predictive modelling of cancer through metabolic networks
TRANSCRIPT
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GENOME-SCALE METABOLIC NETWORK RECONSTRUCTION: PREDICTIVE
MODELLING OF CANCER THROUGH METABOLIC NETWORKS
Presented by :PULAPARTHI BHAVITHA SAI LAKSHMI
15PIM2247M.S. (Pharm.) Sem.-I,
DEPARTMENT OF PHARMACOINFORMATICSNIPER, S.A.S. Nagar
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FLOW OF PRESENTATION
CANCER
SYSTEM BIOLOGY
GENOME SCALE MODELING OF HUMAN METABOLISM
CASE STUDY: Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer
CONCLUSION
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CANCER
Cancer is a malignant growth or tumor resulting from an uncontrolled division of cells and with the potential to invade to other parts of the body.
Normal body cells grow, divide to make new cells, and die in an orderly way.
Science. 2008, 25: 2097-2116.
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TUMOR FORMATION
METASTASIS
UNCONTROLLED CELL DIVISION
AFFECT OTHER CELLS
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CLASSIFICATION OF CANCER
Carcinomasarcoma
MyelomaLeukemia
Lymphoma
Class.Cancer. 2004, Google Patents.
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DIAGNOSIS:IMAGING TESTS
X-RAY
FIBRE -OPTIC ENDOSCOPY
COMPUTED TOMOGRAPHY(CT)
ULTRA-SOUND
MRI
PHYSICAL EXAMINATION
MICROSCOPY
IDENTIFIED BY:
TESTED BY:
CONFORMED BY:
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TREATMENT:
CHEMOTHERAPY
RADIATION THERAPY
SURGERY
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Cancer is not just one disease, but a collection of disorders as such there is no single general treatment that is effective against all cancers.
To avoid this difficulty, SYSTEM BIOLOGY has been derived to construct a CELL SPECIFIC METABOLIC-NETWORK of cancerous cells.
This METABOLIC PHENOTYPE is to develop personalised treatment by finding countless chemical reactions which are occurring in a cancerous cell as well as in healthy cell.
CANCER SYSTEMS BIOLOGY: A NETWORK MODELING PERSPECTIVE
Mol. Syst. biol.2008, 10.
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SYSTEMS BIOLOGY
systematic measurement technologies
GENOMICS
BIOINFORMATICS
PROTEOMICS
COMPUTATIONAL MODELS
MATHEMATICAL MODELS
METABOLOMICS
Mol. Syst.biol.2010, 7: 501.
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GENOME-SCALE MODELING OF HUMAN METABOLISM
GSSM
COLLECTION OF METABOLIC REACTIONS
SIMULATION OF GENETIC
PERTURBATIONSGENE DELETIONS
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•opportunity for predicting new cytotoxic drug targets•Prediction of new targets for approved anti-cancer drugs.•52 Cytostatic drug targets has been predicted.
IDENTIFYING PERTURBATIONS
TARGETING CANCER METABOLISM
•The Cancer Genome Atlas and the International Cancer Genomics Consortium.•Transcriptomics and proteomics have been the main data source. •1,700 cancer genomes along with their gene expression levels has integrated.
INTEGRATING ADDITIONAL OMICS
DATA SOURES
•Development of metabolomics.•This strategy allows for the measurement of intracellular metabolic fluxes .•Metabolic alterations has been observed.
MAPPING THE CANCER
METABOLOME
Mol. Syst. biol. 2007, 3:135.
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CASE STUDY
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PHENOTYPE-BASED CELL-SPECIFIC METABOLIC MODELING REVEALS METABOLIC LIABILITIES OF
CANCER
Modeling cancer
metabolism on a genome scale
Reconstructing a human cancer
metabolic model
Cancer-related metabolic
phenotypes
Phenotype based cell specific metabolic modelling
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GENOME –SCALE MODELING OF METABOLISM
CONSTRAINT BASE
MOTHOD
FLUX BALANCE ANALYSIS
KINETIC MODEL
MET.CONTROL
ANALYSISSTOCHASTIC
MODELCYBEMATIC MODEL
BMC Syst. Biol. 2008,4: 6.
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FBA (FLUX BALANCE ANALYSIS): Flux balance analysis (FBA) is a widely used
approach for studying biochemical networks. FBA is the basis for several algorithms that predict
which reactions are missing by comparing in silico growth simulations to experimental results.
Does not require kinetic parameters. Calculates the flow of metabolites through this
metabolic network. Used to maximize and minimize every reaction in a
network.
Trends in bio.tech. 2003. 21: 162-169.
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GENERATION OF A PHENOTYPE-BASED CELL SPECIFIC (PBCS) GSMMS VIA THE PRIME APPROACH
HapMap dataset(for
normal cells)
NCI-60 datasets(for cancer cells)
BUILT A CELL-SPECIFIC MODEL
PRIME (Personalized Reconstruction of Metabolic
models)
eLife.2005, 3: 3641.
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THE PRIME ALGORITHM:
PRIME is the first method able to generate human cell-specific GSMMs that can predict metabolic phenotypes in an individual manner, including growth rates and drug response.
This model is utilized to identify a set of drug targets. PRIME is given the following three inputs:(1) A set of p samples with gene expression levels;(2) The samples' corresponding growth rate measurements; and3) A generic model (the human model).
eLife.2005, 3: 3641.
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DEFINING THE PRIME NORMALIZATION RANGE:
1. First, the set of essential reactions in the model is identified via Flux Balance Analysis.2. To define the maximal value of the normalization range we examine the change in biomass production as follows The set of reactions in the model. Examine the biomass production. Finally define the maximal value beyond which the
change in biomass production decreases.
eLife.2005, 3: 3641.
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PHENOTYPE BASED CELL SPECIFIC METABOLIC MODELLING
Gene expression of p
cells
Genome – scale
metabolic model
Phenotypic measurement
of p cells
Expression of phenotype associated
genes
Linear transformations
Model reactions, maximum
flux capacity
Gene expression
A set of genes associated
with phenotype
correlation
eLife.2005, 3: 3641.
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PREDICTION OF CELL-SPECIFIC METABOLIC LIABILITIES USING THE NCI-60 COLLECTION PRIME predicts the response of each individual cell
line to various metabolic drugs. In silico drug response is computed according to the
biological phenotype measured experimentally, which in this case includes ATP levels, or AC50/IC50 values.
Spearman correlation between measured and predicted drug response for 12 out of 16 drugs tested in the HapMap and the NCI-60 datasets.
HapMap NCI-60
Categoryp-value
Spearman R p-value Spearman R
0.66 0.03 0.59 -0.07
Mean pairwise 0.97 0.92
Proliferation rate >0.07 0.1-0.11 >3.6e-4 0.43-0.44PLoS Comput Biol.2008, 8: e1002518-e1002518.
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MLYCD SELECTIVELY SUPPRESSES CANCER CELL PROLIFERATION
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CONCLUSIONThe challenge of building integrated kinetic and stoichiometric models of cancer metabolism is to find new targets.
In the future, as more detailed kinetic information on specific central metabolism in humans will be gathered.
This modelling platforms will be crucial to develop potential technologies to improve research work.
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