Intern Report

Indian Institute of Technology, Kharagpur
A Project Report on
Systematic Evaluation of Method for
Integration of Transcriptome Data into
Kinetic Models
Guided By:
Prof. Nagasuma Chandra
Indian Institute of Science, Bangalore
Submitted By:
Tushar Singh Soam
Dual Degree, Biotechnology

BONAFIDE CERTIFICATE
This is to certify that this project report entitled “Systematic Evaluation of
Method for Integration of Transcriptome Data into Kinetic Models”
submitted to the Indian Institute of Science, Bangalore in partial fulfilment of
the requirement for the Dual Degree, Biotechnology, Indian Institute of
Technology, Kharagpur is a bonafide record of work done by Tushar Singh
Soam under my supervision from May 2015 to June 2015. No part of this work
has ever been submitted for any other award.
Prof. Nagasuma Chandra
Associate Professor
Department of Biochemistry
IISc, Bangalore
Tel: Office: +91-08-22932892, E-mail: nchandra@biochem.iisc.ernet.in

i
ABSTRACT
Metabolic pathways of a biological system involves tremendous complexities as they
are interconnected intricately and there are interactions at different temporal scales between
the different components. Modelling a metabolic pathways kinetically in silico is a big
challenge as determining the kinetic parameters of the enzymes involved is difficult, time
consuming and not reliable sometimes. The large amount of high-throughput genome scale
data available to us is demanding the development of integrated mathematical models.
Efficient integration protocols would help in reducing the difficulties in modelling the
metabolic pathways from the scratch.
The aim of this study is to measure the level of metabolic remodelling captured upon
data integration. Glycolysis pathway reported in AS30D cell line (rat liver cancer) was used
as the base model for the present study. We have integrated transcriptomics data from various
sources in our base model to convert it to a normal cell model. The steady state of metabolites
from data integrated normal cell was compared to metabolites from Human metabolite Data
base (HMDB) to measure the accuracy of our protocol.
Results indicated that a normal cell model can be obtained with approximately 70%
accuracy. It is also proved that catalytic activity of metabolic enzymes remains constant in
these two conditions. Analysis also delineates that inter-level (spatial-scale) data integration
gives erroneous results and should be avoided until the data is made compatible for the
respective level.

ii
ACKNOWLEDGEMENT
“It is not possible to prepare a project report without the assistance and encouragement of
other people. This one is certainly no exception.”
On the very outset of this report, I would like to extend my sincere & heartfelt obligation
towards all the personages who have helped me in this process. Without their active
guidance, help, cooperation and encouragement, I would not have made headway in the
project.
I am ineffably indebted to Dr. Nagasuma Chandra for providing me this opportunity, her
conscientious guidance and encouragement to accomplish this assignment.
I am extremely thankful and pay my gratitude to my mentor Miss Madhulika Mishra for her
valuable guidance and support on completion of this project. This work would not have taken
this shape without her.
Special thanks are due to Mr. Sumanta Mukherjee and Miss Chandrani Rajput for all the
valuables discussions, that we had while our walk to mess, and for making IISc mess
accessible to me.
I thank all the members of the lab: Priyanka, Sathya Baarathi, Narmada, Deepesh Nagrajan,
Praveen, Pip, Richa, Amrisha, Jyothi, Abhilash, Raghu, Abhinandan and Awanti for making
lab a comfortable and enjoyable place to work.
I also acknowledge with a deep sense of reverence, my gratitude towards my parents and
member of my family, who has always supported me morally as well as economically.
At last but not least gratitude goes to all of my friends who directly or indirectly helped me to
complete this project report.
Thanking You
Tushar Singh Soam

iii
LIST OF CONTENTS
ABSTRACT............................................................................................................................i
ACKNOWLEDGEMENT .....................................................................................................ii
LIST OF CONTENTS ......................................................................................................... iii
LIST OF ABBREVIATIONS...............................................................................................iv
Introduction............................................................................................................................1
Metabolic Reprogramming.................................................................................................1
Kinetic Modelling of Metabolic Pathways.........................................................................2
Databases and Software .........................................................................................................3
Cancer Glycolysis Model.......................................................................................................4
A. Comparison between in vivo and in silico steady states of metabolites for two
different cell lines (AS30D and HeLa)...............................................................................5
B. Comparison between in-silico steady states of metabolites of different cell lines
(AS30D and HeLa).............................................................................................................6
Section 2.................................................................................................................................9
A. Gene Expression Data integration from ArrayExpress .................................................9
B. Integration of Meta-analysis Gene-expression Data....................................................12
C. Integration of HepG2/Hepatocytes Gene Expression Data .........................................15
Section 3...............................................................................................................................17
A. Activity Based Data Integration ..................................................................................17
B. Integration of Both Activity and Gene-expression Data..............................................19
Remark .................................................................................................................................22
References ............................................................................................................................23

iv
LIST OF ABBREVIATIONS
HMDB Human Metabolite Data Base
HCC Hepatocellular carcinoma
HK Hexokinase
PFK Phosphofructokinase
HPI Hexose Phosphate Isomerase
MCA Metabolic Control Analysis
BRENDA Braunschweig Enzyme Database
GEO Gene Expression Omnibus
FBP Frutose-1, 6-bisphosphate
F6P Fructose-6-phosphate
PEP Phosphoenol Pyruvate
FC Fold Change
SBML System Biology Mark-up Language
GLUT Glucose Transporter

1
Introduction
Cancer cells grow and divide at an unregulated pace and thus to meet the increased
nutritional demands, cells alter their metabolism to a very large degree as is defined by
“Warburg effect” according to this phenomena cancer cells will meet their increased demand
of energy by accentuated rates of glycolysis even when there is enough oxygen present to
respire [1]. Since, there are significant changes between normal and a cancer cell’s
glycolysis, it can be used as a system to evaluate the method of data integration. Also, there is
lot of data reported in literature regarding the pathway dynamics of glycolysis in cancer
condition.
Hence, we are using glycolysis as a model system to evaluate method of
transcriptome data integration into mathematical model. In this work, the glycolysis cancer
model will be used as the base model and further transcriptome data would be integrated with
it to evaluate methodology. The significant changes found in the two conditions can also be
utilised to propose biomarkers which can be used as therapeutic targets, however this aspect
is our principal goal for the present study.
Metabolic Reprogramming: -
Cancerous cell, in general, shows an increment in the glycolytic flux irrespective of the cell
type. This accelerated glycolysis rate is due to the over-expression of pathway enzymes,
which again is induced by some oncogenes and other hypoxia inducible factors (affects
cellular response to systemic oxygen levels) [1]. Production of different isoforms and
overexpression of certain pathway enzymes leads to the change in control distribution of
tumor glycolysis from that of normal cell glycolysis. However, change in catalytic activity
can also happen in this case, but there are no significant mutations found for glycolytic
enzymes in literature [2]. According to [3] and [4], HK and PFK are the major controlling
steps in normal cell while in tumor cells control gets shifted to HK and HPI with a lot of
control around HK (may be a different isoform). It is reported that to reduce glycolytic flux
by 50% in a tumor cell we have to reduce the activity of HK by 76%. Flux control
coefficients of pathway enzymes gives us an idea about those enzymes which differ in tumor
and normal cells and thus can be accounted as therapeutic targets for tumor disease.

