Improving Genome Scale Metabolic Models using Gene Regulatory Networks
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Biotechnology (MPBIO), MSc
Publicerad
2024
Författare
Davidsson, Liam
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This thesis aims to enhance Genome Scale Metabolic Models (GEMs) by integrating
Gene Regulatory Networks (GRNs) to improve modeling capabilities for the yeast
Saccharomyces cerevisiae. The project involved constructing a pipeline to incorporate
gene expression data into GEMs, resulting in a constrained metabolic model
with a more diverse and characteristic flux distribution. By utilizing transcription
data and transcription factor interactions, the integration aims to provide a more
comprehensive understanding of cellular processes. The research demonstrates that
incorporating GRNs can enhance the predictive accuracy of GEMs, despite the
challenges associated with data complexity and integration methodologies. Potential
future work includes upgrading the GRN to a dynamic Bayesian network and
exploring the effects of network size on model outcomes.
Beskrivning
Ämne/nyckelord
Genome Scale Metabolic Models, GEM, Gene Regulatory Network, GRN, Co-Regulation, Gene Expression, Machine Learning, Decision Trees, Random Forest.