune responses and cause cytotoxicity in cells [ 95 ]. Chemically synthesized gRNAs using phosphorothioate modifications that improve gRNA stability may be preferable alternatives to in vitro-transcribed molecules [ 96 , 97 ]. With a gRNA in hand, a Cas9 protein is then selected. There are numerous forms of Cas9 that can be used for different purposes [ 98 ]. For practical purposes, we limit our discussion to Cas9 varieties that are on the market. A number of commercial entities sell wild-type Cas9 protein. When wild type Cas9 is used to target the genome with nonspecific guides, the frequency of off-target genomic hits, besides the desired Cas9 target, is very likely to increase [ 94 , 99 ]. Alternatives to the wild-type protein include enhanced specificity Cas9 from Sigma-Aldrich [ 100 ], and high-fidelity Cas9 from Integrated DNA Technologies [ 101 ]. In addition, there are other versions such as HF1 Cas9 [ 102 ], hyperaccurate Cas9 [ 103 ], and evolved Cas9 [ 104 ], all available in plasmid format from Addgene.org. As may be inferred from the names of these engineered Cas9 versions, they are designed to be more specific than wild type Cas9. Once the gRNAs and Cas9 protein are on hand, then it is a “simple” matter to combine them and deliver them to the target cell to produce a chromosome break and achieve a gene knockout by introducing premature termination codons or DNA sequence deletion of regulatory regions or entire genes.

2.3. Locus-Specific Genetic Engineering Vectors in Mouse and Rat Zygotes

The most challenging type of genetic engineering is the insertion (i.e.; knockin) of a long coding sequence to express a fluorescent reporter protein, Cre recombinase, or conditional allele (floxed gene). In addition to these genetic modifications, numerous other types of specialized reporters can be introduced, each designed to achieve a different purpose. There is great interest in achieving rapid and efficient gene insertions of reporters in animal models with CRISPR/Cas9 technology. It is generally recognized that, the longer the insertion, the less efficient it is to produce a knockin animal. Additional challenges are allele-specific differences that affect efficiency. For example, it is fairly efficient to produce knockins into the genomic ROSA26 locus in mice, while other loci are targeted less efficiently, and thus refractory to knockins. This accessibility to CRISPR/Cas9 complexes mirrors observations in mouse ES cell gene targeting technology, in which it was reported that some genes are not as efficiently targeted as others [ 105 ].

When the purpose of the experiment is to specifically modify the DNA sequence by changing amino acid codons, or introducing new genetic information, then a DNA donor must be delivered to the cells with Cas9 reagents. After the selected gRNAs and Cas9 proteins are demonstrated to produce the desired chromosome break, the DNA donor is designed and procured. The donor should be designed to insert into the genome such that it will not be cleaved by Cas9, usually by mutating the PAM site. The DNA donor may take the form of short oligonucleotides (<200 nt) [ 106 , 107 ], long single-stranded DNA molecules (>200 nt) [ 108 ], or double-stranded linear or circular DNA molecules of varying lengths [ 109 , 110 ].

DNA donor design principles should include the following: (1) nucleotide changes that prevent CRISPR/Cas9 cleavage of the chromosome, after introduction of the DNA donor; (2) insertion of restriction enzyme sites unique to the donor, to simplify downstream genotyping; (3) insertions of reporters or coding sequences, at least 1.5 kb in length, that can be introduced as long single-stranded DNA templates with short 100 base pair arms of homology [ 111 ], or as circular double-stranded DNA plasmids with longer (1.5 or 2 kb) arms of homology [ 63 , 110 ]; and (4) insertions of longer coding sequences, such as Cas9, that use circular double-stranded DNA donors with longer arms of homology [ 63 , 112 ]. It is also possible to use linear DNA fragments as donors [ 63 , 110 , 113 ], although random integration of linear DNA molecules is much higher than those of circular donors, thus requiring careful quality control.