2
Kinetic modelling of a metabolic pathway takes into account all the available
information (e.g. enzyme parameters, metabolic concentrations and fluxes) to mimic the
behaviour of the intracellular environment which can be used to predict the metabolic
concentrations and flux at different steady states [1]. If this approach is extended to the
cancer cell, it will be very useful in identifying the therapeutic targets in a tumor cell.
Since, information regarding kinetics of most of enzymes from cancer cells are not
available, it is prerequisite to develop a method which can linearly integrate data available
for normal cells and omics data for cancer cells to reconstruct cancer-specific kinetic
model. This integrated kinetic model can be further explored to understand metabolic
reprogramming as well as for drug target identification.
Kinetic Modelling of Metabolic Pathways: -
The elucidation, understanding, and eventually prediction of the behaviour of metabolic
systems represent a big challenge [5]. Cellular metabolic pathways constitutes a complex
dynamical system and gives rise to a wide variety of dynamical phenomena, including
multiple steady states and temporal oscillations.
Models are made out from composition of concepts to have a more clear
understanding and simulate the subject the model represents. Reactions occurring among a
defined set of reactants defines a kinetic reaction network. To model the time evolution of
metabolite’s concentration and flux of individual steps, we use reaction rate equations present
among the individual species of the pathway. For modelling the kinetics of a metabolic
pathway, we need to have precise knowledge of functional form of all involved enzymatic
reactions and their associated parameters. For the relevant kinetic data of all the enzymes
present in the pathway we rely mostly on the data reported in the literature previously and use
Michaelis-Menten kinetics for all the enzymes, which is considered a good approximation
for most enzymes’ reaction rates. Once the model is established it can serve as a virtual
laboratory that allows building up of characteristics description of the system and give
insights into the design principles of the same [6], [7]. MCA is the systems level approach to
analyse metabolism quantitatively. The central concept in MCA is of control coefficients
which are a quantitative measurement of the extent to which the activity of a single enzyme
determines the pathway flux [8].

3
Databases and Software
1. ArrayExpress: - It is a database of functional genomics data, generated from
microarray and high-throughput sequencing experiments. We have used it for the
determination of differentially expressed genes in a tumor and normal cell [9].
2. GeneCards: - GeneCards is a searchable, integrated database of human genes that
provides comprehensive, updated, and user-friendly information on all known and
predicted human genes. We used it for knowing the EC number and Entrez gene
number for different isoforms of enzymes involved in glycolysis pathway [10].
3. GEO: - GEO is another high-throughput public functional genomics data repository
[11].
4. BioModels: - It is a repository of computational models of biological processes.
Models collected from literature are manually curated and enriched with cross-
references from external data sources. All models are available freely for use,
modification and distribution, to all users [12].
5. BRENDA: - It is an enzyme information system representing one of the most
comprehensive enzyme repositories. We have used it to determine which isoform of an
enzyme is involved in the pathway by comparing the Km values from the literature
[13].
6. Gepasi: - Gepasi is a software package for modelling biochemical systems. It
simulates the kinetics of systems of biochemical reactions and provides a number of
tools to fit models to data, optimize any function of the model, perform metabolic
control analysis and linear stability analysis. We have used it to convert the .gps to
.xml format because the cell designer supports only .xml format [14].
7. CellDesigner: - CellDesigner is a structured diagram editor for drawing gene-
regulatory and biochemical networks. We have used it to modify our basic model
[Alvaro et al.] by updating the enzyme parameters determined from omics data
integration, simulate and view the dynamics of modified model through an intuitive
graphical interface [15].
8. Liverome: - It is a curated database of liver cancer-related gene signatures. The gene
signatures were obtained mostly from published microarray and proteomic studies, and
thoroughly curated by experts. We have used this database to confirm the accuracy of
meta-analysis data obtained from other databases [16].

4
Cancer Glycolysis Model
We have used Alvaro et al. [3] cancer model as our basic model and following is the
representation of the pathway considered in the model,
Fig 1: Schematic diagram of glycolysis model in cancer cell. Blue colour represents main metabolites of the
pathway; Brown colour represents the metabolite interacting with glycolysis pathway; Red colour denotes
inhibitors and green colour represent activators of pathway. Enzyme are shown in light brown colour.
Assumptions: -
1. All reactions are considered reversible.
2. Specific activity of enzymes is taken as the measure of maximum velocity of reaction.
3. Those metabolites for which the either source or sink is not present inside the
boundary of pathway are taken as constant ( e.g. Lactate, Citrate, Ery4p, glycogen, Pi,
Xy5p, F26BP)
Model was constructed by using Gepasi v3.3 software by Alvaro et al.

5
Section 1
A. Comparison between in vivo and in silico steady states of
metabolites for two different cell lines (AS30D and HeLa)
Modelling of biological systems is known to have lots of artefacts because of complexity of
the system as well as because of lack of complete and accurate information about the
involved species. Comparing the two cells types-glycolysis in silico model with the in vivo
steady states, we can account on the degree of accuracy of the model.
Comparison between steady concentration of metabolites in in vivo and in silico for HeLa
cells: -
Comparison between steady concentration of metabolites in in-vivo and in-silico for HeLa
cells: -
0
5
10
15
20
25
30
Glu(in) G6P F6P FBP DHAP G3P PEP Pyr ATP ADP AMP NAD
Concn.(mM)
Metabolites
AS30D
In vivo Model
R2-value = 0.85
0
2
4
6
8
10
G6P F6P FBP DHAP PEP Pyr ATP ADP AMP
Concn.(mM)
Metabolite
HeLa
In vivo Model
R2-value = 0.88

6
Result: -
As the comparison of in vivo and in silico steady states of metabolites for both the cell lines
has a higher (>0.8) correlation value, it delineates that the kinetic model was able to predict
the in vivo behaviour to quite a good level and was able to predict with high accuracy the
concentration of most of the metabolites in the pathway except FBP, F6P and PEP.
The main sources of differences between real biological system and kinetic models is the lack
of information about the enzymes kinetics, the difference between kinetic parameters of
enzymes in vitro and in vivo and our assumptions. Thus, for building a model of high
accuracy a refinement process is necessary to prompt an experimental re-evaluation of the
kinetic properties of some enzymes as well as the determination of neglected metabolite
concentrations and the inclusion of some branches to improve model reproduction of in
vivo pathway behaviour.
B. Comparison between in-silico steady states of metabolites of
different cell lines (AS30D and HeLa)
Comparison of Vmax of pathway enzymes among AS-30D and HeLa: -
0
5
10
15
20
25
30
GLUT HK HPI PFK ALDO TPI GAPDH PGK PGAM ENO PYK LDH
U/(mgofcytosolicprotein)
Enzyme
Vmax Comparison
AS30D Hela
R2-value = 0.88

7
Comparison between steady state in-silico metabolite concentrations among AS30D and
HeLa:-
Comparison between in-silico steady state metabolic flux among AS30D and HeLa: -
0
2
4
6
8
10
12
14
16
Concn(mM)
Metabolites
Metabolites Concn. Comparison
AS30D HeLa
R2 -value = 0.73
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
Flux(mM/min)
Enzyme
Flux Comparison
AS30D Hela
R2-value = 0.86

8
Result: -
Pattern of Vmax values in AS-30D and HeLa cells shows that specific activity of the
enzymes of glycolytic pathway is much higher in AS-30D cells than HeLa cells. Hence, the
higher amount of metabolites and more flux of intermediary steps in AS-30D in comparison
to HeLa cells is expected as glucose supply is kept constant. Since, FBP is an inhibitor of
PYK enzyme and due to its high production in AS-30D than HeLa it inhibits PYK (Pyruvate
Kinase) more and hence production of pyruvate is more in HeLa than AS-30D.
HeLa cells also have higher rate of glycogen degradation than AS-30D cells because while
preparations HeLa cells were incubated in a medium whose glucose content was quite high as
compared to the that of AS-30D cell’s medium. This leads to the translation of low activity
isoform of GLUT in HeLa and thus to meet the demands of higher glucose, glycogen
degradation is more in HeLa cells.

