Computational Modelling of Biological Pathways Kumar Selvarajoo [email protected].
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Transcript of Computational Modelling of Biological Pathways Kumar Selvarajoo [email protected].
![Page 2: Computational Modelling of Biological Pathways Kumar Selvarajoo kumars@bii.a-star.edu.sg.](https://reader035.fdocuments.in/reader035/viewer/2022062714/56649d125503460f949e5a63/html5/thumbnails/2.jpg)
Outline
• Background of Research
• Methodology
• Discovery of Cell-type Specific Pathways
• Analysis of Complex Metabolic Diseases
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The levels in Biology
DNA
RNA
Protein
Cell
Tissue
Organ
Organism
transcription
translation
The Central
Dogma of Molecular Biology
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Is Genome Sequence Enough?
• The genome sequence contains the information for living systems propagation
• The functioning of living system involves many complex molecular interactions within the cell
• How do we understand these complex interactions with static sequence information?
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Eg. Human Eg. ESR Coding
Eg. Glycolysis Eg. Cancer, Diabetes
The steps involved to convert genome sequence into useful phenotypic description
From Genome to Cellular Phenotype
Genome Sequence
Gene/Protein Function
Cellular Networks
TissuePhenotype
Successful Sequence Analysis
Functional Mapping
????
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• Understanding the individual function of genes, proteins or metabolites does not allow us to understand biological systems behaviour
• It is therefore important to know how each gene, protein or metabolite is connected to each other and how they are regulated over time
• Recent technological breakthroughs in biology has made generating high throughput experimental data a reality
• But by analysing high throughput experimental data of biological systems without understanding the underlying mechanism or circuitry is not very useful
From Genome to Cellular Phenotype
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Computation in Biology
• Computational methods hence become essential to help understand the complexity of biological systems (Hartwell et al, Nature,1999)
• However, the currently available computational techniques are insufficient to accurately model complex biological networks (Baily, Nature Biotechnology, 2001)
• This is mainly due to the general lack of formalised theory in biology at present.
• Biology is yet to see its Newton or Kepler (Baily, Nature Biotechnology, 2001)
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Advantages: Computer Simulations
• Easy to mathematically conceptualise
• Able to develop and predict highly complex processes
• Rapid creation and testing of new hypotheses
• Serves to guide wet-bench experimentation
• Potential cost reductions with accelerated research
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• ‘Bottom-Up’– Predominant in biology (e.g. Enzyme Kinetics)– Deliberately COMPREHENSIVE (include everything)– Need lots of experimentally determined parameters– Very long process– Very expensive
• ‘Top-Down’ or ‘Phenomic’– Common in engineering– Deliberate use of APPROXIMATIONS (reduce complexity)
successful in engineering (e.g. Finite Element Analysis)– Very fast– Inexpensive
Simulation Techniques
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Problems with ‘Bottom-Up’ Approaches
Genomic Sequence
mRNA
Metabolic Network
Proteins
• The correlation between mRNA levels and protein expression levels are very poor
• Protein post-translational modifications cannot be predicted from the genome sequence
• The kinetic parameters used to determine the rate of protein activity is very difficult to determine
• In vitro determination of kinetic parameters fail to capture the robustness of biological systems found in vivo
• Even if all parameters are determined, the model is not versatile or scalable, that is, usually only applied to one cell-type at one specific condition (e.g. muscle cells at aerobic condition)
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‘Top-Down’ Approach
• Attempt to develop a network module*, hence cannot be comprehensive
• First look at a well known network and try to understand the topology through phenotypic observation
• Formulate the interactions within the network with guessing parameters for protein activity
• Check with experiments once parameters are fixed
• Perform perturbation experiments to confirm the hypothesis
• Useful for drug perturbation studiesGenomic Sequence
mRNA
Metabolic Network
Proteins
*A functional module is, by definition, a discrete entity whose function is separable from those of other modules.(Hartwell et al, 1999, Nature)
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Modules in Metabolic Networks
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We chose the glycolytic module
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Our Methodology
Knowing the true system
Systems Approach
A
k
( , , )BS fn A C X
B C X
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Our Methodology
Consider a simple (ideal) reaction, one mole of substrate A converted to one mole of product B by the enzyme E1
ktA eS
ktB eS 1
Assume
E1
A B
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In a typical enzymatic reaction (non ideal), physical constraints exist that prevent complete depletion of substrate. Therefore,
