Modelling Enzyme Allocation and Metabolic Adaptation in Cyanobacteria

Genome-scale metabolic modeling (GEM) has become a fundamental part of systems biology, providing computational representations of cellular metabolism. While GEMs are instrumental in simulating metabolic pathways, offering biotechnological applications, they cannot accurately represent cellular phenotypes. Conventional GEMs neglect enzymatic constraints and the economic optimization of cellular processes, leading to predictions that may be metabolically feasible but not biologically realistic. To address these limitations, we employed two extended modeling approaches, namely GECKO and RBA. GECKO integrates enzymatic kinetics into GEMs, providing a more accurate representation of cellular metabolism. RBA extends this framework by incorporating additional constraints related to macromolecular synthesis and cellular compartments, allowing for a deeper understanding of resource allocation within the cell.

This thesis investigates resource allocation and metabolic adaptation in cyanobacteria through a series of case studies that utilize the GECKO and RBA protocols. We first reconstructed a GECKO model for Synechocystis sp. PCC 6803 to study the effects of light intensity on metabolism and enzyme utilization. The GECKO model enabled us to predict phenotypes that conventional GEMs could not capture. Building on this initial study, we developed an RBA model for the same organism to investigate resource allocation under varying light conditions. This framework incorporates additional constraints related to macromolecular synthesis and cellular compartmentalization, allowing us to represent how cyanobacteria allocate resources across different light conditions emphasizing the trade-offs involved in cellular processes. Given the simplicity of the GECKO protocol compared to RBA, we continued to develop GECKO models for two additional projects. The first of these GECKO models was reconstructed to investigate metabolic flux and reprogramming for engineered strains of Synechocystis sp. PCC 6803 that overproduce aromatic amino acids and compared their metabolic profiles with those of the wildtype strain. Finally, we reconstructed GECKO models for two other cyanobacterial strains, Synechococcus elongatus PCC 7942 and Synechococcus sp. PCC 7002, to analyze differences in flux and enzyme allocation between these strains. This approach helps us understand how metabolic fluxes and enzyme usage vary among different cyanobacterial species.

Overall, this work aims to investigate the metabolic adaptation and enzyme allocation strategies of cyanobacteria under various conditions by employing the GECKO and RBA protocols.