9
Section 2
Gene Expression Data Integration
A. Gene Expression Data integration from ArrayExpress
Objective: -
Gene-expression data from ArrayExpress was integrated into cancer cell (AS30D) glycolysis
model from Alvaro et al. [3] to generate a normal cell model and further this integrated model
was explored for normal glycolysis.
Method: -
We know that,
Vmax = Kcat*Et
Where, Kcat = turnover number of the enzyme
Et = Concentration of enzyme catalytic sites
For a cancer cell,
(Vmax)c = Kcat*(Et)c
Similarly, for a normal cell,
(Vmax)n= Kcat*(Et)n
Where, subscript ‘n’ demotes normal cell and ‘c’ denotes cancer cell.
So,
Vc/Vn= (Et)c/(Et)n
Where, Vc denotes (Vmax)c and same for normal cell parameter.
(Et)c/(Et)n can be defined as fold change, denoted by FC.
Hence,
Vc/Vn = FC
Where, we are assuming that turnover number of the enzymes is constant.
Kinetic parameter (Vmax) of an enzyme in the normal cell can be calculated by integrating
fold change of normal versus cancer cell.

10
We have used gene expression data available in ArrayExpress database to determine the
differential gene expression among the two types of cell and integrated the significantly
expressed genes into the cancer cell model to reconstruct a normal cell model.
We have used gene expression profiles from a HCC tissues and adjacent normal liver tissues
from three patients. Array platform used is “A-AFFY-44-Affymetrix GeneChip Human
Genome U133 Plus 2.0 [HG-U133_Plus_2]” submitted by GSE ID-33006. Normalization of
the gene expression data from ArrayExpress is done by MAS5 algorithm.
Differentially expressed genes were determined by employing a t-test on the normalized
expression levels of genes in normal lever tissues and HCC tissues.
T-test was used to determine if two sets of data are significantly different from each other,
and is most commonly applied when the test statistic would follow a normal distribution. We
have used p-value as the characteristics of differentiation between two sets and the statistical
significance is kept at 0.05.
Differential gene-expression analysis was performed using t-test acknowledging the
statistical significance of the p-value (Appendix-Table 10).
Table 1- List of differentially expressed genes is as follows
Gene p-value
Normalized
gene expression
in Cancer cell
Normalized
gene expression
in Normal cell FC
HK1 0.046359 529.313 726.292 0.728788
ALDOB 0.015168 239.796 484.046 0.495399
LDHA 0.038207 23903.6 38331.3 0.623605
AK3 0.007923 6301.375 12394.36 0.508407
Table 2- Conversion of kinetic parameters from a cancer to a normal cell
Enzyme Parameter Cancer Cell FC Normal Cell
HK Vm 0.35 0.728788 0.480249
ALDO Vmf 0.056 0.495399 0.11304
Vmr 0.044 0.495399 0.088817
LDH Vmf 2 0.623605 3.207157
Vmr 0.27 0.623605 0.432966

11
Steady-state analysis: -
Libsbml ode solver was used for steady-state analysis. Simulation was performed up-to
100,000 time points.
0
1
2
3
4
5
6
7
8
9
10
Concn.(mM)
Metabolites
Metabolite Comparison
Normal Cell Cancer Cell HMDB
R2-Value = 0.38

12
Result: -
As it can be observed from the comparison (Refer Table -11 Appendix), correlation between
steady state concentration of metabolites in in silico gene expression data integrated normal
cell and HMDB reported data is very low and also metabolites are not regulated in cancer
condition in the manner they are supposed to be (Table-5) which suggests that this integration
of gene expression does not fully characterize the metabolic remodelling.
Since, data used for integration was from clinical sample, however model was built on
cell line data and problem in extrapolating the model from one system-level to another
system-level is a well-known problem in modelling [17], this can be one of the possible
reason for this.
In step, we further integrated gene expression data from meta-analysis to cross-check
the above results.
B. Integration of Meta-analysis Gene-expression Data
Objective and methods employed in this data integration protocol is same as the previous one
except the fact that here differentially expressed genes and their fold change were determined
by meta-analysis and later on their regulation is confirmed by liverome database (Table-6).
Table 3- For HBV (Hepatoma B virus) HCC: -
Enzyme FC (abs) Regulation
GAPDH 1.5994142 up
ALDOA 1.7271312 up
ENO1 1.7579875 up
ENO3 2.5182812 down
ALDOB 1.7974195 down
GPI 1.673636 up
TKT 2.1196654 up
LDHD 1.8587292 down
Table 4- For HCV (Hepatoma C virus) HCC: -
Gene Symbol FC (abs) Regulation
GAPDH 1.527778 up
ENO3 3.1940975 down
ALDOB 2.1119492 down
HK3 1.5619298 down
GPI 1.7150853 up
TKT 2.705986 up
LDHD 1.807008 down

13
Libsbml ode solver was used for steady-state analysis. Simulation was performed up-to
100,000 time points.
Steady-state analysis of HCV HCC: -
Comparison of steady state metabolite concentration in cancer and normal cells: -
0
5
10
15
20
25
30
Concn.(mM)
Metabolites
Concentration Comparison
Cancer Cell HCV_N HBV1_N HBV3_N

14
Comparison of steady-state flux of intermediary reaction in pathway: -
Result: -
Analysis showed that metabolism is not significantly different in both cancer and normal cell
at flux as well as metabolite level and also the regulation of metabolites is not as per the
results (Table-5) which is derived from the literature. However, previous works [1] has
shown major alterations in glycolysis in cancer cells which shows that this integration of
gene-expression data is producing erroneous results and was not able to capture metabolic
remodelling.
This confirms the previous result, that integrating tissue-level gene-expression data
into model does not mimic the normal condition.
In next step, we have integrated gene-expression data at the cell line level.
-0.005
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
Flux(mM/min)
Enzymes
Flux Comparison
Cancer Cell HCV HBV1 HBV3

15
C. Integration of HepG2/Hepatocytes Gene Expression Data
Objective and methods employed in this data integration protocol is again same as section
1A. Differentially expressed genes in HepG2 cell line (relative to hepatocytes) and their
expression level (hence the fold changes) are calculated from [18].
Simulation was performed by using Libsbml solver to 1,000,000 time points
Comparison of steady-state metabolite concentration in cancer and normal cells: -
0
1
2
3
4
5
6
7
8
9
Concn.(mM
Metabolites
Cancer Normal HMDB
R2-value = 0.68

