Team:XMU Software

Gene expression in both prokaryotes and eukaryotes is frequently controlled at the level of transcription. This process can be represented as a cycle consisting of four major steps: (1) promoter binding; (2) RNA chain initiation; (3) RNA chain elongation; and (4) termination. Since regulatory controls are exerted at each step, an understanding of the mechanism of each step is of general importance in understanding gene expression.

In the promoter part of our program, we’ve discussed the mechanism of promoter binding step and how it affects the transcription level. To complete our biobrick evaluation program and to better understanding of transcription process, we integrated the software developed by 2012 iGEM team SUSTC-Shenzhen-B to realize the prediction of transcription termination efficiency.

Termination, the last step of the transcription cycle, occurs when the RNA polymerase releases the RNA transcript and dissociates from the DNA template. It is important that transcription is imperfectly terminated at some terminator so that the ratio of the amount of the mRNA transcribed from upstream and that from downstream of the terminator is controlled. This regulation is quantified by the termination efficiency (%T),

Two mechanisms of transcription termination and two classes of termination signals have been described in bacteria: rho-dependent and rho-independent.

Rho-independent (also known as intrinsic) terminators are sequence motifs found in many prokaryotes that cause the transcription of DNA to RNA to stop. These termination signals typically consist of a short, often GC-rich hairpin followed by a sequence enriched in thymine residues.

The conventional model of transcriptional termination is that the stem loop causes RNA polymerase to pause and transcription of the poly-A tail causes the RNA: DNA duplex to unwind and dissociate from RNA polymerase.

In 2011, iGEM team SUSTC-Shenzhen-B developed a software tool TTEC to predict terminator efficiency. It takes DNA sequences as input and returns the terminator efficiency value.

In the algorithm, it takes 3steps to calculate the terminator efficiency:

1. Use RNA folding algorithm to predict the secondary structure of terminator and and recognize A tail, stemloop and T tail.

2. From the secondary structure, we calculate the free energy of stem loop, and generate a score by considering stem loop free energy and T tail

3. From the score, we predict the terminator efficiency based on the score-terminator equation.

The prediction of secondary and recognition of A tail, stemloop and T tail are achieved by Kings ford scoring system.

In 2007, Carleton L Kingsford et al described TransTermHP1, a new computational method to rapidly and accurately detect Rho-independent transcription terminators.

They put forward an algorithm to predict Rho-independent terminators.The first 15 bases of the potential tail sequence are scored using a function:

where

for n=1...15 and =1.

The energy of potential hairpin configurations adjacent to a reference position can be found efficiently with a dynamic programming algorithm. The table entry hairpin_score[i,j] gives the cost of the best hairpin structure for which the base of the 5' stem is at nucleotide position i and the base of the 3' stem is at position j. The entry hairpin_score[i,j] can be computed recursively as follows:

The function energy(i,j) gives the cost of pairing the nucleotide at i with that at j, and loop_pen(n) gives the cost of a hairpin loop of length n. The hairpin's loop is forced to have a length between 3 and 13 nt, inclusive, by setting loop_pen(n) to a large constant for any n outside that range. The constant 'gap' gives the cost of not pairing a base with some base on the opposite stem and thus introducing a gap on one side of the hairpin stem.

Table 1

Parameters used to evaluate hairpins

Pairing Energy

G-C -2.3

A-T -0.9

G-T 1.3

Mismatch 3.5

Gap 6.0

Loop_pen(n) 1•(n - 2)

Parameters used to evaluate the energy of a potential hairpin where n is the length of the hairpin loop

The D score is calculated by Carafa Scoring System

Scoring System 2 is based on the model created by d'Aubenton Carafa2 .The score of terminator consists of two parts, the free energy of stemloop and the score of 15 nt poly T tail. The free energy of stemloop is calculated using Loop Dependent Energy Rules3. The minimization of the free energy also determined the secondary structure of the stemloop. T tail score is calculated by the formula given by d’ Aubenton Carafa.

Detailed Calculation of Score

i. Closing Base Pair

For an RNA sequence, we number it from 5’ to 3’ . If i < j and nucleotides ri and rj form a base pair,we denote it by i.j. We call base ri’ or base pair i’.j’ is accessible from i.j if i <i’ ( <j’ ) <j and if there is no other base pair k.l so that i <k <i’ ( <j’ ) <l <j. We denote the collection of base and base pair accessible from i.j by L(i,j). Then i.j is the closing base pair. Here “L” means loop.

ii. n-loop

If the loop contain n – 1 base pairs, we denote it by n-loop. (Because there is a closing base pair, so we denote it by n-loop even though the closing base pair is not included in the loop.)

