Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

In this work, a new hybrid metaheuristic algorithm is proposed based on the combination of the genetic algorithm (GA) and crowd search algorithm (CSA), for determining parameters in a E. coli growth model. The work is interesting and can greatly interest the scientific community that studies fermentation processes. Before being considered for publication, authors must attend to the following:

a) The introduction presents an extensive review of the algorithms that have recently been applied. However, the way they offer it is extremely extensive. I think it can be simplified by summarizing the most relevant works.

b) The authors do not comment on the conventional methods used for determining parameters and optimizing mathematical models. It would be essential to have a point of comparison between the different methodologies, both in the introduction and in the results.

c) The validation of the model requires a greater number of tests, those presented in the work are appropriate but do not allow us to know the scope of the proposed methodology. Evaluating different experimental scenarios could show the scope of the proposal.

Comments on the Quality of English Language

No comments

Author Response

Reviewer 1

 

Dear reviewer,

Thank you for your valuable comments and suggestions. Taking into account all of them, we believe that the quality of the paper has been improved significantly.

All improvements are presented in red colour to be easily identifiable by the editors and reviewers.

Following are the detailed answers, point by point, to each of your comments.

 

1. The introduction presents an extensive review of the algorithms that have recently been applied. However, the way they offer it is extremely extensive. I think it can be simplified by summarizing the most relevant works.

 

Authors’ answer:

The section Introduction has been revised according to the reviewer’s comments. The literature review of the modifications and hybridizations of CSA was shortened.

 

1. The authors do not comment on the conventional methods used for determining parameters and optimizing mathematical models. It would be essential to have a point of comparison between the different methodologies, both in the introduction and in the results.

Authors’ answer:

Comments about conventional and metaheuristic algorithms have been added to the section Introduction. The Results and Discussion section has been expanded to include the findings of the Sequential Quadratic Programming (SQP) and Quasi-Newton (Q-N) methods, which we found to be the most appropriate based on our expertise. The new results and model simulations obtained with SQP and Q-N (with four different initial solutions) are presented in the new Figure 4. The two best models are now added to Figure 5 (previously Figure 4) for comparison with results obtained by metaheuristic algorithms. A new Table 6 with the obtained SQP and Q-N models is provided. The estimated parameters of the two best models are added in the next Table 7 (previously Table 6). The new results have been discussed. The necessary modifications have been made in the abstract and also in the concluding section.

 

1. The validation of the model requires a greater number of tests, those presented in the work are appropriate but do not allow us to know the scope of the proposed methodology. Evaluating different experimental scenarios could show the scope of the proposal.

Authors’ answer:

The best-obtained models based on SQP and Q-N methods have been also verified by an independent experimental data set. The results have been included in the subsection 5.3. Verification of the obtained mathematical model of E. coli BL21(DE3)pPhyt109 Fed-Batch Cultivation Process. To assess the accuracy of the proposed models, the residuals, i.e., the differences between experimental data and the model predicted values, have been calculated based on Eq. (5). The obtained J values (errors) are presented in the new Table 9. The new results have been discussed.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

In this manuscript, Zoteva and Roeva explores a new hybrid metaheuristic algorithm built upon the good exploration of the genetic algorithm (GA) and exploitation of the crow search algorithm (CSA). The efficiency of the proposed GA-CSA hybrid is studied with a model parameter identification procedure of E. coli BL21(DE3)pPhyt109 fed-batch cultivation process. In summary, this manuscript was well-written, comprehensive, and the experiments were carefully designed and performed. The significance and novelty were clearly stated that the GA-CSA hybrid proposed in this paper is a proven successful collaborative hybridization of GA and CSA with outstanding performance. The proposed hybrid algorithm produced the most accurate model and best described the dynamics of the considered CP of E. coli BL21(DE3)pPhyt109. Upon verifying the models, again the GA-CSA model showed the closest proximity to the available experimental data. After all, only some minor changes need to be addressed before final publication.

Comments:

1)    In Figure 1, the two “experimental data” fonts are not identical. Also, in part (b), maybe it is better to show the whole plot, where is the higher limit of processing variables for the Phytase? And do these biomasses, substrate, and phytase have replicates being performed?

2)    In line 416, does the author meant Fig. 4? And this whole Figure parts a-c need to be reformatted to look better and more concise.

Author Response

Reviewer 2

 

Dear reviewer,

Thank you for your valuable comments and suggestions. Taking into account all of them, we believe that the quality of the paper has been improved.

All improvements are presented in red colour to be easily identifiable by the editors and reviewers.

Following are the detailed answers, point by point, to each of your comments.

 

Comments:

 

• In Figure 1, the two “experimental data” fonts are not identical. Also, in part (b), maybe it is better to show the whole plot, where is the higher limit of processing variables for the Phytase? And do these biomasses, substrate, and phytase have replicates being performed?

 

Authors’ answer:

Thank you for the comments. We have increased the maximum value on the Y-axis of Figure 1 to better visualize the final phytase value. We have also ensured that the fonts are identical across the entire plot. To answer your second question, if we understand it correctly, we have two data sets from the cultivation of the same E. coli strain. Two cultivations were performed (CP1 with  and CP2 with ) and results are available from both of them. One of the data sets is used for model identification, and the other is used for verification.

 

• In line 416, does the author meant Fig. 4? And this whole Figure parts a-c need to be reformatted to look better and more concise.

 

Authors’ answer:

Thank you. Yes, it was Fig. 4. Now, it is Fig. 5. Based on the recommendation of the other reviewer, we have conducted further comparisons with two deterministic algorithms. The new results, which are the model simulations obtained with SQP and Q-N (using different initial solutions), have been presented in the new Figure 4. We have also added the best two models to Figure 5 (previously Figure 4) to compare them with the model results that were obtained using the metaheuristic algorithms.

Author Response File: Author Response.pdf