16
Comparison of steady-state flux of intermediary reaction of the pathway in cancer and normal
cell: -
Result: -
Analysis showed that correlation between data integrated normal cell and data reported in
HMDB at metabolite level is 0.68. Metabolites were regulating (Table-5) in the appropriate
manner, qualitatively. Flux variation pattern between cancer cell and normal also confirms
that this time we were able to capture metabolic remodelling to some extent in our data
integrated model.
Thus, referring all the results of this section, it can be inferred that integrating the
gene-expression data at cell line level can improve the ability of integrated normal model to
mimic the in vivo behaviour.
In next step, we further explored whether gene expression is solely responsible for
metabolic remodelling or not. For this, we integrated specific activity variation obtained from
Alvaro et al. [4].
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
Flux(mM/min)
Enzymes
Flux Comparison
Cancer Cell Normal cell

17
Section 3
A. Activity Based Data Integration
Objective: -
We have a cancer cell model of glycolysis from Alvaro et al. [3], we have extracted the
activity variation of enzymes in two conditions from Alvaro et al. [4] and then integrate this
data into cancer model to remodel it into a normal cell model and then verify whether this
model is mimicking the in vivo behaviour or not.
Method: -
Fold change of specific activity of the enzymes involved in the pathway was directly
integrated into the model.
Steady-state Analysis: -
Simulation was performed by using Libsbml ode solver to 100,000 time points.
Steady state analysis of normal cell (activity data integrated): -

18
cell: -
-0.005
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
Flux(mM/min)
Enzymes
Flux Comparison
Cancer Normal
-1
0
1
2
3
4
5
6
7
8
9
10
Concn.(mM)
Metabolites
Cancer Normal
R2-value = 0.69

19
Result:-
Table 5- Data extracted from literature regarding the metabolites concentration variation in a
tumor and normal cell
Metabolite Remark(C/N)
FBP 5.212(up)
G6P 2.42(up)
G3P 0.36(down)
Glucose down regulated
G3P Up regulated
Glucose down regulated
G3P up regulated
Analysis of regulation of metabolite concentration in the activity based model to that of data
extracted from literature [19], [20] showed that data integrated model follows the trend as per
the literature, though qualitatively. Flux variation in two conditions also supports the above
inference as the flux of individual steps in tumor cell has accelerated, which is in line with
the fact that glycolysis gets accelerated in cancer cells. Correlation value delineates that
normal cell model is mimicking the in vivo behaviour to quite a good level but not
completely.
Since, correlation values obtained from previous model (2c) and this model are very
similar, it reflects that variation in specific activity is due to change in gene-expression. To
see the other possible effect of variation of specific activity on pathway, we further
constructed integrated model having both variation of gene-expression and activity.
B. Integration of Both Activity and Gene-expression Data
Objective is same as the previous section but in this protocol the fold change is calculated by
assuming that both catalytic activity and gene-expression changes thus, fold change here is
the basically the multiplication of gene-expression and activity based fold changes
individually. Then, analysis of the accuracy of the integrated model by comparing the
correlation value to the previous results and the regulation of metabolites from liverome
database was done.

20
Simulation was performed using Libsbml ode solver up-to 100,000 time points.
Steady state analysis of normal cell (specific activity and gene-expression data integrated): -
cell: -
0
1
2
3
4
5
6
7
8
9
10
Concn.(mM)
Metabolites
Cancer Normal HMDB
R2-value = 0.76
-0.005
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
Flux(mM/min)
Enzymes
Flux Comparison
Cancer Normal

21
Results: -
Though we have integrated both specific activity and gene expression in combination but still
there is a mere improvement in the correlation value. We have also verified from the
literature [2] that catalytic activity of metabolic enzymes is generally a constant as the
mutation frequency is very low in these enzymes. Thus, this analysis confirmed that variation
of specific activity and gene expression data is same.

22
Remark
We used two approaches of data integration for making a normal cell from a cancer cell. In
first approach, we assumed that catalytic activity of an enzyme remains constant and in all the
conditions it’s only the concentration (expression) of the enzymes that creates the difference
between a normal and a tumor cell. In second approach, we integrated the specific activity of
the enzymes and observed that gene-expression (cell line level) and specific activity
integrated models were mimicking the in vivo behaviour in the same way suggesting that they
basically are the same.
Another important thing that this study shows is that the integration should be done on
the same spatial level in a biological system because there is a change in the behaviour of the
system as it gets more organised. Thus, integrating data from one level to another creates
erroneous models. Hence, integration of either gene-expression or specific activity of
enzymes involved in the pathway can capture the metabolic remodelling to approximately
70% which is enough for the rational characterization of the two conditions.

23
References: -
1. “Understanding the Warburg Effect: The Metabolic Requirements of Cell
Proliferation”, MatthewG. Vander Heiden, Lewis C. Cantley, and Craig B. Thompson
Science 22 May 2009: 324 (5930), 1029-1033. [DOI:10.1126/science.1160809]
2. Thompson, Craig B. “Metabolic Enzymes as Oncogenes or Tumor Suppressors.” The
New England journal of medicine 360.8 (2009): 813–815.PMC. Web. 30 June 2015
3. Alvaro Marín-Hernández, Juan Carlos Gallardo-Pérez, Sara Rodríguez-Enríquez,
RuselyEncalada, Rafael Moreno-Sánchez, Emma Saavedra, “Modeling cancer
glycolysis”, Biochimica et BiophysicaActa (BBA) - Bioenergetics, Volume 1807,
Issue 6, June 2011, Pages 755-767, ISSN 0005-2728
4. Alvaro Marín-Hernández, Sara Rodríguez-Enríquez, Paola A. Vital-González, Fanny
L. Flores-Rodríguez, Marina Macías-Silva, Marcela Sosa-Garrocho, Rafael Moreno-
Sánche, “Determining and understanding the control of glycolysis in fast-growth
tumor cells. Flux control by an over-expressed but strongly product-inhibited
hexokinase” FEBS J. 2006 May; 273(9): 1975–1988. doi: 10.1111/j.1742-
4658.2006.05214.x
5. Stavrum, Anne-Kristin et al. “Model of Tryptophan Metabolism, Readily Scalable
Using Tissue-Specific Gene Expression Data.” The Journal of Biological
Chemistry 288.48 (2013): 34555–34566. PMC. Web. 29 June 2015
6. Johann M. Rohwer et al., “Kinetic modelling of plant metabolic pathways”, J. Exp.
Bot. first published online March 14, 2012 doi:10.1093/jxb/ers080
7. Resat, H., Petzold, L., & Pettigrew, M. F. (2009). “Kinetic Modeling of Biological
Systems”, Methods in Molecular Biology (Clifton, N.J.), 541, 311–335.
doi:10.1007/978-1-59745-243-4_14
8. Jack Snoep, “Teaching Metabolic control analysis abd kinetic modelling”, The
Biochemist Society, February 1999
9. Kolesnikov N. et al., 2015. “ArrayExpress update-simplifying data
submissions.”,Nucleic Acids Res, doi:10.1093/nar/gku1057. Pubmed ID 25361974
10. http://www.genecards.org/
11. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall
KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson
CL, Serova N, Davis S, Soboleva A. “NCBI GEO: archive for functional genomics
data sets--update” Nucleic Acids Res. 2013 Jan;41(Database issue):D991-5.
12. Nick Juty et al., “BioModels: Content, Features, Functionality and Use”, CPT:
Pharmacometrics & Systems Pharmacology 2015
13. Barthelmes, Jens et al. “BRENDA, AMENDA and FRENDA: The Enzyme Information
System in 2007.” Nucleic Acids Research 35.Database issue (2007): D511–
D514. PMC. Web. 29 June 2015
14. “GEPASI: a software package for modelling the dynamics, steady states and control of
biochemical and other systems”, P. Mendes, Comput Appl Biosci. 1993 October; 9(5):
563–571
15. “CellDesigner: a process diagram editor for gene-regulatory and biochemical
networks” BIOSILICO, Vol. 1, No. 5. (05 November 2003), pp. 159-
162, doi:10.1016/s1478-5382(03)02370-9 by A. Funahashi, M. Morohashi, H. Kitano,
N. Tanimura