Here we can divide loops which may be formed in the terminator secondary structure into two kinds.

1-loop : Hairpin loop(size of loop shouldn’t be smaller than 3)

2-loop : Interior Loop(right strand size and left strand size are both bigger than 0.)

Buldge(Size of one strand is bigger than 0 and that of another strand is 0.)Stack(size of the loop is 0.)

G（i,j）= min { GH ( i , j ) , GS( i , j ) + G ( i + 1 , j – 1 ) , GBI( i , j ) } ;

GBI ( i , j ) = min{ gbi( i , j , k , l ) + G( k , l ) } for all 0 < k – i + l – j - 2 < max_size

G(i,j) is the minimum free energy change of stemloop formation. GH is the free energy change to form a hairpin loop. GS is the free energy change to form a stack. GBI is to calculate the minimum free energy change of structure containing 2-loop. gbi(i,j,k,l) is the free energy change to form 2-loop.

Here we consider 15 nucleotide in the downstream of stemloop. T tail score nT is calculated as follows :

In our program, if the length of the T tail( n ) is less than 15, we will only consider n nucleotides. If TL is more than 15, we will only consider 15 nucleotides.

Score = nT * 18.16 + deltaG / LH * 96.59 – 116.87

Here nT is T tail score. deltaG is the minimum free energy change of stemloop formation. LH is the length of stemloop^5.6

RBS-decoder is a software tool for evaluating the relative strength of RBS sequence. This program use the same method—PWM to calculate the similarity between RBS sequence and the position frequency matrix of SD sequence and transform the similarity to the relative strength of an RBS sequence.

Translational efficiency in Escherichia coli is generally determined at the stage of initiation. There are several principal mRNA sequence elements which can affect the kinetics of ternary initiation complex formation (30 S-mRNA-fMet-tRNA): the initiation codon, the Shine-Dalgarno (SD) sequence, the spacing between those two elements, and other factors.

How do baterial Ribosome Binding Sites work?

The bacterial ribosome binds to particular sequences on an mRNA, primarily the Ribosome Binding Site (RBS) and the start codon (ATG). The RBS base-pairs with an RNA molecule that forms part of the bacterial ribosome (the 16s rRNA), while the start codon base-pairs with the initiator tRNA which is bound to the ribosome. In addition to the sequences of the RBS and the start codon being important, these two sequences need to be positioned approximately 6-7 nucleotides apart so they can both make contact with the appropriate parts of the ribosome complex1.

The Shine-Dalgarno Sequence

The RBS sequence logo representing the sequences of 149 RBS from E. coli. The height of each letter represents the frequency of the base at that location. From Tom Schneider, "A Gallery of Sequence Logos".

The end of the 16s rRNA that is free to bind with the mRNA includes the sequence 5′–ACCUCC–3′. The complementary sequence, 5′–GGAGGU–3′, named the Shine-Dalgarno sequence, can be found in whole or in part in many bacterial mRNA. Very roughly speaking, ribosome binding sites with purine-rich sequences (A's and G's close to the Shine-Dalgarno sequence will lead to high rates of translation initiation whereas sequences that are very different from the Shine-Dalgarno sequence will lead to low or negligible translation rates. The sequence is named after the researchers who discovered the high frequency of the sequence upstream of bacterial coding sequences . You can see how common the sequence is by looking at the RBS sequence logo on the right (where the height of a letter indicates the frequency of the letter at that location).

As we know, the RBS strength is greatly influenced by the SD sequence, where the 16s RNA of the ribosom binds to, so the strength can be determined by the free binding energy between the SD sequence and the 16s RNA. So we designed a program calculating the free binding energy but the results show that the correlation between the free energy and the strength of RBS is rather weak (R2=0.5517). So we decide to find other algorithms for better accuracy.

