Dataset Information

A Novel LSSVM Based Algorithm to Increase Accuracy of Bacterial Growth Modeling.

ABSTRACT:

Background

The recent progress and achievements in the advanced, accurate, and rigorously evaluated algorithms has revolutionized different aspects of the predictive microbiology including bacterial growth.

Objectives

In this study, attempts were made to develop a more accurate hybrid algorithm for predicting the bacterial growth curve which can also be applicable in predictive microbiology studies.

Materials and methods

Sigmoid functions, including Logistic and Gompertz, as well as least square support vector machine (LSSVM) based algorithms were employed to model the bacterial growth of the two important strains comprising Listeria monocytogenes and Escherichia coli. Even though cross-validation is generally used for tuning the parameters in LSSVM, in this study, parameters tuning (i.e.,'c' and '?') of the LSSVM were optimized using non-dominated sorting genetic algorithm-II (NSGA-II), named as NSGA-II-LSSVM. Then, the results of each approach were compared with the mean absolute error (MAE) as well as the mean absolute percentage error (MAPE).

Results

Applying LSSVM, it was resulted in a precise bacterial growth modeling compared to the sigmoid functions. Moreover, our results have indicated that NSGA-II-LSSVM was more accurate in terms of prediction than LSSVM method.

Conclusion

Application of the NSGA-II-LSSVM hybrid algorithm to predict precise values of 'c' and '?' parameters in the bacterial growth modeling resulted in a better growth prediction. In fact, the power of NSGA-II for estimating optimal coefficients led to a better disclosure of the predictive potential of the LSSVM.

SUBMITTER: Borujeni MS

PROVIDER: S-EPMC6371636 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Publications

A Novel LSSVM Based Algorithm to Increase Accuracy of Bacterial Growth Modeling.

Borujeni Masoud Salehi MS Ghaderi-Zefrehei Mostafa M Ghanegolmohammadi Farzan F Ansari-Mahyari Saeid S

Iranian journal of biotechnology 20180515 2

<h4>Background</h4>The recent progress and achievements in the advanced, accurate, and rigorously evaluated algorithms has revolutionized different aspects of the predictive microbiology including bacterial growth.<h4>Objectives</h4>In this study, attempts were made to develop a more accurate hybrid algorithm for predicting the bacterial growth curve which can also be applicable in predictive microbiology studies.<h4>Materials and methods</h4>Sigmoid functions, including Logistic and Gompertz, a ...[more]

PMID: 30805384

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Project description:A few bacterial cells may be sufficient to produce a food-borne illness outbreak, provided that they are capable of adapting and proliferating on a food matrix. This is why any quantitative health risk assessment policy must incorporate methods to accurately predict the growth of bacterial populations from a small number of pathogens. In this aim, mathematical models have become a powerful tool. Unfortunately, at low cell concentrations, standard deterministic models fail to predict the fate of the population, essentially because the heterogeneity between individuals becomes relevant. In this work, a stochastic differential equation (SDE) model is proposed to describe variability within single-cell growth and division and to simulate population growth from a given initial number of individuals. We provide evidence of the model ability to explain the observed distributions of times to division, including the lag time produced by the adaptation to the environment, by comparing model predictions with experiments from the literature for Escherichia coli, Listeria innocua, and Salmonella enterica. The model is shown to accurately predict experimental growth population dynamics for both small and large microbial populations. The use of stochastic models for the estimation of parameters to successfully fit experimental data is a particularly challenging problem. For instance, if Monte Carlo methods are employed to model the required distributions of times to division, the parameter estimation problem can become numerically intractable. We overcame this limitation by converting the stochastic description to a partial differential equation (backward Kolmogorov) instead, which relates to the distribution of division times. Contrary to previous stochastic formulations based on random parameters, the present model is capable of explaining the variability observed in populations that result from the growth of a small number of initial cells as well as the lack of it compared to populations initiated by a larger number of individuals, where the random effects become negligible.

Dataset Information

A Novel LSSVM Based Algorithm to Increase Accuracy of Bacterial Growth Modeling.

Background

Objectives

Materials and methods

Results

Conclusion

Publications

A Novel LSSVM Based Algorithm to Increase Accuracy of Bacterial Growth Modeling.

Similar Datasets

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