Metabolic adaptation of a human pathogen during chronic infections - a systems biology approach

Biological systems are complex. When we want to understand biological processes we often need advanced methods to reveal the relationship between genotype and phenotype. The focus of this thesis has been to extract biological meaningful features from complex data sets and to use mathematical modeling to uncover how human pathogens adapt to the human host.

Pseudomonas aeruginosa infections in cystic fibrosis patients are used as a model system for under-­‐ standing these adaptation processes. The exploratory systems biology approach facilitates identification of important phenotypes and metabolic pathways that are necessary or related to establishment of chronic infections.

Archetypal analysis showed to be successful in extracting relevant phenotypes from global gene expression da-­‐ ta. Furthermore, genome-­‐scale metabolic modeling showed to be useful in connecting the genotype to phenotype at a systemic level. Particular metabolic subsystems were identified as important for metabolic adaptation in P. aeruginosa.

One altered metabolic phenotype was connected to a genetic change; a finding that was possible through the systems characterization and which was not identi-­‐ fied by classical molecular biology approaches where genes and reactions typically are investigated in a one to one relationship. This thesis is an example of how mathematical approaches and modeling can facilitate new biologi-­‐ cal understanding and provide new surprising ideas to important biological processes.