Inspired by the strength prediction algorithms used in promoter part, in which the similarity to the sigma factors’ PWM is interlocking with the binding affinity between the protein and DNA sequence. We obtained the Position Frequency Matrix of SD sequence of E.coli and use the PMW method (illustrated in detail in the promoter part) to calculate the similarity between the RBS sequence and the Position frequency sequence, what is different from the promoter is that, the spacer length between the SD sequence and the startcodon and the start codon itself both act as constraints in locating the SD sequence, which is confined to 3-16 bp and ATG/TTG/GTG. And similar to the prediction of promoter strength, the spacer length between the SD sequence also contribute to the RBS strength, the weight of the influence of the spacer on strength is determined by our experiments in which we either mutated the SD sequence in a RBS sequence or change the spacer length to figure out the relative influence of these two factors 2.

We use the RBS sequence listed on the iGEM registry with experimentally determined relative strength, and the correlation between the RBS strength predicted by our software and the actual relative strength is strong, with a determination coefficient value 0.8039.

Due to scarcity of experimental data, the relative weight of the SD sequence and the spacer length used currently is roughly determined which may undermine the accuracy of RBS strength prediction. For further improvement of our program, we’ll try to obtain more reliable experimental data to accurately determine the weight used in our algorithm and hopefully elevate the accuracy of RBS-decoder.

In the next version of RBS-decoder, the secondary structure of the RBS sequence will be shown on the software and we’ll also include the other species’ SD sequence data in order to analyze predict the strength of RBS.

Pro-decoder is part of our software designed to analyze and evaluate promoters. The software use PWM method to calculate the similarity between promoter sequences and the position frequency matrix of transcription factor binding sites (TFBS) to locate the TFBS as well as predict the relative strength of the promoter. So the user may have a better understanding of the promoter’s regulation mechanism and the key sites which influences the promoter’s performance mostly.

Bacteria encode several thousands of different proteins, which are necessary for normal cell functions or for adaptation to environmental changes. These proteins are not required at the same time or in the same amount. Regulation of gene expression therefore enables the cell to control the production of proteins needed for its life cycle or for adaptation to extracellular changes. This regulation in turn makes it possible for the bacterium to adequately adapt to rapid changes in the environment. The various steps during transcription and translation are therefore subject to different regulatory mechanisms. The most prominent step in gene regulation is the initiation of transcription in which the DNA-dependent RNA polymerase (RNAP) is the key enzyme. The RNAP or the RNAP core enzyme is the catalytic machinery for the synthesis of RNA from a DNA template. However, RNAP cannot initiate transcription by itself. Initiation of transcription requires an additional polypeptide known as a sigma-factor. Sigma-factors are a family of relatively small proteins that can associate in a reversible way with the RNAP core enzyme. Together, the sigma-factor and the RNAP core enzyme form an initiation-specific enzyme, the RNAP holoenzyme

The sigma-factor directs RNA polymerase to a specific class of promoter sequences. Most bacterial species synthesize several different sigma-factors that recognize different consensus sequences. This variety in sigma-factors provides bacteria with the opportunity to maintain basal gene expression as well as for regulation of gene expression in response to altered environmental or developmental signals.

The frequency at which the RNAP holoenzyme initiates transcription, also known as the strength of a promoter, is influenced by the promoter sequence and the conformation of the DNA in the promoter region. The sigma-factors recognize two conserved sequences in the promoter region, known as the promoter consensus sequence. Sigma-factors or fragments of sigma-factors bind specifically to promoter DNA sequence and by specific base pair and amino acid substitutions in the promoter consensus sequences or sigma factors. Most bacterial species synthesize several different sigma-factors which direct the RNAP holoenzyme to distinct classes of promoters with a different consensus sequence. This variety in sigma-factors provides the bacterium with the opportunity to maintain basal gene expression as well as for regulation of gene expression in response to specific environmental stimuli.

The identification of bacterial promoters is an essential step in the elucidation of gene regulation. As a general rule, the more complex the life-cycle and environmental niche of a bacterium, the greater the number of sigma factors with corresponding promoter types. Typically however, the most common promoter type is that which regulates the housekeeping genes and the corresponding major sigma-factor is shared by all bacteria (sigma 70 in the well studied Escherichia coli, and its homologues in other species). The binding site for the sigma70-family of promoters is defined by two consensus hexamers, TTGACA and TATAAT, located at approximately −35 and −10, respectively relative to the transcript start site (TSS) and spaced 15–21 base pairs (bp) apart2. RNA polymerase core enzyme associates with the major sigma-factor to form the holoenzyme which in turn binds to its cognate promoters to initiate transcription.

In prokaryotes, the minimum requirement for RNA polymerase binding is recognition of the promoter by the sigma factor. In general, prokaryotic RNA polymerases can interchange a number of sigma factors which bind and initiate different groups of genes³.