24
16. “Liverome: a curated database of liver cancer-related gene signatures with self-
contained context information”, BMC Genomics, Vol. 12, No. Suppl 3. (2011),
S3, doi:10.1186/1471-2164-12-s3-s3 by Langho Lee, Kai Wang, Gang Li, et al
17. Meier-Schellersheim, Martin, Iain D. C. Fraser, and Frederick Klauschen. “Multi-Scale
Modeling in Cell Biology.” Wiley interdisciplinary reviews. Systems biology and
medicine 1.1 (2009): 4–14. PMC. Web. 30 June 2015
18. Hart, Steven N. et al. “A Comparison of Whole Genome Gene Expression Profiles of
HepaRG Cells and HepG2 Cells to Primary Human Hepatocytes and Human Liver
Tissues” Drug Metabolism and Disposition 38.6 (2010): 988–994.PMC. Web. 29 June
2015
19. Qiang Huang, Yexiong Tan, Peiyuan Yin, Guozhu Ye, PengGao, Xin Lu, Hongyang
Wang, GuowangXu “Metabolic characterization of hepatocellular carcinoma using
nontargeted tissue metabolomics”, Cancer Res. 2013 August 15; 73(16): 4992–
5002. Published online 2013 July 1. doi: 10.1158/0008-5472.CAN-13-0308
20. Shu-Ye-Liu et al., “Human liver tissue metabolic profiling research on hepatitis B
virus-related hepatocellular carcinoma”, World J Gastroenterol. 2013 June 14; 19(22):
3423-3432, doi: 10.3748/wjg.v19.i22.3423

25
Appendix
Table 1: - Following reactions were taken into account while modelling glycolysis in cancer cell by Alvaro et al.
Table 2: - Glycolytic flux and intermediary concentration of metabolites in in-vivo and in-silico modelling for
AS-30D cells
Metabolites Concentration(mM)
5mM Glucose 1mM Glucose
In-vivo Model In-vivo Model
Glu_in 6.2±1 3.4 NM 0.8
G6P 5.3±2.6 6.5 2±0.5 3
F6P 1.5 ±0.7 0.03 0.7±0.2 0.016
FBP 25±7.6 5.2 0.6±0.3 0.36
DHAP 10±2.3 14 1±0.3 3
G3P 0.9±0.4 0.3 0.38 0.09
BPG ND 0.01 NM 0.002
3PG ND 0.01 NM 0.005
2PG ND 0.04 NM 0.016
PEP 0.1±0.02 0.003 NM 0.001
PYR 2.1±1 0.84 0.72 0.78
Lactate 27±11 Fixed NM Fixed
F2,6BP 6±1 Fixed NM Fixed
Citrate 1.7±0.7 Fixed NM Fixed
ATP 5.6±1.2 7.9 6 4.9
ADP 2.4±0.7 2.1 1.5 2.9
AMP 3.3±1.4 1.3 NM 3.9
Pi 4.8±1.9 Fixed 5 Fixed
NADH NM 0.005 NM 0.005
NAD 1.3±0.5 1.34 NM 1.34
Glycolytic Flux 21±9 29 10.5 14
Enzyme or branch Reaction
GLUT Glu_out = Glu_in
HK Glu_in + ATP = G6P + ADP
HPI G6P = F6P
PFK-1 F6P + ATP = FBP + ADP
ALDO FBP = DHAP + G3P
TPI DHAP = G3P
GAPDH NAD + G3P + Pi = 13BPG + NADH
PGK 1,3BPG + ADP = 3PG + ATP
PGAM 3PG = 2PG
ENO 2PG = PEP
PYK PEP + ADP = Pyr + ATP
LDH NADH + Pyr = Lac + NAD
Glycogen synthesis G6P + ATP -> glycogen + ADP + Pi + Pi
Glycogen degradation Glycogen + Pi -> G6P
ATPases ATP -> ADP + Pi
AK ATP + AMP = ADP + ADP
DHases NADH = NAD
PPP G6P -> 6PG
TK Xy5P + Ery4P -> G3P + F6P
MPM Pyr + 13ADP + 13Pi -> 13ATP

26
Table 3: - Glycolytic flux and intermediary concentration of metabolites in in vivo and in silico modelling for
HeLa cells
Metabolites Concentration(mM)
Normoxia Hypoxia
In vivo Model In vivo Model
Glu_in NM 0.61 NM 1.4
G6P 1.3±0.4 0.66 1.4±0.4 1
F6P 0.5±0.2 0.01 0.5±0.2 0.02
FBP 0.38 0.14 0.23 0.52
DHAP 0.93±0.07 2 0.54 3.6
G3P ND 0.008 NM 0.14
1,3BPG ND 0.0009 NM 0.001
3PG ND 0.006 NM 0.009
2PG ND 0.003 NM 0.004
PEP 0.32 0.002 NM 0.0003
Pyr 8.5±3.6 2.5 4.2 2.6
Lactate 33 Fixed NM Fixed
F2,6BP 4.2±0.8 Fixed NM Fixed
Citrate NM Fixed NM Fixed
ATP 8.7±3 8.4 7.9±4 7.7
ADP 2.7±1.3 2.2 1.8 2.1
AMP 0.4 1.3 NM 1.2
Pi 7.5 Fixed 7.8 Fixed
NADH NM 0.005 NM 0.005
NAD NM 1.34 NM 1.34
Glycolytic Flux 16±12 20 21±9 29
Table 4: - Kinetic parameters of AS-30D and HeLa glycolytic enzymes
Enzymes Parameter AS30D HeLa
GLUT KmGlu1 0.52 9.3
Keq1 1 1
Kmp 10 10
Vmf1 0.055 0.017
HK KmGlu2 0.18 0.1
KmATP1 0.99 1.1
KiG6P 0.02 0.02
KmADP1 3.5 3.5
Keq2 651 651
Vmf2 0.46 0.06
HPI KmG6P 0.9 0.4
KmF6P1 0.07 0.05
KiERY4 0.0017 0.001
KiFBP 0.17 0.6
Ki6PG 0.0094 0.015
Vmf3 4.9±1.9 1.2±0.2
Vmr1 3.4±1.1 2.8
PFK-1 KmF6P2 4.6 1
KmATP2 0.048 0.021
KiATP1 1.75 20
KiCIT 3.9 6.8
KaF26BP 1.8*10(-4) 8.4*10(-4)
b2 2.35 0.98
a2 4.47 0.32
L 13 4.1

