Applications of Protein-Constrained Genome-Scale Modelling for Strain Design and Context-Specific Metabolic Prototyping in Synthetic Biology

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Yao, Herbert
Genome-scale modelling , Systems Biology , Optimization , Synthetic biology
Genome-scale modelling (GEM) has been a research interest and a potent tool in cell-level modelling for decades. Among all studies that uses GEM, the simplest genome-scale metabolic model (M-model) is used in the vast majority of cases due to its accessibility in published models and algorithms, despite having clear limitations. Incorporating protein constraints to M-model greatly mitigates some of its limitations, yet there is little consensus regarding the formulation and algorithms for the resulting metabolic model with protein constraints (PC-model). In this thesis, we are proposing a toolbox specifically for building PC-model and tailoring algorithms for it, with an ultimate goal of building a new PC-model community that can enhance the accuracy of in-silico experiment through GEM. In Chapter 2, we introduce a new method, OVERLAY, to decipher the metabolism of the cell for a given transcriptome measurement using PC-model. This is accomplished in three main phases: first, a computational pipeline is developed to incorporate the published M-model with the enzyme information of the cell to produce a PC-model; second, the protein level is constrained by the experimental gene expression data through solving a two-step quadratic optimization, resulting in a context-specific PC-model; last, the context-specific PC-model is explored using flux variability analysis. In a case study, OVERLAY is proven proficient in decoding cellular metabolism and suggesting metabolic reprogramming strategies. In Chapter 3, we adapted several algorithms (minimal cell, PC-OptKnock, MOPA, PC-dynamicFBA) that was designed for M-model into the appropriate form for PC-model, some with boosted utilities. These algorithms are all for strain design and metabolic engineering purposes, and we believe they would synergize well with OVERLAY to exploit the potential of PC-model as a tool for simulating biochemical productions and biotechnology.
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