Sigma factors are essential for the transcription initiation in Escherichia coli. In addition, promoter strengths are not determined purely by the binding of the sigma factor. other transcription factors can bind specific sequences surrounding or overlapping the promoter to either activate or repress transcription4. The mechanism is Transcriptional activators and repressors contribute to and detract from the accessibility of DNA by the RNA polymerase. These transcription-regulating nuclear proteins bind to specific binding sites in the regulatory regions (e.g. promoters, enhancers) of the genes thus providing their activation or repression.

Computational methods of predicting TF binding sites in DNA are very important for understanding the molecular mechanisms of gene regulation.

The binding sites of the same transcription factor show a significant sequence conservation, which is often summarized as a short (5–20 bases long) common pattern called a transcription factor binding site (TFBS) or binding consensus. Our software aims to figure out the possible TFBS in promoters and precisely locate the TFBS so that the user may know the exact sites that play a role in regulating the transcription.

In prokaryotes (lower organisms without nuclei), there are fewer TFs, their motifs tend to be relatively long and the strength of regulation for a particular gene often depends on how closely a particular site matches the consensus for the motif. The more mismatches to the consensus in a binding site, the less often the TF will bind and therefore the less control it will exert on the target gene. So our software will calculate the similarity between the possible TFBS in the promoter and the standard motifs so the user will know to which extent the transcription factor will control the promoter transcription

To facilitate the design of PCR primers of various promoters, we’ve developed an additional function, namely, primer design in this part og our program. After inputting the promoter sequence, the software will figure out the most suitable primers based on the theory of Thomas K¨ampke1, Markus Kieninger, and Michael Mecklenburg.

Genes and operons that are under control of the same TF are members of that TF’s regulon. Although methods for the prediction of regulons have been substantially improved, they are still far from perfect.

Comparative genomics tools can be used to predict regulons in bacterial genomes but the procedure can lead to incorrect regulon calling. Despite this drawback, several regulon databases are available that are based on comparative genomics methods and lack experimental evidence.

Probably the extended and accurate databases of regulons for E.coli are RegulonDB which provides the data source for our program.

Experimental results show that these are the strongest promoters that have been characterized in vitro so far and confirm the hypothesis that the consensus promoter sequence is “best.” To calculate the similarity between the promoter sequence and the best sequence, we implement the PWM method ⁵.

PWM (Position Weight Matrix)

Molecular techniques for the identification of promoters are both costly and time consuming, hence in silico methods are an attractive and well explored alternative. The most common in silico method to identify _70 promoters uses position weight matrices (PWMs) and depends on the relative conservation of the transcription factor binding site (TFBS, or motifs ).

The algorithm can be divided into two parts regarding to the difference between the motifs of sigma factors and other transcription factors.

Other transcription factors are proteins that can bind to a specific DNA sequence (motifs) and regulate the promoter’s transcription. To recognize these possible motifs in a given promoter sequence, we calculate the Matrix Similarity Score (MSS) of every possible sites in the promoter sequence using the position frequency matrix of 86 transcription factors published by RegulonDB. The algorithm reports only those matches of a matrix that have got MSS higher than the settled threshold. And MSS for a subsequence x of the length L is calculated in following steps:

fi,B, frequency of nucleotide B to occur at the position i of the matrix (B ∈{A, T, G, C})

fimin, frequency of the nucleotide which is rarest in position i in the matrix

fimax, highest frequency in position i.

The information vector

describes the conservation of the positions i in a matrix (5).Multiplication of the frequencies with the information vector leads to a higher acceptance of mismatches in less conserved regions, whereas mismatches in highly conserved regions are very much discouraged. This leads to a better performance in recognition of TF binding sites if compared with methods that do not use the information vector.

To determine the best threshold of the motif finding algorithm, we test various threshold values and analyze the true negative and false positive rate of each threshold value. The ideal threshold is supposed to have both the least true negative and false positive rates.

The picture above shows part of our test results and to keep both the true negative and false positive rates at a reasonable level, we adopt 3 threshold values, namely, low (0.5977) , median (0.0778) and high (0.85), with a true negative rate at 0.1, 0.5, and 0.7 respectively. And for more flexibility, we also allow the users to set their own thresholds.

In the case of sigma 70 factors, the motifs are the −35 and −10 hexamers. enclosing a spacer of length 15–19 bp.

