Bioengineering for multiple PAHs degradation using process centric and data centric approaches

Gosai, Haren B.; Sachaniya, Bhumi K.; Dudhagara, Dushyant R.; Panseriya, Haresh Z.; Dave, Bharti P.

doi:10.1016/j.chemolab.2018.04.019

Cited by 19 publications

(7 citation statements)

References 30 publications

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“…The optimum conditions for L-asparaginase production were predicted by creating a linear feed-forward ANN model using MATLAB R2018a software [ 3 9 -41]. The feed-forward model also known as multi-layer perceptron (MLP) has a gradient descent backpropagation (BP) learning algorithm and has been used expansively for biological applications [22,40,42]. BP algorithm performs this task by minimizing the error of changing weights that are inversely proportional to the negative error gradient.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…This kind of results can be explained by the ability of ANN to provide global optimization, as training of the neurons was repeated several times for different physico-chemical variables. Repeated training could be helpful to provide global optimization of the variables [22]. A disadvantage of the ANN can be requirement of high-quality data for the training.…”

Section: Ann Modelling Predictionmentioning

confidence: 99%

“…Apart from RSM, in recent years artificial neural network (ANN) has emerged as a popular tool for a wide range of bioprocess modelling and inference system including bioremediation and biodegradation techniques, quality assessment of water and sediments, forecasting of monsoon and river flow etc [20][21][22][23]. Although the use of ANN for optimisation of L-asparaginase production has been limited so far, no study on ANN analysis for halotolerant L-asparaginase has been reported to our best knowledge.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of Process and Data Centric Inference System for Enhanced Production of L-Asparaginase from Halotolerant Bacillus licheniformis PPD37

Patel

Gosai

Panseriya³

et al. 2021

Appl Biochem Biotechnol

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The present study aims at bioengineering of medium components usingdata and process centric approaches for enhanced production of L-asparaginase, an important biological molecule, by halotolerant Bacillus licheniformis PPD37 strain. To achieve this, first significant medium components were screened followed by optimization of a combination of media components and culture conditions such as L-asparagine, MgSO4, NaCl, pH and temperature. Optimization study was carried out using statistical models such as response surface methodology (RSM)process centric and artificial neural network (ANN)data centric approaches. The production improved from 2.86 U/mL to 17.089 U/mL, an increase of approximately 6-times of the unoptimized L-asparaginase production. On comparing RSM and ANN models for optimised L-asparaginase production based on R 2 value, mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute deviation (MAD) values, the ANN model emerged as the superior one. As this is the first report to the authors best knowledge on development of inference system using RSM and ANN models for enhanced L-asparaginase production using a halotolerant bacteria, this study could lead to more in-depth and large-scale Lasparaginase production.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Ann Modelling Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of Process and Data Centric Inference System for Enhanced Production of L-Asparaginase from Halotolerant Bacillus licheniformis PPD37

Patel

Gosai

Panseriya³

et al. 2021

Appl Biochem Biotechnol

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“…In an example of catabolic ME, a very recent study used an ANN model to screen media components and predict which ones would maximize the microbial degradation of polycyclic aromatic hydrocarbons, a molecule that has negative effects on marine and human health . Using data from 31 different shake flask runs utilizing varying media component quantities served to train the ANN model.…”

Section: Review Of Instances Of Data‐driven Me Effortsmentioning

confidence: 99%

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data‐Driven Metabolic Engineering

Presnell

Alper

2019

Biotechnology Journal

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The recent increase in high‐throughput capacity of ‘omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data‐driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of ‘omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data‐driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system‐scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.

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“…More recently, high‐throughput innovations, such as micro‐bioreactor arrays and small‐scale fully‐automated reactors, have been adopted to speed up medium development (Delouvroy et al, 2015; Rameez et al, 2014; Wilson, 2007), while real‐time analytics and diagnosis further enabled online medium adjustment (Ritacco et al, 2018). To enable a more targeted tuning of specific nutrients, metabolic flux analysis (MFA) (Xing et al, 2011) and mathematical optimization (Jones et al, 2016) have been applied, while the emergence of artificial intelligence and machine learning‐based approaches have enabled the optimization of entire medium composition (Grzesik & Warth, 2021; Zheng et al, 2017) as well as the screening of desirable supplements and components (Gosai et al, 2018; Tachibana et al, 2021). Machine learning has been further combined with genome‐scale metabolic modeling to predict the dynamic medium formulation required during cell culture (Schinn et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Combined multivariate statistical and flux balance analyses uncover media bottlenecks to the growth and productivity of Chinese hamster ovary cell cultures

Yeo

Park

Tan

et al. 2022

Biotech & Bioengineering

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Chinese hamster ovary (CHO) cells are widely used for producing recombinant proteins. To enhance their productivity and product quality, media reformulation has been a key strategy, albeit with several technical challenges, due to the myriad of complex molecular mechanisms underlying media effects on culture performance. Thus, it is imperative to characterize metabolic bottlenecks under various media conditions systematically. To do so, we combined partial least square regression (PLS‐R) with the flux balance analysis of a genome‐scale metabolic model to elucidate the physiological states and metabolic behaviors of human alpha‐1 antitrypsin producing CHO‐DG44 cells grown in one commercial and another two in‐house media under development. At the onset, PLS‐R was used to identify metabolite exchanges that were correlated to specific growth and productivity. Then, by comparing metabolic states described by resultant flux distributions under two of the media conditions, we found suboptimal level of four nutrients and two metabolic wastes, which plausibly hindered cellular growth and productivity; mechanistically, lactate and ammonia recycling were modulated by glutamine and asparagine metabolisms in the media conditions, and also by hitherto unsuspected folate and choline supplements. Our work demonstrated how multivariate statistical analysis can be synergistically combined with metabolic modeling to uncover the mechanistic elements underlying differing media performance. It thus paved the way for the systematic identification of nutrient targets for medium reformulation to enhance recombinant protein production in CHO cells.

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Bioengineering for multiple PAHs degradation using process centric and data centric approaches

Cited by 19 publications

References 30 publications

Development of Process and Data Centric Inference System for Enhanced Production of L-Asparaginase from Halotolerant Bacillus licheniformis PPD37

Development of Process and Data Centric Inference System for Enhanced Production of L-Asparaginase from Halotolerant Bacillus licheniformis PPD37

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data‐Driven Metabolic Engineering

Combined multivariate statistical and flux balance analyses uncover media bottlenecks to the growth and productivity of Chinese hamster ovary cell cultures

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