Computational characterization of biomolecular networks in physiology and

disease

Kakajan Komurov, Ph.DDepartment of Systems Biology

University of Texas MD Anderson Cancer Center

Classical to Systems Biology

Gene 1

Function 1

Gene 2

Function 2 . . .

Gene/protein/molecule-centric research

Classical to Systems Biology

Phenotype 1

Phenotype 2Phenotype 3 . . .

Classical to Systems Biology

Phenotype 1

Phenotype 2Phenotype 3 . . .

• Systems-level analyses

• High throughput experiments – high content data

• Genomics, proteomics, metabolomis, … - “omics” fields

• Extensive use of computational tools

• Computational systems biology

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

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)

Dynamic organizational principles in protein networks

Komurov and White (2007), Komurov, Gunes, White (2009)

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

Biological complexity

• ~22,000 annotated human genes in RefSeq• ~60,000 known protein-protein interactions in human• Millions of indirect relationships between genes• Typical genomic experiment: millions of data points

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 transitionprobability matrix

Deriving Edge Flux (EF) value

Node relevance score = visitation probability

Deriving Edge Flux (EF) value

Edge Flux

Node relevance score = visitation probability

Too much bias towards network topology

Deriving Edge Flux (EF) value

Edge Flux

Normalized Edge Flux

Node relevance score = visitation probability

Background node visitation score

Low dose vs. high dose DNA damage

Statistical analyses using EF values instead of gene valuesIdentifying link communities instead of gene communities

Development of drug resistance in breast cancer

• Lapatinib: drug that blocks activity of HER2 oncoprotein

• Patients with activated HER2 have good initial response to the drug, but develop resistance in a short time

• Our strategy: identify networks supporting the drug resistance of breast cancer cells to lapatinib

Cell culture model of drug resistance in breast cancer

SKBR3 SKBR3-R

SKBR3 SKBR3-R +Lapatinib (1uM)

Perform NetWalk analysis of gene expression datato identify most active networks in lapatinib resistance

Strategy

Over-represented networks in lapatinib resistance

0 0.1 0.5 1 20

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1.2

ControlGCGR inhibitor (5uM)

Lapatinib concentration (uM)

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actio

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Drug resistance can be reversed by diabetes drugs

0 0.15625 0.3125 0.625 1.25 2.5 5 100

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SKBR3SKBR3-R

Metformin concentration (mM)

Surv

ival

Acknowledgments• Ph.D Mentor: Michael White, Ph.D• Current Mentor: Prahlad Ram, Ph.D• Ram lab:

– Melissa Muller, Ph.D– Jen-Te Tseng– Sergio Iadevaia, Ph.D

• Ju-Seog Lee, Ph.D• Yun-Yong Park, Ph.D

• Collaborators:– Luay Nakhleh, Ph.D (Rice

University)– Michael Davies, M.D Ph.D (MDA)– Mehmet Gunes, Ph.D (UNR)