Given a known or predicted TSS location, the corresponding predictions for the −10 and −35 hexamers are located using a combination of two PWMs which are derived from literatures. For any known or putative TSS, the −35 and −10 hexamers are located upstream of the TSS by searching for the highest combination of PWM scores, subject to two constraints:

(i) That the spacer length (the number of base pairs between the −35 hexamer and the −10 hexamer) should lie in the range {14–20};

(ii) the total MSS (our results are the sum of the scores for the −10 and −35 hexamers and therefore lie in the interval [0,2], with a score of 2 corresponding to the joint consensus TTGACA (−35) and TATAAT (−10).

In prokaryotes, the strength of sigma factor regulation for a particular gene often depends on how closely a particular site matches the consensus for the motif. The more mismatches to the consensus in a binding site, the less often the sigma factor will bind and therefore the less strength the promoter will have. Experiments have confirmed the hypothesis that the consensus promoter sequence is “best”. We set the best promoter strength to 100% and calculate the relative strength of a given promoter by the Score (promoter)

A primer pair (p, q) is assigned the scoring vector

sc(p, q) = (|p|, |q|,GC(p),GC(q), Tm(p), Tm(q), sa(p),sa(q), sea(p), sea(q), pa(p, q), pea(p, q))T ∈ R12

All primers are designed to have ideal values of length,GC content, and melting temperature which are specified externally by the designer of the hybridization experiment. These ideal values are to be specified for forward and reverse primers. The ideal score vector or reference vector for the primer pair is

&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp&nbsp sc_ideal =(length_f , length_r,GC_f ,GC_r , T_m,f ,T_m,r , 0, 0, 0, 0, 0, 0)^T.

All ideal annealing values are set to zero and typically

Tm_f= T_m,ras well as GC_f = GC_r . The final assessment of a primer pair (p, q) can be its deviation from the reference in terms of the l1-distance

Here, we employ a weighted distance

with weights given in the following table.

The formulas for calculating the variations above are provided in ⁷

To test the ability of our program to recognize the exact type of promoters, i.e., to nail down by which sigma factor a promoter is regulated, we run our program with 100 various promoters sequences whose type have already confirmed experimentally. And we’ve recognized 56 of them correctly. The results are showed below.

Specifically, we use the sigma70 promoter sequence with annotated -35 and -10 region provided by RegulonDB to test the correct prediction rate of the binding site of a specific transcription. We input 89 sigma70 promoter sequences and run our program to precisely locate the sigma factor binding site. Results show that the correct site prediction rate of our program is 64%.

The test results are as follows. the numbers represent the site of actual-35 motif, the actual spacer length, the predicted site and predicted spacer length respectively.

Making an attempt to predict the promoter strength through motif similarity and spacer length is one of our software’s shine points. We input XX promoter sequences with experimentally determined strength data. And the result shows that the determination coefficient is X.

Apply our algorithms to more species. Now Pro-decoder is designed expressly for prediction and evaluation of E.coli promoters, in the near future we’ll study the transcription regulation mechanism of other species and try to apply our algorithms to a extended range of species.

Enhance promoter strength prediction accuracy. Because our experimental data is so limited, the weight of the spacer length and the motif similarity is roughly determined, which lead to a weak correlation between the promoter strength. In the future we hope to obtain more experimental data with regard to the effect of spacer length and motif similarity having on promoter strength so we can revise the weight coefficient of the two factors and get more reliable results.

The next version of this part of our program will be able to analyze not only the promoters of E.coli, but other species such as Bacillus subtilis, we’ll integrate the transcription factor binding site data of more species into our database and use PWM algorithm to predict the TFBS in the promoters.

Our software team mainly focuses on programming the software by two methods using genetic algorithm, evaluating both optimization of single codon and codon pair and hence determining the fittest optimized sequence for expression in heterologous host cell.

After choosing the method and deciding the host cell, user inputs an original encoding sequence. By substitute the synonymous codons in the sequence, a new sequence is generated. Caluculate the fitness value and choose between the two sequences under the rule that the larger the value is, the higher the possibility of being chosen will be. The chosen sequence will be regarded as the original encoding sequence and repeat the steps until iteration stop criterion. By the way, user can click the “values” button to input the values needed in genetic algorithm (default value: population size—200, hereditary algebra—100, mutation probability--0.1, crossover probability--0.1).

