Predictive modelling of cancer through metabolic networks
- 1. GENOME-SCALE METABOLIC NETWORK RECONSTRUCTION: PREDICTIVE MODELLING OF CANCER THROUGH METABOLIC NETWORKS Presented by : PULAPARTHI BHAVITHA SAI LAKSHMI 15PIM2247 M.S. (Pharm.) Sem.-I, DEPARTMENT OF PHARMACOINFORMATICS NIPER, S.A.S. Nagar 1
- 2. FLOW OF PRESENTATION CANCER SYSTEM BIOLOGY GENOME SCALE MODELING OF HUMAN METABOLISM CASE STUDY: CONCLUSION 2
- 3. 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. 3 Science. 2008, 25: 2097-2116.
- 4. 4
- 6. CLASSIFICATION OF CANCER Carcinoma sarcoma Myeloma Leukemia Lymphoma 6 Class.Cancer. 2004, Google Patents.
- 7. IMAGING TESTS X-RAY FIBRE -OPTIC ENDOSCOPY COMPUTED TOMOGRAPHY(CT) ULTRA-SOUND MRI PHYSICAL EXAMINATION MICROSCOPY IDENTIFIED BY: TESTED BY: CONFORMED BY: 7
- 9. 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 9 Mol. Syst. biol.2008, 10.
- 10. SYSTEMS BIOLOGY systematic measurement technologies GENOMICS BIOINFORMATICS PROTEOMICS COMPUTATIONAL MODELS MATHEMATICAL MODELS METABOLOMICS 10 Mol. Syst.biol.2010, 7: 501.
- 11. GENOME-SCALE MODELING OF HUMAN METABOLISM GSSM COLLECTION OF METABOLIC REACTIONS SIMULATION OF GENETIC PERTURBATIONS GENE DELETIONS 11
- 12. • 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 12 Mol. Syst. biol. 2007, 3:135.
- 13. CASE STUDY 13
- 14. Modeling cancer metabolism on a genome scale Reconstructing a human cancer metabolic model Cancer-related metabolic phenotypes Phenotype based cell specific metabolic modelling Prediction of cell-specific metabolic liabilities using the NCI-60 collection 14
- 15. GENOME –SCALE MODELING OF METABOLISM CONSTRAINT BASE MOTHOD FLUX BALANCE ANALYSIS KINETIC MODEL MET.CONTR OL ANALYSIS STOCHASTIC MODEL CYBEMATIC MODEL 15 BMC Syst. Biol. 2008,4: 6.
- 16. 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. 16 Trends in bio.tech. 2003. 21: 162-169.
- 17. 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) 17 eLife.2005, 3: 3641.
- 18. 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; and 3) A generic model (the human model). 18 eLife.2005, 3: 3641.
- 19. 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. 19 eLife.2005, 3: 3641.
- 20. 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 20 eLife.2005, 3: 3641.
- 21. 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 Category p- 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 0.43-0.44 21 PLoS Comput Biol.2008, 8: e1002518-e1002518.
- 22. MLYCD SELECTIVELY SUPPRESSES CANCER CELL PROLIFERATION 22
- 23. CONCLUSION The 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. 23
- 24. 24
Editor's Notes
- #7: Sarcoma Leukemia