Computational Tools for Metabolic Modeling and Gene Duplication Analysis

This thesis presents new computational methods to analyse both short and long-term effects of temperature increase on biological systems. First, we consider the problem of acclimation of an organism to increased temperatures on short timescales. We develop a novel method of network regression, Accli...

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Main Author: Spivakovsky-Gonzalez, Pablo
Format: Doctoral or Postdoctoral Thesis
Language:English
Published: University of Cambridge 2022
Subjects:
Online Access:https://www.repository.cam.ac.uk/handle/1810/339067
https://doi.org/10.17863/CAM.86478
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spelling ftunivcam:oai:www.repository.cam.ac.uk:1810/339067 2024-01-21T10:00:37+01:00 Computational Tools for Metabolic Modeling and Gene Duplication Analysis Spivakovsky-Gonzalez, Pablo 2022-07-03T19:22:46Z application/pdf https://www.repository.cam.ac.uk/handle/1810/339067 https://doi.org/10.17863/CAM.86478 eng eng University of Cambridge https://www.repository.cam.ac.uk/handle/1810/339067 doi:10.17863/CAM.86478 All Rights Reserved https://www.rioxx.net/licenses/all-rights-reserved/ Bioinformatics Thesis Doctoral 2022 ftunivcam https://doi.org/10.17863/CAM.86478 2023-12-28T23:22:34Z This thesis presents new computational methods to analyse both short and long-term effects of temperature increase on biological systems. First, we consider the problem of acclimation of an organism to increased temperatures on short timescales. We develop a novel method of network regression, AccliNet, based on the acclimation times, which takes into account prior knowledge of functional links between genes to improve the performance of the algorithm. The results obtained by AccliNet are compared with the performance of existing algorithms and are shown to be an improvement in this area. Next, we delve deeper into the metabolic response of the organism to changing temperatures, and develop methods to model and simulate the fluxes of metabolites occurring through a metabolic network. In particular, we construct a simplified model of aerobic respiration for an Antarctic species, and, given a gene expression dataset across different temperatures, we develop two different machine learning approaches to model the fluxes through the metabolic network. The first approach we use is based on denoising autoencoders. The performance of this method is compared to a traditional Bayesian inference approach and found to have higher accuracy. Next, we develop a different machine learning approach to model the unknown data distributions, in this case using a Generative Adversarial Network (GAN) to learn an SDE path through the sampled data points. The performance of this method is compared to the earlier autoencoder approach, as well as to other algorithms. The GAN method is found to have similar accuracy but less robustness to noise than the autoencoder approach. Lastly, we also consider the long-term effects of changing temperatures on biological systems. In particular, we develop a novel package for phylogenetic analysis, called PhylSim, which allows simulations and studies of adaptation and evolution under different scenarios of climate change. We apply the package to the case of adaptation of Antarctic species to their ... Doctoral or Postdoctoral Thesis Antarc* Antarctic Apollo - University of Cambridge Repository Antarctic
institution Open Polar
collection Apollo - University of Cambridge Repository
op_collection_id ftunivcam
language English
topic Bioinformatics
spellingShingle Bioinformatics
Spivakovsky-Gonzalez, Pablo
Computational Tools for Metabolic Modeling and Gene Duplication Analysis
topic_facet Bioinformatics
description This thesis presents new computational methods to analyse both short and long-term effects of temperature increase on biological systems. First, we consider the problem of acclimation of an organism to increased temperatures on short timescales. We develop a novel method of network regression, AccliNet, based on the acclimation times, which takes into account prior knowledge of functional links between genes to improve the performance of the algorithm. The results obtained by AccliNet are compared with the performance of existing algorithms and are shown to be an improvement in this area. Next, we delve deeper into the metabolic response of the organism to changing temperatures, and develop methods to model and simulate the fluxes of metabolites occurring through a metabolic network. In particular, we construct a simplified model of aerobic respiration for an Antarctic species, and, given a gene expression dataset across different temperatures, we develop two different machine learning approaches to model the fluxes through the metabolic network. The first approach we use is based on denoising autoencoders. The performance of this method is compared to a traditional Bayesian inference approach and found to have higher accuracy. Next, we develop a different machine learning approach to model the unknown data distributions, in this case using a Generative Adversarial Network (GAN) to learn an SDE path through the sampled data points. The performance of this method is compared to the earlier autoencoder approach, as well as to other algorithms. The GAN method is found to have similar accuracy but less robustness to noise than the autoencoder approach. Lastly, we also consider the long-term effects of changing temperatures on biological systems. In particular, we develop a novel package for phylogenetic analysis, called PhylSim, which allows simulations and studies of adaptation and evolution under different scenarios of climate change. We apply the package to the case of adaptation of Antarctic species to their ...
format Doctoral or Postdoctoral Thesis
author Spivakovsky-Gonzalez, Pablo
author_facet Spivakovsky-Gonzalez, Pablo
author_sort Spivakovsky-Gonzalez, Pablo
title Computational Tools for Metabolic Modeling and Gene Duplication Analysis
title_short Computational Tools for Metabolic Modeling and Gene Duplication Analysis
title_full Computational Tools for Metabolic Modeling and Gene Duplication Analysis
title_fullStr Computational Tools for Metabolic Modeling and Gene Duplication Analysis
title_full_unstemmed Computational Tools for Metabolic Modeling and Gene Duplication Analysis
title_sort computational tools for metabolic modeling and gene duplication analysis
publisher University of Cambridge
publishDate 2022
url https://www.repository.cam.ac.uk/handle/1810/339067
https://doi.org/10.17863/CAM.86478
geographic Antarctic
geographic_facet Antarctic
genre Antarc*
Antarctic
genre_facet Antarc*
Antarctic
op_relation https://www.repository.cam.ac.uk/handle/1810/339067
doi:10.17863/CAM.86478
op_rights All Rights Reserved
https://www.rioxx.net/licenses/all-rights-reserved/
op_doi https://doi.org/10.17863/CAM.86478
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