Dissertation/ Thesis
Προβολή σε EDS

The application of neural differential equations to enzymatic time-series

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: The application of neural differential equations to enzymatic time-series
Συγγραφείς: Heusdens, Danica Aimee
Συνεισφορές: Rohwer, Johann, Stellenbosch University. Faculty of Science. Centre for Bioinformatics & Computational Biology.
Στοιχεία εκδότη: Stellenbosch University
Έτος έκδοσης: 2025
Συλλογή: Stellenbosch University: SUNScholar Research Repository
Θεματικοί όροι: Enzymatic analysis, Differential equations -- Data processing, Neural networks (Computer science) -- Mathematical models, Time-series analysis -- Data processing, Saccharomyces cerevisiae, Enzyme kinetics, UCTD
Περιγραφή: Thesis (MSc)--Stellenbosch University, 2025. ; Heusdens, D. A. 2025. The Application of Neural Differential Equations to Enzymatic Time-Series. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/1fb7a419-ca41-4aca-a096-aa7e4c0d8708 ; ENGLISH ABSTRACT: The aim of this thesis was to explore machine-learning models for parameter estimation in enzymatic systems, with focus on phosphoglycerate mutase (PGM) and enolase (ENO) in Saccharomyces cerevisiae. This thesis proposes a combination model of neural differential equations (NDE) and Hamiltonian Monte Carlo simulation (HMC). By utilizing time-series data obtained from the laboratory and processing it using the EnzymeML framework, this study demonstrated the combination of NDE-HMC and how it can enhance the accuracy of parameter estimation while reducing the computational demands compared to an HMC-only approach. This study employed the Catalax framework to train and validate the NDE, with the utilization of optimization algorithms such as L-BFGS and Nelder-Mead. The results show an improvement in computational efficiency and goodness of fit metrics for the NDE-HMC model compared to that of the HMC-only model. This thesis established a foundation for the utilization of machine-learning models to estimate enzyme kinetic parameters in biochemical reaction systems. ; AFRIKAANSE OPSOMMING: Geen opsomming beskikbaar. ; Masters
Τύπος εγγράφου: thesis
Περιγραφή αρχείου: viii, 74 pages : illustrations; application/pdf
Γλώσσα: unknown
Relation: https://scholar.sun.ac.za/handle/10019.1/132433
Διαθεσιμότητα: https://scholar.sun.ac.za/handle/10019.1/132433
Rights: Stellenbosch University
Αριθμός Καταχώρησης: edsbas.73DC2083
Βάση Δεδομένων: BASE
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  – Url: https://scholar.sun.ac.za/handle/10019.1/132433#
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PubType: Dissertation/ Thesis
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  Data: The application of neural differential equations to enzymatic time-series
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  Data: <searchLink fieldCode="AR" term="%22Heusdens%2C+Danica+Aimee%22">Heusdens, Danica Aimee</searchLink>
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  Data: Rohwer, Johann<br />Stellenbosch University. Faculty of Science. Centre for Bioinformatics & Computational Biology.
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  Data: Stellenbosch University
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  Data: 2025
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  Data: Stellenbosch University: SUNScholar Research Repository
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  Data: <searchLink fieldCode="DE" term="%22Enzymatic+analysis%22">Enzymatic analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Differential+equations+--+Data+processing%22">Differential equations -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Neural+networks+%28Computer+science%29+--+Mathematical+models%22">Neural networks (Computer science) -- Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Time-series+analysis+--+Data+processing%22">Time-series analysis -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Saccharomyces+cerevisiae%22">Saccharomyces cerevisiae</searchLink><br /><searchLink fieldCode="DE" term="%22Enzyme+kinetics%22">Enzyme kinetics</searchLink><br /><searchLink fieldCode="DE" term="%22UCTD%22">UCTD</searchLink>
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  Data: Thesis (MSc)--Stellenbosch University, 2025. ; Heusdens, D. A. 2025. The Application of Neural Differential Equations to Enzymatic Time-Series. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/1fb7a419-ca41-4aca-a096-aa7e4c0d8708 ; ENGLISH ABSTRACT: The aim of this thesis was to explore machine-learning models for parameter estimation in enzymatic systems, with focus on phosphoglycerate mutase (PGM) and enolase (ENO) in Saccharomyces cerevisiae. This thesis proposes a combination model of neural differential equations (NDE) and Hamiltonian Monte Carlo simulation (HMC). By utilizing time-series data obtained from the laboratory and processing it using the EnzymeML framework, this study demonstrated the combination of NDE-HMC and how it can enhance the accuracy of parameter estimation while reducing the computational demands compared to an HMC-only approach. This study employed the Catalax framework to train and validate the NDE, with the utilization of optimization algorithms such as L-BFGS and Nelder-Mead. The results show an improvement in computational efficiency and goodness of fit metrics for the NDE-HMC model compared to that of the HMC-only model. This thesis established a foundation for the utilization of machine-learning models to estimate enzyme kinetic parameters in biochemical reaction systems. ; AFRIKAANSE OPSOMMING: Geen opsomming beskikbaar. ; Masters
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  Data: https://scholar.sun.ac.za/handle/10019.1/132433
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    Subjects:
      – SubjectFull: Enzymatic analysis
        Type: general
      – SubjectFull: Differential equations -- Data processing
        Type: general
      – SubjectFull: Neural networks (Computer science) -- Mathematical models
        Type: general
      – SubjectFull: Time-series analysis -- Data processing
        Type: general
      – SubjectFull: Saccharomyces cerevisiae
        Type: general
      – SubjectFull: Enzyme kinetics
        Type: general
      – SubjectFull: UCTD
        Type: general
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      – TitleFull: The application of neural differential equations to enzymatic time-series
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            NameFull: Heusdens, Danica Aimee
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            NameFull: Rohwer, Johann
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            NameFull: Stellenbosch University. Faculty of Science. Centre for Bioinformatics & Computational Biology.
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            – D: 01
              M: 01
              Type: published
              Y: 2025
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