Stoichiometric identification with maximum likelihood principal component analysis

Mailier, Johan; Remy, M.; Wouwer, Alain Vande

doi:10.1007/s00285-012-0559-0

Cited by 17 publications

(9 citation statements)

References 15 publications

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“…In [ 5 ], Principal Component Analysis (PCA) is used to determine the minimum number of reactions required to interpret the data. This methodology was further extended in [ 6 ], where an insightful geometric interpretation is provided, and maximum likelihood principal component analysis (MLPCA) is used to estimate the reaction number and stoichiometric matrix. In this study, the latter approach is applied to the culture of hybridoma cells in sequential batch reactors (SBR).…”

Section: Introductionmentioning

confidence: 99%

Macroscopic Dynamic Modeling of Sequential Batch Cultures of Hybridoma Cells: An Experimental Validation

et al. 2017

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Hybridoma cells are commonly grown for the production of monoclonal antibodies (MAb). For monitoring and control purposes of the bioreactors, dynamic models of the cultures are required. However these models are difficult to infer from the usually limited amount of available experimental data and do not focus on target protein production optimization. This paper explores an experimental case study where hybridoma cells are grown in a sequential batch reactor. The simplest macroscopic reaction scheme translating the data is first derived using a maximum likelihood principal component analysis. Subsequently, nonlinear least-squares estimation is used to determine the kinetic laws. The resulting dynamic model reproduces quite satisfactorily the experimental data, as evidenced in direct and cross-validation tests. Furthermore, model predictions can also be used to predict optimal medium renewal time and composition.

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Section: Introductionmentioning

confidence: 99%

Macroscopic Dynamic Modeling of Sequential Batch Cultures of Hybridoma Cells: An Experimental Validation

et al. 2017

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“…A stoichiometric kernel therefore would encode coefficients for all substrates and products, where enzymes that do not interact would have stoichiometric coefficients of 0. Other authors [ 46 – 48 ] have defined and used similar types of stochiometric data, which can be converted into kernels to be consider with PRKs.…”

Section: Resultsmentioning

confidence: 99%

Metabolic network prediction through pairwise rational kernels

2014

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BackgroundMetabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations.ResultsWe create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy values have been improved, while maintaining lower construction and execution times.ConclusionsThe power of using kernels is that almost any sort of data can be represented using kernels. Therefore, completely disparate types of data can be combined to add power to kernel-based machine learning methods. When we compared our proposal using PRKs with other similar kernel, the execution times were decreased, with no compromise of accuracy. We also proved that by combining PRKs with other kernels that include evolutionary information, the accuracy can also also be improved. As our proposal can use any type of sequence data, genes do not need to be properly annotated, avoiding accumulation errors because of incorrect previous annotations.

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“…Some methods for the objective determination of macroscopic reaction scheme and stoichiometric identification have been developed [2][3][4][5][6][7][8] while the determination of the kinetic structure seems to remain based on arbitrary choices. Indeed, there exists in the literature a profusion of apparently equivalent laws allowing the description of specific kinetic phenomena (e.g.…”

Section: Introductionmentioning

confidence: 99%

Systematic methodology for bioprocess model identification based on generalized kinetic functions

Richelle

Bogaerts

2015

Biochemical Engineering Journal

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Stoichiometric identification with maximum likelihood principal component analysis

Cited by 17 publications

References 15 publications

Macroscopic Dynamic Modeling of Sequential Batch Cultures of Hybridoma Cells: An Experimental Validation

Macroscopic Dynamic Modeling of Sequential Batch Cultures of Hybridoma Cells: An Experimental Validation

Metabolic network prediction through pairwise rational kernels

Systematic methodology for bioprocess model identification based on generalized kinetic functions

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