computational characterization of biomolecular networks in physiology and disease kakajan komurov,...
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Computational characterization of biomolecular networks in physiology and
disease
Kakajan Komurov, Ph.DDepartment of Systems Biology
University of Texas MD Anderson Cancer Center
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Classical to Systems Biology
Gene 1
Function 1
Gene 2
Function 2 . . .
Gene/protein/molecule-centric research
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Classical to Systems Biology
Phenotype 1
Phenotype 2Phenotype 3 . . .
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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
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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
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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
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Dynamics of protein interaction networks
Stimulus
Protein network
Gene expression program
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Dynamics of protein interaction networks
Stimulus
Protein network
Gene expression program
Remodeling of the network
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Dynamic organizational principles in protein networks
Komurov and White (2007), Komurov, Gunes, White (2009)
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Dynamic organizational principles in protein networks
Komurov and White (2007), Komurov, Gunes, White (2009)
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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
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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
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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?
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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
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Transition probability
Deriving node relevance scores
Relevance score at step k
Left eigenvector of the transitionprobability matrix
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Deriving Edge Flux (EF) value
Node relevance score = visitation probability
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Deriving Edge Flux (EF) value
Edge Flux
Node relevance score = visitation probability
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Too much bias towards network topology
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Deriving Edge Flux (EF) value
Edge Flux
Normalized Edge Flux
Node relevance score = visitation probability
Background node visitation score
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Low dose vs. high dose DNA damage
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Statistical analyses using EF values instead of gene valuesIdentifying link communities instead of gene communities
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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
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Cell culture model of drug resistance in breast cancer
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SKBR3 SKBR3-R
SKBR3 SKBR3-R +Lapatinib (1uM)
Perform NetWalk analysis of gene expression datato identify most active networks in lapatinib resistance
Strategy
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Over-represented networks in lapatinib resistance
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0 0.1 0.5 1 20
0.2
0.4
0.6
0.8
1
1.2
ControlGCGR inhibitor (5uM)
Lapatinib concentration (uM)
Surv
ivin
g fr
actio
n
Drug resistance can be reversed by diabetes drugs
0 0.15625 0.3125 0.625 1.25 2.5 5 100
0.2
0.4
0.6
0.8
1
1.2
SKBR3SKBR3-R
Metformin concentration (mM)
Surv
ival
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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)