The establishment of genetically modified mouse and rat models can be divided into three phases, after potential founder animals are born from CRISPR/Cas9-treated zygotes. In the first phase, animals with genetic modifications are identified. The first phase requires a sensitive and specific genotyping assay to identify cells or animals harboring the desired knockin. Genotyping potential founder mice for knockins typically begins with a PCR assay using a primer that recognizes the exogenous DNA sequence and a primer in genomic DNA outside of the homology arm in the targeting vector. Accordingly, PCR assays are designed to specifically detect the upstream and downstream junctions of the inserted DNA in genomic DNA. Subsequent assays may be used to confirm that the entire exogenous sequence is intact. Conditional genes represent a special case of insertion, as PCR assays designed to detect correct insertion of loxP-flanked exons will also detect genomic DNA [ 108 ]. In the second phase, founders are mated and G1 pups are identified that inherited the desired mutation [ 114 ]. In the third phase, it is essential to sequence additional genomic regions upstream and downstream of the inserted targeting vector DNA, because Cas9 is very efficient at inducing chromosomal breaks, but has no repair function. Thus, it is not unusual to identify deletions/insertions that flank the immediate vicinity of the Cas9 cut site or inserted targeting vector DNA sequences [ 115 , 116 ]. If such deletions affect nearby exons, gene expression can be disrupted, and confounding phenotypes may arise.

For gene knockouts, PCR amplicons from primers that span the chromosome break site are analyzed by DNA sequencing. Any animals that are wild-type at the allele are not further characterized or used, so as to prevent any off-target hits from entering the animal colony or confounding phenotypes. Animals that show disrupted DNA sequences at the Cas9 cut site are mated with wild-type animals for the transmission of mutant alleles that produce premature termination codons, for gene knockout models [ 57 , 73 ]. As founders from Cas9-treated zygotes are genetic mosaics [ 55 , 115 ], it is essential to mate them to wild-type breeding partners, such that obligate heterozygotes are produced. In the heterozygotes, the wild-type sequence and the mutant sequence can be precisely identified by techniques such as TOPO TA cloning (Invitrogen, CA, USA) or next-generation sequencing (NGS) methods [ 117 , 118 , 119 , 120 ]. Animals carrying a defined indel, with the desired properties, are then used to establish lines for phenotyping. The identical approach is used when short DNA sequences are deleted by two guide RNAs [ 58 ]. Intercrossing mosaic founders will produce offspring carrying two different mutations with different effects on gene expression. These animals are not suitable for line establishment.

2.4. Gene Editing in Immortalized Cell Lines

CRISPR/Cas9 gene editing in immortalized cell lines presents a set of challenges unique from those used in the generation of transgenic animals. Cell lines encompass a wide range of characteristics, resulting in each line being handled differently. Some of these characteristics include phenotype heterogeneity, aberrant chromosome ploidy, varying growth rates, DNA damage response efficiency, transfection efficiency, and clonability. While the principles of CRISPR/Cas9 experimental design, as stated above, remain the same, three major considerations must be taken into account when using cell lines: (1) copy number variation, or the number of alleles of the gene of interest; (2) transfection efficiency of the cell line; and (3) clonal isolation of the modified cell line. In cell lines, all alleles need to be modified in the generation of a null phenotype, or in the creation of a homozygous genotype. Unlike transgenic animals, where single allele gene edits can be bred to homozygosity, CRISPR/Cas9-edited cells must be screened for homozygous gene edits. Copy number variations within the cell line can decrease the efficiency and add labor and time (i.e.; editing 3 or 4 copies versus editing 1 or 2). Furthermore, an aberrant number of chromosomes, deletions, duplications, pseudogenes, and repetitive regions complicate genetic backgrounds for PCR analysis of the CRISPR edits. To help with some of these issues, one common approach is to use NGS on all the clonal isolates for a complete understanding of copy number variations for each clonal cell line generated, and the exact sequence for each allele.

