computational modelling of biological pathways

Computational Modelling

of Biological Pathways

Kumar Selvarajoo

[email protected]

Outline

• Background of Research

• Methodology

• Discovery of Cell-type Specific Pathways

• Analysis of Complex Metabolic Diseases

The levels in Biology

DNA

RNA

Protein

Cell

Tissue

Organ

Organism

transcription

translation

The Central

Dogma of Molecular Biology

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?

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

????

• 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

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)

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

• ‘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

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)

‘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)

Modules in Metabolic Networks

We chose the glycolytic module

Our Methodology

Knowing the true system

Systems Approach

A

k

( , , )BS fn A C X

B C X

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

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

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

Constraints

• Constraints are introduced to increase the coefficient confidence

• Examples

- lead coefficient

- rate coefficient

- frequency coefficient

Lead coefficient constraint, 0< kf<1

E1

A B

Constraints

)1( tkfB

bekS

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

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

Glycolytic Network and Measured Values for Erythrocytes (RBC)

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

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%

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



Co

ncen

trati

on

(m

M)

sim

-20%

20%

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

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)

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



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

Discovery of Cell-type Specific Pathways

Using Computational Simulations

Trypanosoma Brucei (T.brucei)

• is a parasite• causes the African Sleeping

Disease or Trypanosomiasis • carried by Tsetse fly

Prediction of T.brucei Glycolytic Phenotype (Aerobic Condition)

0

5

10

15

20

25



Con

cent

ratio

n (m

M)

sim

exp


Model: Reference (RBC)Test: TbruceiIM: 1.64mM

0

5

10

15

20

25



Co

nce

ntr

atio

n (

mM

)

sim

exp


Model: Reference with modfication at DHAP, G3P & BPGTest: TbruceiIM: 1.64mM

Prediction of T.brucei Glycolytic Phenotype under Aerobic Condition

0

5

10

15

20

25

30



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

Optimising model for Cell-Specificity,

T.brucei

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


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)

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


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

Aerobic Condition T.brucei

computational modelling of biological pathways

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