1) Genome-scale metabolic network reconstruction allows for predictive modeling of cancer metabolism through metabolic networks. 2) By constructing cell-specific metabolic networks for cancerous cells using systems biology approaches like genome-scale modeling, the metabolic phenotypes of cancers can be determined. 3) Case studies have used constraint-based modeling techniques like flux balance analysis on reconstructed human metabolic networks to predict cancer-specific drug targets and metabolic liabilities by integrating omics data from cancer datasets.

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

5. TUMOR
FORMATION
METASTASIS
UNCONTROLLED
CELL DIVISION
AFFECT OTHER
CELLS
5

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

8. CHEMOTHERAPY
RADIATION THERAPY
SURGERY
8

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

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