As all cell types are not the same, different CRISPR/Cas9 delivery techniques may need to be tested to identify which method works best. One approach is to use viruses or transposons to deliver CRISPR/Cas9 reagents (detailed below). However, the viruses and transposons themselves will integrate into the genome, as well as allowing long-term expression of CRISPR/Cas9 in the cell. This prolonged expression of gRNAs and Cas9 protein may lead to off-target effects. Moreover, transfection and electroporation can have varying efficiencies, depending on the cell lines and the form of CRISPR/Cas9 reagents (e.g.; DNA plasmids or ribonucleoprotein particles (RNPs)).

Following delivery, clonal isolation is required to identify the edited cell line, and at times, can result in the isolation of a cell phenotype different than that expected, arising from events apart from the desired gene edit. While flow cytometry can aid in isolating individual cells, specific flow conditions, such as pressure, may require adjustment to ensure cell viability. Furthermore, one clonal isolate from a cell line may possess a different number of alleles for the targeted gene than another clonal isolate. Additionally, not all cell lines will grow from a single cell, thus complicating isolation. Growth conditions and cell viability can also change when isolating single cells.

Despite these challenges, new advances in CRISPR technology can likely alleviate some of these difficulties when editing cell lines. For example, fluorescently tagged Cas9 and RNAs help to isolate only transfected cells, which helps to eliminate time wasted on screening untransfected cells. Cas9-variants that harbor mutations that only create single-strand nicks (Cas9-nickases) complexed with two different, but proximal gRNAs can increase HDR-mediated knockin [ 48 , 121 ]. Similarly, fusing Cas9 with base-editing enzymes can also increase the efficiency of editing, without causing double-strand breaks [ 121 ].

2.5. Viruses and Transposons as Genetic Engineering Vectors

Viral and transposon vectors have been engineered to be safe, efficient delivery systems of exogenous genetic material into cells. The natural lifecycle of some viruses and transposons includes the stable integration into the host genome. In the field of genome engineering, these vectors can be used to modify the genome in a non-directed fashion, by inserting cassettes expressing any cDNA, shRNA, miRNA, or any non-coding RNA. The most widely used vectors capable of integrating ectopic genetic material into cells are retroviruses, lentiviruses, and adeno-associated virus (AAV). These viruses are flanked by terminal repeats that mark the boundaries of the integration. In engineering these viruses into recombinant vector systems, all the viral genes are removed from the flanking terminal repeats and supplied in trans for the recombinant virus to be packaged. These “gutted”, nonreplicable viral vectors allow for the packaging, delivery, integration, and expression of cDNAs of interest, shRNAs, and CRISPR/Cas9, without viral replication in various biological targets.

Similar to recombinant viruses, transposon vectors are also “gutted”, separating the transposase from the terminal repeat-flanked genetic material to be inserted into the genome. DNA transposons are mobile elements (“jumping genes”) that integrate into the host genome through a cut-and-paste mechanism [ 122 ]. Transposons, much like viral vectors, are flanked by repeats that mark the region to be transposed [ 123 ]. The enzyme transposase binds the flanking DNA repeats and mediates the excision and integration into the genome. Unlike viral vectors, transposons are not packaged into viral particles, but form a DNA-protein complex that stays in the host cell. Thus, the transgene to be integrated can be much larger than the packaging limits of some viruses.

Two transposons, Sleeping Beauty (SB) and piggybac (PB), have been engineered and optimized for high activity for generating transgenic mammalian cell lines [ 124 , 125 , 126 ]. Sleeping Beauty is a transposable element resurrected from fish genomes. The SB system has been used to generate transgenic HeLa cell lines, T-cells expressing chimeric antigen receptors that recognize tumor-specific antigens, and transgenic primary human stem cells [ 127 , 128 , 129 ]. The insect-derived PB system also has been used to generate transgenic cell lines [ 126 , 130 , 131 ]. The PB system was used to generate induced pluripotent stem cells (iPSCs) from mouse embryonic fibroblasts, by linking four or five cDNAs of the reprogramming (Yamanaka) factors [ 132 ] with intervening peptide self-cleavage (P2A) sites, thus delivering all of the factors in one vector [ 130 ]. Furthermore, once reprogrammed, the transgene may be removed by another round of PB transposase activity, leaving no genetic trace of integration or excision (i.e.; transgene-free iPSCs). Following PB transposase activity, epigenetic differences remaining at the endogenous promoters of the reprogramming factor genes result in sustained expression and pluripotency, despite transgene removal.

