This document discusses using network analysis and mass flow graphs to analyze cancer cell metabolism. It assesses different published genome-scale metabolic models of cancer and determines that PRIME models are best suited for applying mass flow graph analysis. Constraint-based analysis is performed on PRIME models to simulate metabolic conditions and genetic perturbations. Centrality analysis using PageRank reveals changes in network structure under different conditions but does not fully support the centrality-lethality hypothesis regarding essential reactions. Future work is needed to better integrate omics data and identify centrality measures that correlate with biological importance.

1. Towards personalised cancer
medicine: Network analysis to
track the metabolic footprints of
cancer
- By VARSHIT DUSAD
Project Supervisors:
Dr. Diego Oyarzún
Dr. Hector Keun
Dr. Mauricio Barahona

3. Flux Balance Analysis
 Most common technique to
analyze metabolic networks.
 Provides steady state flux
distribution
 Needs constraints and objective
function for optimization!
 Biomass growth reaction
 ATP maintenance

4. Substrate Graph Reaction Graph Bipartite Graph
3 most common graph representations of a metabolic network
Imagine a network of 4 reactions comprising of metabolites A, B, C, D, E. F and G such that R1: A+B→C; R2: C+D→E; R3:
A→F; R4: A→G
Metabolite graph: Where metabolites are nodes and reactions are edges (left);
Reaction graph: Where reactions are nodes and metabolites are edges (right), and
Bipartite graph: Where both nodes and reactions are nodes and connected by edges.

5. Constraint
Based
Analysis
Graph
Theory
Mass
Flow
Graph
Metabolic
Networks

6. Mass flow graphs

7. Why Mass Flow Graphs?
Weighted and
Directed graphs.
1
Reduces over
contribution of
pool metabolites
2
Incorporates
context specific
information
3

8. CHALLENGES
GOALS

9. Many cancer models with varying quality
Assess and rank models best suited for MFG
Right modeling also needs right constraints
Cross-validate published results
How does MFG capture cancer metabolism?
Analyze MFGs in various genetic knockouts

10. Objectives
 Critical assessment of different cancer metabolic models to identifying best
set of models suited for applying MFG.
 To cross-validate the selected model’s behavior with published results.
 To characterize the metabolic properties of mass flow graphs for cell line
specific cancer metabolic models
 a. Constructing MFGs in diverse genetic and environmental conditions.
 b. Centrality analysis using PageRank and characterizing differences
between different MFGs.
 c. Evaluation of gene essentiality and synthetic lethality in the context
of MFGs.

11. Results

12. Models Publication Algorithm What’s different
mCADRE Wang et al, 2012 mCADRE 126 tissue specific
models including some
cancer models
CL Ghaffari et al, 2015 tINIT 11 models corresponding
to 11 different cancer
cell lines (CL)
INIT Agren et al, 2012 INIT 16 tissue specific cancer
models
Nam Nam et al, 2013 GIMME 8 tissue specific cancer
models + their
corresponding wild type
PRIME Yizhak et al, 2014 PRIME 60 models corresponding
to 60 NCI-60 cancer cell
lines
Critical Assessment of 5 group of Cancer GEMs

13. Critical Assessment of
Cancer GEMs
 Not absolute best or
worst but how valuable
are they as “off the
shelf”.
 Checked for total
number of reactions and
metabolites.
 Nam > CL > PRIME > INIT
> mCADRE

14. Critical Assessment
of Cancer GEMs
 4 group of models
compared*.
 Checked for total
number of subsystems.
 CL > Nam ≥ PRIME >
mCADRE
*INIT models lacked subsystem information

15. Critical Assessment
of Cancer GEMs
 All 5 group of models
compared.
 Checked for “Active
reactions” and “Active
metabolites”.
 mCADRE > PRIME ≥
Nam > INIT > CL

16. Critical Assessment
of Cancer GEMs
 3 group of models from
compared*.
 Checked for
Mass/Charge balance.
 Only artificial reactions
unbalanced as expected.
*Nam, CL, INIT lacked information for
checking mass and charge balance.
Additionally Recon1 has been added
for control.

