Probabilistic Inference in Context-Specific Dynamic Networks

Abstract

Cells execute their functions through dynamic operations of biological networks. Dynamic networks delineate the operation of biological networks in terms of temporal changes of abundances or activities of nodes (proteins and RNAs), as well as formation of new edges and disappearance of existing edges over time. Global genomic and proteomic technologies can be used to decode dynamic networks. However, when using these experimental methods, it is still challenging to identify the temporal transition of nodes and edges. Thus, several computational methods for estimating dynamic topological and functional characteristics of networks have been introduced. In this thesis, I first summarize concepts and applications of these computational methods for inferring dynamic networks, and further summarize methods for estimating spatial transition of biological networks. After the summarization, I propose two novel integrative methods for inferring dynamic networks. First, I present principal network analysis (PNA) that can automatically capture major dynamic activation patterns over multiple conditions and then generate protein and metabolic subnetworks for the captured patterns using predetermined interactome. Then, I present a probabilistic model for estimating Time-Evolving GINs using Multiple Information (TEMPI). This model describes probabilistic relationships among network structures, time-course gene expression data, and gene ontology biological processes (GOBPs). The methods proposed in this thesis can serves as useful tools that can provide hypotheses for the underlying mechanisms in dynamic biological systems.