Aside from transgene insertion, Sleeping Beauty (SB) and piggyback (PB) have both been engineered to deliver CRISPR/Cas9 reagents into cells [ 133 , 134 , 135 ]. Similar to lentivirus, the stable integration of CRISPR/Cas9 by transposons could increase the efficacy of targeting and modifying multiple alleles. SB and PB have been used to deliver multiple gRNAs to target multiple genes (instead of just one), aiding in high-throughput screening. Furthermore, owing to the nature of PB excision stated above, the integrated CRISPR/Cas9 can be removed once a clonal cell line is established, to limit off-target effects. However, engineered transposons must be transfected into cells. As stated above, efficiencies vary between different cell lines and transfection methods. One potential solution to overcome this challenge is to merge technologies. For example, instead of transfecting cells with a plasmid harboring a gRNA flanked by SB terminal repeats (SB-CRISPR), the SB-CRISPR may be flanked by recombinant AAV (rAAV) terminal repeats (AAV-SB-CRISPR), allowing for packaging into rAAV. To that end, rAAV-SB-CRISPR has been used to infect primary murine T-cells, and deliver the SB-CRISPR construct [ 136 ].

2.6. Genetic Engineering Using Retroviruses

Retroviruses are RNA viruses that replicate through a DNA intermediate [ 137 ]. They belong to a large family of viruses including both onco-retroviruses, such as the Moloney murine leukemia virus (MMLV) (simply referred to as retrovirus), and lentiviruses, including human immunodeficiency virus (HIV). In all retroviruses, the RNA genome is flanked on both sides by long terminal repeats (LTRs); packaged with viral reverse transcriptase, integrase, and protease, surrounded by a protein capsid; and then enveloped into a lipid-based particle [ 138 ]. Envelope proteins interact with specific host cell surface receptors to mediate entry into host cells through membrane fusion. Then, the RNA genome is reverse-transcribed by the associated viral reverse transcriptase. The proviral DNA is then transported into the nucleus, along with viral integrase, resulting in integration into the host cell genome [ 139 ]. By contrast, the retroviral MMLV pre-integration complex is incapable of crossing the nuclear membrane, thus requiring the cell to undergo mitosis to gain access to chromatin [ 139 ], while lentiviral pre-integration complexes can cross nuclear membrane pores, allowing genome integration in both dividing and non-dividing cells.

Large-scale assessments of genomic material composition have uncovered features associated with retroviral insertion into mammalian genomes [ 140 ]. Although determination of integration target sites remains ill-defined, it does depend on both cellular and viral factors. For retroviruses such as MMLV, integration is preferentially targeted to promoter and regulatory regions [ 140 , 141 , 142 ]. Such preferences can be genotoxic owing to insertional activation of proto-oncogenes in patients undergoing gene therapy treatments for X-linked severe combined immunodeficiency [ 143 , 144 ], Wiskott–Aldrich syndrome [ 143 ], and chronic granulomatous disease [ 145 ]. Likewise, retroviral integration can generate chimeric and read-through transcripts driven by strong retroviral LTR promoters, post-transcriptional deregulation of endogenous gene expression by introducing retroviral splice sites (leading to aberrant splicing), and retroviral polyadenylation signals that lead to premature termination of endogenous transcripts [ 142 , 146 , 147 ].

Unlike retroviruses, lentiviruses prefer to integrate into transcribed portions of expressed genes in gene-rich regions, distanced from promoters and regulatory elements [ 140 , 142 , 148 ]. The cellular protein LEDGF/p75 aids in the target site selection by binding directly to both the active gene and the viral integrase within the HIV pre-integration complex [ 149 ]. Although the propensity of lentivirus to integrate into the body of expressed genes should increase the incidence of post-transcriptional deregulation, deletion of promoter elements from the lentiviral LTR (self-inactivating (SIN) vectors) has been reported to decrease transcriptional termination, but increase the generation of chimeric transcripts [ 149 ]. Overall, it appears that lentiviral SIN vectors are less likely to cause tumors than retroviral vectors with an active LTR promoter [ 148 , 150 , 151 , 152 ].

