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Path Finding in Biological Networks

  • Lore ClootsEmail author
  • Dries De MaeyerEmail author
  • Kathleen MarchalEmail author
Part of the Springer Handbooks book series (SHB)

Abstract

Understanding the cellular behavior from a systems perspective requires the identification of functional and physical interactions among diverse molecular entities in a cell (i.e., DNA/RNA, proteins, and metabolites). The most straightforward way to represent such datasets is by means of molecular networks of which nodes correspond to molecular entities and edges to the interactions amongst those entities. Nowadays with large amounts of interaction data being generated, genome-wide networks can be created for an increasing number of organisms. These networks can be exploited to study a molecular entity like a protein in a wider context than just in isolation and provide a way of representing our knowledge of the system as a whole. On the other hand, viewing a single entity or an experimental dataset in the light of an interaction network can reveal previous unknown insights in biological processes.

In this chapter we focus on different approaches that have been developed to reveal the functional state of a network, or to find an explanation for the observations in functional data through paths in the network. In addition we give an overview of the different omics datasets and data-integration techniques that can be used to build integrated biological networks.

Keywords

Gene Ontology Interaction Network Causal Gene Target Node Functional Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

CDK1

cyclin-dependent kinase 1

CNV

copy number variation

ChIP

chromatin immunoprecipitation

DEAD

asp-glu-ala-asp

DIP

database of interaction proteins

DNA

deoxyribonucleic acid

FBN3

fibrilin 3

FST

follistatin

GBM

glioblastoma multiforme

GO

gene ontology

LR

likelihood ratio

MAP

maximum a posteriori

MIPS

Munich Information Center for Protein Sequences

MS

mass spectrometry

PPI

protein–protein interaction

PTEN

phosphatase and tensin homolog

PhI

phosphorylation interaction

RAS

rat sarcoma

RNA

ribonucleic acid

SPINE

safe programmable and integrated network environment

STRING

search tool for the retrieval of interacting genes

TF

transcription factor

TRI

integrated transcriptional

Y2H

yeast two hybrid

eQED

eQTL electrical diagram

eQTL

expression quantitative trait loci

mRNA

messenger RNA

sRNA

small RNA

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Copyright information

© Springer-Verlag 2014

Authors and Affiliations

  1. 1.Centre of Microbial and Plant GeneticsKU LeuvenHeverleeBelgium
  2. 2.Department of Microbial and Molecular SystemsKU LeuvenHeverleeBelgium
  3. 3.Department of Plant Biotechnology and BioinformaticsGhent UniversityGentBelgium

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