Modelling metabolism of contractile cells using constraint-based methods

Student thesis: Doctoral ThesisDoctor of Philosophy


Constraint-based genome-scale modelling is a widely used approach that facilitates computational exploration of biological networks of many organisms and diseases. This modelling approach is an efficient alternative to wet lab experiments for analysing cell metabolism and physiological functions. A plethora of constraint-based methods have been developed to facilitate a comprehensive analysis of these models. These methods can be applied to generate testable computational predictions and hypotheses.
The work carried out in this thesis was focused on developing and analysing three genome scale constraint-based models for the normal fibroblasts (control), TGF-β-stimulated-fibroblasts (myofibroblasts) and skeletal muscle cell. These models can provide valuable insight into the cell metabolic pathways and even study the metabolism of certain diseases. Fibroblast and myofibroblast models were compared to investigate the effects of TGF-β on cell proliferation, glycolysis and collagen synthesis. Having compared both models, the work then focused further on the myofibroblast model and investigated the metabolic pathways leading to collagen synthesis. Genes and reactions which are essential to collagen synthesis were identified. Most of the results generated by simulations were shown to support in vivo observations and could pave the way for identifying drug targets for patients with Idiopathic Pulmonary Fibrosis. In separate modelling, skeletal muscle simulations focused on studying the effects of hypoxia and amino acid supplementation on protein synthesis rate. This was with the view to providing valuable insights into metabolic requirements of muscle hypertrophy, possible causes (and treatments) of muscle atrophy, the cell responses to hypoxic stimulus and underlining the metabolic changes that occur in muscle during hypoxia. The modelling results showed that hypoxia directly impacts the protein synthesis rate and a metabolic switch from oxidative to glycolytic metabolism occurs. Furthermore, given the controversies surrounding amino acid supplements, studying the effects of amino acid supplementation on protein synthesis in silico can be an appropriate alternative to in vivo studies that aim at identifying the optimal combination of amino acids that could potentially trigger anabolic responses and increase protein synthesis in skeletal muscle. The results suggested that supplementation with certain combinations of amino acids could impact protein synthesis in muscle. Although further computational and experimental work may be required to verify these results, the current model predictions might pave the way for designing nutritional strategies that can positively influence the protein turnover in skeletal muscle.
Date of Award1 Jul 2020
Original languageEnglish
Awarding Institution
  • King's College London
SupervisorLindsay Edwards (Supervisor) & Stephen Harridge (Supervisor)

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