Except methionine and tryptophan, all amino acids can be encoded by two to six synonymous codons, resulting from the degeneracy of the genetic code.1 However, unequal utilization of the synonymous condons leads to the phenomenon of codon usage bias, which is mainly due to natural selection, mutation and genetic drift.2 According to related studies, codon usage bias has certain connection with gene expression level. 3The larger the value of codon usage bias is, the higher gene expression will be. So the problem, how to substitute the synonymous codons aimed at raising the efficiency of gene expression and thus increasing the production of recombination protein in heterologous host cell, is expected to be addressed.

Individual codon usage optimization has been attached importance to, taking Codon optimizer4, Gene Designer5, OPTIMIZER6 for example. Subsequently, people found the effect of gene expression optimization cannot be perfect just by single codon optimization. Codon pair, namely the pair of k-th and (k+1)-th codons from the 5’ to 3’ end, is another crucial factor. Due to potential tRNA-tRNA steric interaction within the ribosomes7, the usage of rare condon pairs, which correlate with translation elongation, decrease protein translation rates8. Optimization of individual codon has an influence on the corresponding codon pair resulting in maybe-not-the-best codon pair optimization. In the same way, optimizing codon pair merely contributes to maybe-not- the-best single codon optimization. Therefore, it is a challenging way for us to apply a method considering and weighing the effects of single codon and codon pair optimization and thus make the whole best.

Our software team mainly focuses on programming the software by two methods using genetic algorithm, evaluating both optimization of single codon and codon pair and hence determining the fittest optimized sequence for expression in heterologous host cell.

Considering E. coli and S. cerevisiae are the ideal hosts for recombinant proteinexpression, and Gram-positive bacterium L.lactis and methylotrophic yeast P.pastoris are also promising candidates for expressing recombinant proteins9, we attached importance to selecting these four kinds of bacterium as host cell to optimize the sequence.

Fitness function：

In the function,

cpi is a value larger than zero, ranging from 10^-4 to 0.5，fit_cp(g) is the fitness function of the codon pair，fit_sc(g) is the fitness function of the single codon，w((c(k)，c(k+1)) is the weight of codon pairs in sequence g，|g| is the length of encoding sequence，c(k) is k-th codon in the sequence， is the target ratio of k-th codon, is the actual ratio of k-th codon in the sequence，the best value of cpi is 0.2 in the software.

In the function, the target ratio of k-th codon can be approximated by the equation below:

In the function, weight can be calculated by the equation below:

stands for the ratio of single codon c_kin the complete genome'is the number of pair (c_i,c_j) in high-expression genes，and high-expression genes are genes whose copy numbers of mRNA can be detected at least 20 per cell.

syn(c_k)stands for the synonymous codon set related to c_k,equals to the number of amino acid encoded by c_i in the whole protein set.

Based on the table below, we calculate function of single codon (ICU) , function of codon pair (CCO) and function of the combination of two (MOCO). MOCO aims at make the optimization of whole best by calculating the relative effect of ICU and CCO.

max&nbsp&nbsp&nbsp

s.t.&nbsp&nbsp&nbsp

max&nbsp&nbsp&nbsp

s.t.&nbsp&nbsp&nbsp

The MOCO problem is as follows (NSGA-II algorithm applied):

1. Randomly initialize a population of coding sequences for target protein.

2. Evaluate ICU and CC fitness of each sequence in the population.

3. Group the sequences into nondominated sets and rank the sets.

4. Check termination criterion.

5. If termination criterion is not satisfied, select the “fittest” sequences (top 50% of the population) as the parents for creation of offsprings via recombination and mutation.

6. Combine the parents and offsprings to form a new population.

7. Repeat steps 2 to 5 until termination criterion is satisfied.

The identification and ranking of nondominated sets in step 3 is performed via pair-wise comparison of the sequences’ ICU and CC fitness. For a given pair of sequences with fitness values expressed as and , the domination status can be evaluated as follows:

• If > and >= , sequence 1 dominates sequence 2.

• If >= and > , sequence 1 dominates sequence 2.

• If < and <= , sequence 2 dominates sequence 1.

• If <= and < , sequence 2 dominates sequence 1.

1. Shortening the computing time of the software

2. Expanding the range of the host cells

3. Improving bacterium’s resistance to toxic molecule

4. Advancing existed paths of synthetic biology by the method

5. Designing new paths of synthetic biology by the method

6. Increasing the output of recombinant protein

7. Predicting the expression of heterologous gene in a new host cell