Dana Pe'er - Disseration
From Gene Expression to Molecular Pathways
Molecular networks involving interacting proteins, RNA, and DNA molecules,
underlie the major functions of living cells.
DNA microarrays probe how the gene expression changes
to perform complex coordinated tasks in adaptation to a changing environment
at a genome-wide scale.
In this dissertation we address the challenge of reconstructing molecular pathways
and gene regulation from gene expression data. Our goal is to automatically infer regulatory
relations between genes, as well as other types of molecular interactions.
To answer this challenge, we develop probabilistic graphical models of the biological
system. We offer three such models and algorithms to automatically learn these from
gene expression data. Our models and learning algorithms are based on the
assumption that statistical correlation might indicate molecular or genetic interaction.
We offer systematic evaluation for each of the methods presented culminating in experimental
validation of novel predictions, automatically generated by one of our models.