Computational Characterization - Complex Networks - Lecture Slides, Slides of Computer Networks

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Computational characterization of

biomolecular networks in

physiology and disease

Classical to Systems Biology

Gene 1

Function 1

Gene 2

Function 2

Gene/protein/molecule-centric research

Classical to Systems Biology

• Systems-level analyses
• High throughput experiments
  • high content data
• Genomics, proteomics, metabolomis, … - “omics” fields
• Extensive use of computational tools
• Computational systems biology

Phenotype 1

Phenotype 2

Phenotype 3...

Computational systems biology

• Studying organizational principles

of biological systems

– Dynamic structure – function

relationship in biological networks

• Developing computational tools to

analyze/interpret large-scale data

Dynamics of protein interaction

networks

Stimulus

Protein network

Gene expression program

Dynamics of protein interaction

networks

Stimulus

Protein network

Gene expression program

Remodeling of the network

Dynamic organizational principles in

protein networks

Komurov and White (2007), Komurov, Gunes, White (2009) Docsity.com

Cancer systems biology

• Extensive data collection at

the whole-genome level

• The Cancer Genome Atlas

Project

• Expression Oncology project
• Alliance for Signaling project
• System-level understanding

of cellular processes activated in cancer

• Computational methods to

maximize analytic power, generate testable hypotheses

Objectives

• Analyze data within the context of a priori

information

• Physical interactions
• Function similarity
• Sequence similarity
• Co-localization

• Extract most relevant genes/subnetworks

• Genes with high data values
• Coordinately regulated genes with similar functions
• Genes with partially redundant functions

• How to score importance/relevance of a

gene/subnetwork to the given experimental

context?

NetWalk

• Principle: relevance of a gene depends on its

measured experimental value and its connections

to other relevant genes

• Random walk – based method for scoring

network interactions for their relevance to the

supplied data

• Simultaneously assesses the local network

connectivity and the data values of genes

• No data cutoffs, assesses the whole data

distribution

Transition probability

Deriving node relevance scores

Relevance score at step k

Left eigenvector of the transition probability matrix

Deriving Edge Flux (EF) value

Node relevance score = visitation probability