The 7.5–10 kb packaging limit of lentiviruses can accommodate the packaging, delivery, and stable integration of Cas9 cDNA, gRNAs, or Cas9 and gRNAs (all-in-one) to cells [ 153 , 154 ]. Often, a selectable marker, such as drug resistance, can also be included to isolate transduced cells. The high transduction efficiency of lentivirus can result in an abundance of CRISPR/Cas9-expressing cells to screen, compared with more traditional transfection methods. Stable and prolonged expression of CRISPR/Cas9 can facilitate targeting of multiple alleles of the gene of interest, resulting in more cells harboring homozygous gene modifications. Conversely, stable integration of CRISPR/Cas9 increases potential off-target effects. Moreover, lentiviral integration itself is a factor that may confound cellular phenotypes and should be considered when characterizing CRISPR-edited cell lines.

2.7. Gene Targeting Using Adeno-Associated Virus

Adeno-associated virus (AAV) is a human parvovirus with a single-stranded DNA genome of 4.7 kb, which was originally identified as a contaminant of adenoviral preparations [ 155 ]. The genome is flanked on both sides by inverted terminal repeats (ITR) and contains two genes, rep and cap [ 156 , 157 ]. Different capsid proteins confer serotype and tissue-specific targeting of distinct AAVs, in vivo. AAV cannot replicate on its own, and requires a helper virus, such as adenovirus or herpes simplex virus (HSV), to provide essential proteins in trans. AAV is the only known virus to integrate into the human genome in a site-specific manner at the AAVS1 site on chromosome 19q13.3-qter [ 158 , 159 , 160 ]. Although the precise mechanism is not well understood, the Rep protein functions to tether the virus to the host genome through direct binding of the AAV ITR and the AAVS1 site [ 158 , 160 , 161 ]. In the recombinant AAV (rAAV) vector system, the rep and cap genes are removed from the packaged virus, resulting in the loss of site-specific integration into the AAVS1 site. Despite removal of Rep, it has been shown that rAAV can still integrate, albeit randomly, into the host genome, via nonhomologous recombination, at low frequencies [ 162 , 163 , 164 ]. Furthermore, numerous clinical trials, to date, have shown that rAAV integration is safe and has no genotoxicity [ 165 , 166 , 167 ]. However, this “safety” is controversial, owing to preclinical studies suggesting genotoxicity in mouse models [ 168 , 169 , 170 , 171 ]. More studies are needed to understand the cellular impact of rAAV integration.

rAAVs have been used to deliver one or two CRISPR guide RNAs (gRNAs), in cells and model animals, by taking advantage of different rAAV serotypes to target specific cells or tissue types. Owing to the packaging capacity of rAAV, SpCas9 must be delivered as a separate virus, unlike lentivirus, which can be delivered as an “all-in-one” CRISPR/Cas9 vector. However, alternate, smaller Cas9s can be packaged into rAAVs [ 172 ]. Furthermore, rAAVs can be used to deliver repair templates or single-stranded donor oligonucleotides (ssODNs) for homology-directed repair (HDR), relying on the single-stranded nature of the AAV genome [ 173 , 174 ]. It has also been observed that rAAVs can integrate into the genome at CRISPR/Cas9-induced breaks in various cultured mouse tissue types, including neurons and muscle [ 175 ]. This observation goes against the notion of rAAVs integrating only at the AAVS1 locus, and should be considered when analyzing and characterizing rAAV-mediated CRISPR-edited cells.