27
Keq3 247 247
KmADP2 5 5
KmFBP2 5 5
Vmf4 0.273 0.078
ALDO KmFBP1 0.01 0.009
KmG3P1 0.16 0.16
KmDHAP1 0.08 0.08
Vmf5 0.23 0.2
Vmr2 0.18 NM
TPI KmDHAP2 1.9 1.6
KmG3P3 0.41 0.51
Vmf10 5.6 5
Vmr6 56 42
GAPDH KmG3P2 0.29 0.19
KmBPG1 0.02 0.022
KmNAD1 0.08 0.09
KmNADH1 0.004 0.01
KmPi 11 29
Vmf6 1 2
Vmr3 0.9 2.5
PGK KmBPG2 0.035 0.79
Km3PG1 0.12 0.13
KmADP3 0.67 0.04
KmATP3 0.15 0.27
a 1 1
b 1 1
Vmf7 27 13
Vmr4 4.3 3.8
PGAM Km3PG2 0.18 0.19
Km2PG1 0.04 0.12
Vmf11 20 1.4
Vmr7 1.3 0.53
ENO Km2PG2 0.16 0.038
KmPEP2 0.04 0.06
Vmf12 0.51 0.36
Vmr8 0.74 0.4
PYK KmPEP1 0.4 0.014
KmADP4 0.3 0.4
KmPYR2 10 10
KmATP4 0.86 0.86
Keq4 195172 195172
KaF16BP 4*10(-4) 4*10(-4)
KiATP2 2.5 2.5
L1 1 1
Vmf9 6.6 3
LDH KmPYR1 0.13 0.3
KmLAC 4.7 4.7
KmNADH2 0.002 0.002
KmNAD2 0.07 0.07
a1 1 1
b1 1 1
Vmf8 13.4 11.4
Vmr5 1.8 NM
Glycogen Degradation v5 1.2*10(-3) 6*10(-3)
Glycogen Synthesis v4 2.2*10(-3) 1.1*10(-3)
ATPases k1 4.2*10(-3) 0.003
AK k2 1 1
k3 2.26 2.26
DHases k4 250 250
k5 1 1
PPP v2 9.5*10(-5) 9.5*10(-5)

28
Table 5: - Steady-state metabolite concentrations in in vivo and in silico model of AS-30D cells
Metabolites In-vivo(mM) Model(mM)
Glu_in 6.2 3.4
G6P 5.3 6.5
F6P 1.5 0.03
FBP 25 5.2
DHAP 10 14
G3P 0.9 0.3
PEP 0.1 0.003
Pyr 2.1 0.84
ATP 5.6 7.9
ADP 2.4 2.1
AMP 3.3 1.3
NAD 1.3 1.34
Table 6: - Steady-state metabolite concentrations in in vivo and in silico model of HeLa cells
Metabolite In-vivo (mM) Model (mM)
G6P 1.3 0.66
F6P 0.5 0.01
FBP 0.38 0.14
DHAP 0.93 2
PEP 0.32 0.002
Pyr 8.5 2.5
ATP 8.7 8.4
ADP 2.7 2.2
AMP 0.4 1.3
Table 7: - Vmax values of pathway enzymes of AS30D and HeLa cells
Enzymes AS30D Hela
GLUT 0.055 0.017
HK 0.46 0.06
HPI 4.9 1.2
PFK 0.273 0.078
ALDO 0.23 0.2
TPI 5.6 5
GAPDH 1 2
PGK 27 13
PGAM 20 1.4
ENO 0.51 0.36
PYK 6.6 3
LDH 13.4 11.4
Unit of Vmax is U/ (mg of cytosolic protein).

29
Table 8: - In silico steady state metabolite concentration in AS30D and HeLa cells
Metabolites AS30D(mM) HeLa(mM)
Glu_in 3.4 0.61
G6P 6.5 0.66
F6P 0.03 0.01
FBP 5.2 0.14
DHAP 14 2
G3P 0.3 0.008
1,3BPG 0.01 0.0009
3PG 0.01 0.006
2PG 0.04 0.003
PEP 0.003 0.002
Pyr 0.84 2.5
ADP 2.1 2.2
AMP 1.3 1.3
NADH 0.005 0.005
NAD 1.34 1.34
Table 9: - In silico steady state flux of pathway enzymes in AS30D and HeLa cells
Enzyme AS30D Hela
GLUT 0.01587 0.009661
HK 0.01587 0.009661
HPI 0.014774 0.014466
PFK1 0.014869 0.014561
ALDO 0.014869 0.014561
TPI 0.014869 0.014561
GAPDH 0.029834 0.029218
PGK 0.029834 0.029218
PGAM 0.029834 0.029218
ENO 0.029834 0.029218
PYK 0.029834 0.029218
LDH 0.029334 0.029118
Glycogen_degradation 0.0012 0.006
Glycogen_synthesis 0.0022 0.0011

30
Table 10: - Gene expression data from ArrayExpress
Table 11: - Comparison of steady state metabolite concentration in a cancerous cell, data integrated normal cell
and normal human cell (HMDB).
Metabolite HMDB N_cell C_cell
D-Glucose 5.35 3.13 3.36
G6P 0.038 8.97 6.45
F6P 0.016 0.028 0.025
FBP 0.0076 7.43 5.25
DHAP 0.14 3.54 3.06
G3P 0.0067 2.39 1.48
13BPG 0.0004 0.33 0.22
3PG 0.045 0.28 0.274
2PG 0.014 0.34 0.3
PEP 0.017 0.003 0.0026
Pyr 0.218 0.8 0.835
Lactate 2.15 27 27
Citrate 0.114 1.7 1.7
Pi 0.379 2.5 2.5
ATP 1.54 8.97 7.91
ADP 0.27 1.65 2.11
NADH 0.022 0.005 0.0054
NAD 0.0887 1.34 1.344
AMP 0.051 0.68 1.275
Gene
Symbol T1 T1 T3 NT1 NT2 NT3 P-value Mean_C Mean_N FC
HK1 529.313 623.159 302 759.527 726.292 640.113 0.046359 529.313 726.292 0.728788
GPI 9128.2 12215.8 2173.18 3566.27 4818.1 6254.2 0.194792 9128.2 4818.1 1.894564
PFKL 1416.55 1944.96 962.687 1341.61 1415.13 1328.3 0.396821 1416.55 1341.61 1.055858
ALDOB 201.659 357.57 239.796 484.046 437.817 641.905 0.015168 239.796 484.046 0.495399
TPI1 31573.9 9493.87 11145.4 9266.85 9095.92 7628.15 0.143438 11145.4 9095.92 1.225319
GAPDH 75451.5 29247.4 40611 39007.5 42591.1 35995.2 0.273099 40611 39007.5 1.041107
PGK1 2985.44 2456.3 1043.99 1608.96 1837.49 1790.3 0.257521 2456.3 1790.3 1.372005
PGAM1 14918.2 6293.57 6230.84 7379.95 10303.9 6222.68 0.362827 6293.57 7379.95 0.852793
PGAM2 14.2499 4.40276 17.8622 37.2697 13.3455 8.39433 0.243016 14.2499 13.3455 1.067768
PGAM5 144.9984 29.14445 98.52555 36.038 51.38285 70.27534 0.168049 98.52555 51.38285 1.917479
ENO1 1069.88 4281.71 4366.77 1007.31 4270.77 3563.99 0.426076 4281.71 3563.99 1.201381
PKLR 296.274 4788.05 281.379 724.588 2033.5 1670.29 0.425026 296.274 1670.29 0.177379
LDHA 30573.9 23903.6 7195.82 34095.8 38331.3 40620.9 0.038207 23903.6 38331.3 0.623605
LDHD 269.421 503.779 619.887 1311.18 1145.41 1450.12 0.001741 503.779 1311.18 0.384218
AK3 1878.853 7776.89 6301.375 12394.36 12070 13495.41 0.007923 6301.375 12394.36 0.508407
TKT 1441.77 2215.22 7308.59 707.633 1284.14 1007.77 0.112037 2215.22 1007.77 2.19814

