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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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