where kf is the fitting parameter and 0< kf<1 (Constraint)
Our Methodology
)1( tkfB
bekS
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For feedback/feedforward mechanisms k2 could be a function of the upstream/downstream substrate
A B X
k2
Our Methodology
Atktk
xB SkeeSktkS 543
21 )1)((sin
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Constraints
• Constraints are introduced to increase the coefficient confidence
• Examples
- lead coefficient
- rate coefficient
- frequency coefficient
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Lead coefficient constraint, 0< kf<1
E1
A B
Constraints
)1( tkfB
bekS
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Rate coefficient constraint, 0.1<kb<1.0
0
0.2
0.4
0.6
0.8
1
1.2
0 1 2 3 4 5 6 7 8 9 10
Time (s)
Co
ncen
trati
on
(m
M)
kb=1.0(max)
kb=0.1(min)increasingkb
Constraints
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Features of Our Methodology
• Fewer parameters required
• Able to construct complex networks
• Able to produce accurate predictions even under reduced complexity
• Uses and predicts metabolite concentrations, rather than enzyme activity
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Glycolytic Network and Measured Values for Erythrocytes (RBC)
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Comparison between Measured and Predicted Values in RBC
A B A/B
Measured* (mM) Predicted (mM) (-)
G6P 0.039 0.0390 1.00
F6P 0.013 0.0129 1.01
FBP 0.0027 0.0027 1.00
DHAP 0.14 0.1400 1.00
G3P 0.0057 0.0058 0.98
BPG 0.0007 0.0007 1.00
3PG 0.069 0.0705 0.98
2PG 0.01 0.0106 0.94
PEP 0.017 0.0180 0.94
PYR 0.085 0.0881 0.96
Metabolites
*
*Model of 2,3-biphosphoglycerate metabolism in the human erythrocyte Biochem. J. 342 (1999), Mulquiney & Kuchel
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Robustness of Model Parameters+/- 20% Variation in Input G6P Values
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
G6P F6P FBP DHAP G3P BPG 3PG 2PG PEP PYR
Glycolytic Metabolites
Co
nc
en
tra
tio
n (
mM
)
sim
-20%
20%
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Robustness of Model Parameters+/- 20% Variation in All Model Parameters
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
G6P F6P FBP DHAP G3P BPG 3PG 2PG PEP PYR
Glycolytic Metabolites
Co
ncen
trati
on
(m
M)
sim
-20%
20%
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Model Application
• Model applied to other cell types and conditions
• These are predictions - No experimental data from the ‘test’ cell type is used (unless stated otherwise)
• Model parameters are fixed unless stated otherwise
• Points of accurate prediction represented by green, otherwise indicated as red
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Metabolic Phenotypes of Erythrocytes and Myocytes are Highly Distinct
-1
-0.5
0
0.5
1
1.5
2
2.5
3
G6P F6P FBP G3P 3PG 2PG PEP PYR
ln(Ratios)
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Prediction of Myocyte Glycolytic Phenotype
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
G6P F6P FBP DHAP G3P BPG 3PG 2PG PEP PYR
Glycolytic Metabolites
Co
nce
ntr
atio
n (
mM
)
sim
exp
* data not available
* *
G6P(IM) F6P FBP G3P BPG 3PG 2PG PEP PYRDHAP
Model: Reference (RBC)Test: MyocytesIM: 0.45mM
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Discovery of Cell-type Specific Pathways
Using Computational Simulations
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Trypanosoma Brucei (T.brucei)
• is a parasite• causes the African Sleeping
Disease or Trypanosomiasis • carried by Tsetse fly
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Prediction of T.brucei Glycolytic Phenotype (Aerobic Condition)
0
5
10
15
20
25
G6P F6P FBP DHAP G3P BPG 3PG 2PG PEP PYR
Glycolytic Metabolites
Con
cent
ratio
n (m
M)
sim
exp
G6P(IM) F6P FBP G3P BPG 3PG 2PG PEP PYRDHAP
Model: Reference (RBC)Test: TbruceiIM: 1.64mM
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0
5
10
15
20
25
G6P F6P FBP DHAP G3P BPG 3PG 2PG PEP PYR
Glycolytic Metabolites
Co
nce
ntr
atio
n (
mM
)
sim
exp
G6P(IM) F6P FBP G3P BPG 3PG 2PG PEP PYRDHAP
Model: Reference with modfication at DHAP, G3P & BPGTest: TbruceiIM: 1.64mM
Prediction of T.brucei Glycolytic Phenotype under Aerobic Condition
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0
5
10
15
20
25
30
G6P F6P FBP DHAP G3P BPG 3PG 2PG PEP PYR
Glycolytic Metabolites
Co
nc
en
tra
tio
n (
mM
)
sim
exp
lit
FBP
Comparison of Predicted T.brucei Glycolytic Phenotype Against a Literature Model*
*Glycolysis in Bloodstream Form Trypansoma brucei J. Bio. Chem, 342 (1997), Bakker B. M. et al
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Optimising model for Cell-Specificity,
T.brucei
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Prediction of T.brucei Glycolytic Phenotype after Optimisation, Aerobic Condition
0
5
10
15
20
25
G6P F6P FBP DHAP G3P BPG 3PG 2PG PEP PYR GLY3P
Glycolytic Metabolites
Co
nce
ntr
atio
n (
mM
)
sim
exp
G6P(IM) F6P FBP G3P BPG 3PG 2PG PEP PYRDHAP GLY3
Model: Reference (Tbrucei, Aerobic)Test: Tbrucei (Aerobic)
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Prediction of T.brucei Glycolytic Phenotype under Anaerobic Condition
0
5
10
15
20
25
G6P F6P FBP DHAP G3P BPG 3PG 2PG PEP PYR GLY3P
Glycolytic Metabolites
Co
nce
ntr
atio
n (
mM
)
sim
exp
FBP PYRG6P(IM) F6P G3P BPG 3PG 2PG PEPDHA GLY3
Model: Reference (Tbrucei, Aerobic)Test: Tbrucei (Anaerobic)
* *
* data not available
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Aerobic Condition T.brucei