31
Table 12: - Kinetic parameters of enzymes of a normal cell derived from cancer cell
Table 13: - Concentrations of steady state pathway metabolites of tumor and different normal cells
Metabolite Cancer Cell HCV_N HBV1_N HBV3_N
Gluin 3.35682 3.428674 3.41274 3.410185
ATP 7.911383 7.576172 7.650473 7.662384
G6P 6.452338 10.54909 10.41647 10.39542
ADP 2.113087 2.234457 2.208582 2.20438
F6P 0.025517 0.024852 0.025031 0.025042
FBP 5.248007 5.552506 6.520523 6.084282
DHAP 3.064261 3.327128 3.45913 3.405487
G3P 1.4804 1.922377 2.198263 2.080854
NAD 1.34462 1.34462 1.34462 1.34462
13BPG 0.219089 0.188803 0.208598 0.195067
NADH 0.00538 0.00538 0.00538 0.00538
3PG 0.273927 0.260915 0.282254 0.262875
2PG 0.299798 0.243051 0.34718 0.250575
PEP 0.002556 0.002414 0.002445 0.00245
Pyr 0.835182 0.779936 0.778964 0.77907
Lac 27 27 27 27
Pi 2.5 2.5 2.5 2.5
AMP 1.27553 1.489371 1.440945 1.433235
Cit 1.7 1.7 1.7 1.7
Enzyme Parameters_C FC Type HBV_HCC FC HBV3_HCC FC HBV1_HCC
GAPDH Vmf 0.38 1.53 up 0.248 1.6 up 0.237 1.6 up 0.237
Vmr 0.34 1.53 up 0.222 1.6 up 0.212 1.6 up 0.212
ENO3 Vf 0.29 3.194 down 0.926 2.52 down 0.731 1.76 up 0.165
Vr 0.42 3.194 down 1.341 2.52 down 1.058 1.76 up 0.238
ALDOB Vmf 0.056 2.11 down 0.118 1.8 down 0.100 1.8 down 0.100
Vmr 0.044 2.11 down 0.092 1.8 down 0.079 1.8 down 0.079
HK3 Vm 0.35 1.562 down 0.531
GPI Vmf 1.42 1.72 up 0.825 1.67 up 0.850 1.67 up 0.085
Vmr 0.98 1.72 up 0.569 1.67 up 0.586 1.67 up 0.586
LDHD Vmf 2 1.81 down 3.62 1.86 down 3.72 1.86 down 3.72
Vmr 0.27 1.81 down 0.487 1.86 down 0.502 1.86 down 0.050

32
Table 14: - Steady state flux of intermediary pathway reactions in cancer and normal cells
Enzyme Cancer Cell HCV HBV1 HBV3
GLUT 0.01587 0.015166 0.015322 0.015348
HK 0.01587 0.015166 0.015322 0.015348
HPI 0.014774 0.01407 0.014226 0.014252
PFK1 0.014869 0.014165 0.014321 0.014347
ALDO 0.014869 0.014165 0.014321 0.014347
TPI 0.014869 0.014165 0.014321 0.014347
GAPDH 0.029834 0.028426 0.028738 0.028788
PGK 0.029834 0.028426 0.028738 0.028788
PGAM 0.029834 0.028426 0.028738 0.028788
ENO 0.029834 0.028426 0.028738 0.028788
PYK 0.029834 0.028426 0.028738 0.028788
LDH 0.029334 0.027926 0.028238 0.028288
Glycogen_degradation 0.0012 0.0012 0.0012 0.0012
ATPases 0.033228 0.03182 0.032132 0.032182
AK 2E-09 2.78E-07 -2.6E-10 -1.5E-08
DHases 0.0005 0.0005 0.0005 0.0005
PPP 0.000096 0.000096 0.000096 0.000096
Glycogen_synthesis 0.0022 0.0022 0.0022 0.0022
MPM 0.0005 0.0005 0.0005 0.0005
TK 0.000095 0.000095 0.000095 0.000095
Table 15: - Concentrations of steady state pathway metabolites of tumor and different normal cell
Note: - HBV1 and HBV3 denotes cancer cell with ENO1 and ENO3 as differentially expressed gene,
respectively and unit of concn. ismM and that of flux is mM/min.
Metabolite Cancer Cell HCV_N HBV1_N HBV3_N
Gluin 3.35682 3.428674 3.41274 3.410185
ATP 7.911383 7.576172 7.650473 7.662384
G6P 6.452338 10.54909 10.41647 10.39542
ADP 2.113087 2.234457 2.208582 2.20438
F6P 0.025517 0.024852 0.025031 0.025042
FBP 5.248007 5.552506 6.520523 6.084282
DHAP 3.064261 3.327128 3.45913 3.405487
G3P 1.4804 1.922377 2.198263 2.080854
NAD 1.34462 1.34462 1.34462 1.34462
13BPG 0.219089 0.188803 0.208598 0.195067
NADH 0.00538 0.00538 0.00538 0.00538
3PG 0.273927 0.260915 0.282254 0.262875
2PG 0.299798 0.243051 0.34718 0.250575
PEP 0.002556 0.002414 0.002445 0.00245
Pyr 0.835182 0.779936 0.778964 0.77907
Lac 27 27 27 27
Pi 2.5 2.5 2.5 2.5
AMP 1.27553 1.489371 1.440945 1.433235
Cit 1.7 1.7 1.7 1.7

33
Table 16: - Calculation of kinetic parameters of enzymes for normal cell from cancer using FC (determined by
ratio of activity of enzymes in two condition
Table 17: - Steady state metabolite concentrations in tumor cell and data integrated normal cell
Enzyme FC Parameter Value_C Value_N
HK 153 Vm 0.35 0.002288
HPI 4 Vmf 1.42 0.355
Vmr 0.98 0.245
PFK-1 22 Vm 0.066 0.003
ALDO 2.7 Vmf 0.056 0.020741
Vmr 0.044 0.016296
TPI 3.6 Vf 1.39 0.386111
Vr 13.9 3.861111
GAPDH 2 Vmf 0.38 0.19
Vmr 0.34 0.17
PGK 3.3 Vmf 10.8 3.272727
Vmr 1.72 0.521212
PGAM 2.3 Vf 8 3.478261
Vr 0.52 0.226087
ENOLASE 4.3 Vf 0.29 0.067442
Vr 0.42 0.097674
PYK 8.1 Vm 4.2 0.518519
LDH 1.5 Vmf 2 1.333333
Vmr 0.27 0.18
Metabolite Cancer Cell Normal Cell
Gluin 3.35682 4.894008
ATP 7.911383 0.834781
G6P 6.452338 -0.12396
ADP 2.113087 1.790072
F6P 0.025517 0.001504
FBP 5.248007 0.005906
DHAP 3.064261 0.314595
G3P 1.4804 0.030378
NAD 1.34462 1.34462
_13BPG 0.219089 0.008141
NADH 0.00538 0.00538
_3PG 0.273927 0.084273
_2PG 0.299798 0.017876
PEP 0.002556 7.92E-05
Pyr 0.835182 0.719426
Lac 27 27
glycogen 26 26
Pi 2.5 2.5
AMP 1.27553 8.675146
_6PG 0.35 0.35
Xy5P 0.54 0.54
Ery4P 1 1
Cit 1.7 1.7
F26BP 0.006 0.006

