Computational Modelling

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• Computational Modelling
• of Biological Pathways

Kumar Selvarajoo kumars_at_bii.a-star.edu.sg
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Outline

• Background of Research
• Methodology
• Discovery of Cell-type Specific Pathways
• Analysis of Complex Metabolic Diseases

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The levels in Biology
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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From Genome to Cellular Phenotype
Eg. Human
Eg. ESR Coding
Eg. Glycolysis
Eg. Cancer, Diabetes
The steps involved to convert genome sequence
into useful phenotypic description
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From Genome to Cellular Phenotype

• 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

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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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Simulation Techniques

• 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

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Problems with Bottom-Up Approaches

• 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
Metabolic Network

• 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 studies

Proteins
mRNA
Genomic Sequence
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
k
A
B
Systems Approach
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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
E1
A
B
Assume
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Our Methodology
In a typical enzymatic reaction (non ideal),
physical constraints exist that prevent complete
depletion of substrate. Therefore,
where kf is the fitting parameter and 0lt kflt1
(Constraint)
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Our Methodology
For feedback/feedforward mechanisms k2 could be a
function of the upstream/downstream substrate
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Constraints

• Constraints are introduced to increase the
  coefficient confidence
• Examples
• - lead coefficient
• - rate coefficient
• - frequency coefficient

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Constraints
Lead coefficient constraint, 0lt kflt1
E1
A
B
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Constraints

• Rate coefficient constraint, 0.1ltkblt1.0

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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
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
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Robustness of Model Parameters/- 20 Variation
in All Model Parameters
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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
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Prediction of Myocyte Glycolytic Phenotype
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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)
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Prediction of T.brucei Glycolytic Phenotype
under Aerobic Condition
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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
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Prediction of T.brucei Glycolytic Phenotype under
Anaerobic Condition
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T.brucei
Aerobic Condition