17. Critical Assessment of
Cancer GEMs
 5 group of models from
different publications
compared.
 Checked for
biologically reasonable
simulation via bounds.
 PRIME > Nam >
mCADRE ≥ INIT ≥ CL

18. Qualitative model comparison
Biomass function Objective
function
Experimental
Confirmation
mCADRE NA NA NA
INIT NA NA NA
CL Exists
(Unconventional)
Exists
(Unconventional)
Yes - 1
Nam Exists Exists Literature
confirmation
PRIME Exists Exists Yes - 2

20. PRIME vs Nam
 Based on previous
results it appears that
Nam and PRIME models
are most useful
 Tested for known
metabolic properties:
Comparison with growth
rate
 PRIME
 Nam
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.12 0.13 0.14 0.15 0.16 0.17 0.18 0.19 0.2
Experimental(1/hr)
Prime models (mmol/gDw-hr)
Prime vs Experimental
R=0.69
0.084
0.085
0.086
0.087
0.088
0.089
0.09
0.091
0.092
0.093
0.022 0.024 0.026 0.028 0.03 0.032 0.034 0.036 0.038 0.04
Experimental(1/hr)
Nam models (mmol/gDw-hr)
Nam vs Experimental
R= - 0.90

21. PRIME vs Nam
 Based on previous
results it appears that
Nam and PRIME models
are most useful
 Tested for known
metabolic properties:
Simulating drug response
 PRIME
 Nam
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Biomassflux
Drugs
Drug simulation: PRIME models
786-0 A498 A549 ACHN BT-549
0
0.02
0.04
0.06
0.08
0.1
Biomassflux
Drugs
Drug simulation: Nam models
breast_cancer kidney_RCC__cancer
liver_HCC_cancer lung_SCC_cancer
lung_adenocarcinoma_cancer

22. Warburg Effect
 Cross-validating
published results:
Warburg effect.
 Below a certain oxygen
uptake minimum amount
of Lactate secreted will
be >0.
 Cell line: BT-549
 Oxygen threshold:
between -6 and -5.5
mmol●gDW-1●hr-1

23. Warburg Effect
 Cross-validating
published results:
Warburg effect.
 Below a certain oxygen
uptake minimum amount
of Lactate secreted will
be >0.
 All cell lines
 Oxygen threshold:
between variable for
each cell line.

24. Constraint Based analysis of cancer GEMs
 Based upon previous results, it was determined that PRIME models are best
candidates for reliable, as well as flexible flux simulations.
 However, flux simulation without proper constraints can still provide
biologically inaccurate results. Therefore, all the further simulations were
carried out in condition where the “Warburg effect” hypothesis was
confirmed.
 Standard: D-Glucose: -5, L-Glutamine: -1, D-Lactate release: 0.005
 noDLactate Constraint: D-Glucose: -5, L-Glutamine: -1
 Methotrexate: Standard + Dihydrofolate reductase knockout
 SDH Knockout: Standard + Succinate Dehydrogenase knockout.
 FH Knockout: Standard + Fumarate Hydratase knockout.
 Experimental measured fluxes as constraints (Jain et al, 2012)
 Oxygen level was variable for each cell line as determined in previous
results.

25. Pagerank Dependence on Flux
 Pagerank used to measure
importance of nodes!
 Absolute Pagerank will
depend upon flux to a very
high degree.
 Cell Line: A498

26. Pagerank Percentile
comparison.
 Cell Line: A498
 Pagerank percentile of reactions
changes in different conditions.
 Most reaction having shift in
pagerank have intermediate
pagerank percentile.

27. Pagerank Percentile
comparison
 Pagerank percentile of
reactions changes in
different conditions.
 Most reaction having
shift in pagerank have
intermediate pagerank
percentile.

28. Essentiality and Synthetic
Lethality
 Essential reactions: A is
essential if its knockout kills
cell.
 Synthetic lethal: A and B are
synthetically lethal if and
only if double knockout of A
and B kills the cell.
 Essentiality remained
constant with environment.
 Synthetic lethality changes
with changing environment.
 CellLine: A498
96 99 96
135
140
204
0
50
100
150
200
250
Default Artificial Real Data
Chart Title
Essential Synthetic Lethal

29. Centrality Lethality
Hypothesis (CLH)
 CLH states that in a
biological network,
nodes with high
centrality should be
essential.
 Cell Line: A498
 Pagerank percentile of
most essential
reactions is not high
 Similar results for
synthetic lethality
(Data not shown)

30. Conclusions
Genome scale models of
Cancer
1. Many cancer models exist,
with varying scope and utility.
2. Applying right constraints is
a must to achieve biologically
relevant results.
3. PRIME models were the best
fit to apply MFG.
Network Analysis of Mass
flow graphs
1. PageRank can provide more
information than flux alone.
2. PageRank captures changes
in network due to metabolic
perturbation.
3. PageRank and MFGs do not
support “Centrality Lethality
Hypothesis”.

31. • Identifying a centrality measure which
is similar to biological importance.
• Identifying the network property
which explains the basis of
essentiality.
• Integrating omics data sets to map
better constraints and even form new,
better models.
Future works