34
Table 18: - Steady state flux of intermediary reactions of pathway in normal cell and cancer ce
Table 19- List of differentially expressed genes, their fold changes and conversion of kinetic parameters from
cancer cell to normal cell (cell line gene-expression data)
Enzyme Cancer Normal
GLUT 0.01587 0.00101
HK 0.01587 0.00101
HPI 0.014774 -8.6E-05
PFK1 0.014869 8.54E-06
ALDO 0.014869 8.54E-06
TPI 0.014869 8.54E-06
GAPDH 0.029834 0.000112
PGK 0.029834 0.000112
PGAM 0.029834 0.000112
ENO 0.029834 0.000112
PYK 0.029834 0.000112
LDH 0.029334 -0.00039
ATPases 0.033228 0.003506
AK 2E-09 -9.3E-12
DHases 0.0005 0.0005
PPP 0.000096 0.000096
MPM 0.0005 0.0005
TK 0.000095 0.000095
Enzyme Parameter Cancer cell FC Normal cell
HK1 Vm 0.35 6.23 0.05618
PFKL Vm 0.066 1.434 0.046025
ALDOB Vmf 0.056 0.0024 23.33333
Vmr 0.044 0.0024 18.33333
TPI Vf 1.39 1.3 1.069231
Vr 13.9 1.3 10.69231
GAPDH Vmf 0.38 2.464 0.154221
Vmr 0.34 2.464 0.137987
PGK1 Vmf 10.8 2.023 5.338606
Vmr 1.72 2.023 0.850222
PGAM Vf 8 1.222 6.546645
Vr 0.52 1.222 0.425532
ENO3 Vf 0.29 0.3 0.966667
Vr 0.42 0.3 1.4
PKLR Vm 4.2 1.834 2.290076
LDHA Vmf 2 1.444 1.385042
Vmr 0.27 1.444 0.186981

35
Table 20- List of fold change and parameters of normal cell (activity and gene expression)
Enzyme Parameter Cancer cell
FC(activity
based)
FC(gene-
expression
based) FC(total) Normal cell
HK1 Vm_2 0.35 153 6.23 953.19 0.003182
PFKL Vm_4 0.066 22 1.434 31.548 0.002092
ALDOB Vmf_5 0.056 2.7 0.0024 0.00648 8.641975
Vmr_5 0.044 2.7 0.0024 0.00648 6.790123
TPI Vf_6 1.39 3.6 1.3 4.68 0.297009
Vr_6 13.9 3.6 1.3 4.68 2.970085
GAPDH Vmf_7 0.38 2 2.464 4.928 0.07711
Vmr_7 0.34 2 2.464 4.928 0.068994
PGK1 Vmf_8 10.8 3.3 2.023 6.6759 1.617759
Vmr_8 1.72 3.3 2.023 6.6759 0.257643
PGAM Vf_9 8 2.3 1.222 2.8106 2.846367
Vr_9 0.52 2.3 1.222 2.8106 0.185014
ENO3 Vf_10 0.29 4.3 0.3 1.29 0.224806
Vr_10 0.42 4.3 0.3 1.29 0.325581
PKLR Vm_11 4.2 8.1 1.834 14.8554 0.282725
LDHA Vmf_12 2 1.5 1.444 2.166 0.923361
Vmr_12 0.27 1.5 1.444 2.166 0.124654
Table -21 Steady-state metabolite concentration in cancer cell and data integrated normal cell (gene-expression
at cell line level)
Metabolites Cancer Normal HMDB
Gluin 3.35682 4.343296 5.35
ATP 7.911383 3.347404 1.54
G6P 6.452338 2.305708 0.038
ADP 2.113087 2.770472 0.27
F6P 0.025517 0.025332 0.016
FBP 5.248007 0.545682 0.0076
DHAP 3.064261 1.908318 0.14
G3P 1.4804 0.465562 0.0067
NAD 1.34462 1.34462 0.0887
_13BPG 0.219089 0.061263 0.0004
NADH 0.00538 0.00538 0.022
_3PG 0.273927 0.238917 0.045
_2PG 0.299798 0.179321 0.014
PEP 0.002556 0.001608 0.017
Pyr 0.835182 0.468102 0.218
AMP 1.27553 5.182124 0.051

36
Table- 22 Steady state flux of intermediary reactions of pathway in data integrated normal cell and cancer cell
(gene-expression at cell line level)
Enzymes Cancer Cell Normal cell
GLUT 0.01587 0.006286
HK 0.01587 0.006286
HPI 0.014774 0.00519
PFK1 0.014869 0.005285
ALDO 0.014869 0.005285
TPI 0.014869 0.005285
GAPDH 0.029834 0.010665
PGK 0.029834 0.010665
PGAM 0.029834 0.010665
ENO 0.029834 0.010665
PYK 0.029834 0.010665
LDH 0.029334 0.010165
ATPases 0.033228 0.014059
AK 2E-09 6.32E-10
DHases 0.0005 0.0005
PPP 0.000096 0.000096
MPM 0.0005 0.0005
TK 0.000095 0.000095
Table -23 Steady-state metabolite concentration in cancer cell and data integrated normal cell (activity and gene-
expression data combination)
Metabolites Cancer Normal HMDB
Gluin 3.35682 4.89028 5.35
ATP 7.911383 0.851707 1.54
G6P 6.452338 0.039045 0.038
ADP 2.113087 1.804824 0.27
F6P 0.025517 0.013519 0.016
FBP 5.248007 0.015591 0.0076
DHAP 3.064261 0.485596 0.14
G3P 1.4804 0.052306 0.0067
NAD 1.34462 1.34462 0.0887
_13BPG 0.219089 0.01342 0.0004
NADH 0.00538 0.00538 0.022
_3PG 0.273927 0.136791 0.045
_2PG 0.299798 0.04299 0.014
PEP 0.002556 0.000237 0.017
Pyr 0.835182 0.719507 0.218
AMP 1.27553 8.643469 0.051

37
Table-24 Steady state flux of intermediary reactions of pathway in data integrated normal cell and cancer cell
(activity and gene-expression combination)
Enzymes Cancer Normal
GLUT 0.01587 0.001045
HK 0.01587 0.001045
HPI 0.014774 -5.1E-05
PFK1 0.014869 4.41E-05
ALDO 0.014869 4.41E-05
TPI 0.014869 4.41E-05
GAPDH 0.029834 0.000183
PGK 0.029834 0.000183
PGAM 0.029834 0.000183
ENO 0.029834 0.000183
PYK 0.029834 0.000183
LDH 0.029334 -0.00032
ATPases 0.033228 0.003577
AK 2E-09 -1.5E-14
DHases 0.0005 0.0005
PPP 0.000096 0.000096
MPM 0.0005 0.0005
TK 0.000095 0.000095

Intern Report

Recommended

Recommended

More Related Content

Similar to Intern Report

Similar to Intern Report (20)

Intern Report