3. Conclusions

There are many approaches to inserting new genetic information into chromosomes in cells and animals. At this time, the most appealing method is single copy gene insertion at a defined locus. This approach has numerous advantages, with respect to reproducible transgene expression. Random insertion transgenesis has been effectively used to probe gene function in mouse models [ 176 ]. It is generally accepted that this requires a spontaneous chromosome break [ 176 ]. Recent NGS data suggest that the repair mechanism resembles chromothripsis [ 118 , 177 ]. In addition to unintended gene disruptions owing to chromosome damage, the random insertion of transgenes exposes them to “position effects” in which their expression is controlled by neighboring genes [ 118 , 178 ]. Ideally, the insertion of reporter cDNAs in the genome results in single copy transgene insertions in defined loci in such a way that endogenous genes are not disrupted, and reporters are placed under the control of specific endogenous promoters [ 179 ]. The application of CRISPR/Cas9 technology to address this problem shows it can be used to achieve these goals [ 63 , 82 , 180 ]. The development of CRISPR/Cas9 base editing technology shows that it is possible to make single-nucleotide changes in the genome [ 181 , 182 , 183 , 184 ]. Base editors have the advantage that double-strand chromosome breaks are not produced, thus lessening the chances of undesirable mutations in the genome. A novel approach to small insertions in the genome by the use of a RNA donor sequence fused to the sgRNA in combination with a reverse transcriptase fused to dead Cas9 also avoids the need to produce double-strand breaks on chromosomes. This approach is referred to as “prime editing” [ 185 ]. CRISPR technology that avoids chromosome breaks, while making changes to the genome, is extremely important in clinical applications where unintended changes can adversely affect patients. These advanced versions of CRISPR technology will be important for future research.

The desire to apply CRISPR/Cas9 for the targeted insertion of transgenes is reflected in the profusion of methods directed towards this purpose [ 63 , 108 , 110 , 112 , 186 , 187 ]. Each method was successfully used to engineer mouse and rat genomes ( Table 1 ). Each method was shown to be more cost-effective and rapid than the application of mouse or rat ES cell technology. For the practitioner of the art, the question remains: which method is most efficient? That is to say, which method minimizes the number of animals needed for zygote production and maximizes the number of gene-targeted founders? One approach to this question is to compare the transgenic efficiency of each method [ 188 ]. The results in Table 1 show that the highest efficiency experiments were obtained when long single-stranded DNA donors and Cas9 ribonucleoproteins were used to produce genetically engineered mice. All methods are very effective compared with traditional methods of gene targeting in zygotes. Perhaps future avenues to even more efficient gene targeting lie in the application of small molecule activators for HDR [ 189 , 190 , 191 ].

Analysis of targeting vector knockin by CRISPR/Cas9 in mouse and rat zygotes.

1 Conditional: A critical exon was flanked by loxP sites, so as to produce a Cre-dependent knockout allele. Reporter: an exogenous coding sequence, such as for a fluorescent protein, was inserted. 2 RNP: ribonucleoprotein; Cas9 protein was complexed with guide RNA. Cas9 mRNA: in vitro transcribed mRNA from a plasmid containing Cas9 mixed with guide RNA. Cas9-mSa: in vitro transcribed mRNA from a plasmid containing Cas9 fused to monomeric streptavidin. 3 ssDNA: single-stranded DNA repair template. BioPCR: PCR was used to prepare biotinylated PCR amplicons. dsDNA: circular double-stranded DNA repair template. HMEJ: homology-mediated end joining; circular double-stranded DNA repair template incorporating sgRNA targets that flank homology arms. Tild: linear double-stranded DNA repair template. AAV: an adeno-associated vector donor was cultured with zygotes loaded with Cas9 RNP, by electroporation. 4 Efficiency, as calculated as the number of genetically engineered mice or rats produced per 100 zygotes treated with CRISPR/Cas9 reagents and transferred to pseudopregnant females.

Author Contributions

Conceptualization, T.L.S. Writing—review and editing, T.M.L.; H.C.K.; and, T.L.S. All authors have read and agreed to the published version of the manuscript.

This research was supported by Institutional Funds from the University of Michigan Biomedical Research Core Facilities.

Conflicts of Interest

The authors declare no conflict of interest.