Computational Modelling of Biological Pathways

1. Computational Modelling of Biological Pathways Kumar Selvarajoo kumars@bii.a-star.edu.sg

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

3. DNA Organism transcription RNA Organ translation Protein Tissue Cell The levels in Biology The Central Dogma of Molecular Biology

4. 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?

5. Successful Sequence Analysis Functional Mapping ???? Genome Sequence Gene/Protein Function Cellular Networks Tissue Phenotype 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

6. 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

7. 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)

8. 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

9. 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

10. Metabolic Network Proteins mRNA Genomic Sequence 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)

11. ‘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)

12. Modules in Metabolic Networks

13. We chose the glycolytic module

14. C X Our Methodology Knowing the true system k A B Systems Approach

15. 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

16. Our Methodology In a typical enzymatic reaction (non ideal), physical constraints exist that prevent complete depletion of substrate. Therefore, where kfis the fitting parameter and 0< kf<1 (Constraint)

17. k2 A B X Our Methodology For feedback/feedforward mechanisms k2 could be a function of the upstream/downstream substrate

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

19. Constraints Lead coefficient constraint, 0< kf<1 E1 A B

20. Constraints Rate coefficient constraint, 0.1<kb<1.0

21. 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

22. Glycolytic Network and Measured Values for Erythrocytes (RBC)

23. Comparison between Measured and Predicted Values in RBC *Model of 2,3-biphosphoglycerate metabolism in the human erythrocyte Biochem. J. 342 (1999), Mulquiney & Kuchel *

24. Robustness of Model Parameters+/- 20% Variation in Input G6P Values

25. Robustness of Model Parameters+/- 20% Variation in All Model Parameters

26. 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

27. Metabolic Phenotypes of Erythrocytes and Myocytes are Highly Distinct

28. Prediction of Myocyte Glycolytic Phenotype

29. Discovery of Cell-type Specific Pathways Using Computational Simulations

30. Trypanosoma Brucei (T.brucei) • is a parasite • causes the African Sleeping Disease or Trypanosomiasis • carried by Tsetse fly

31. Prediction of T.brucei Glycolytic Phenotype (Aerobic Condition)

33. Prediction of T.brucei Glycolytic Phenotype under Aerobic Condition

34. 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

35. Optimising model for Cell-Specificity, T.brucei

36. Prediction of T.brucei Glycolytic Phenotype after Optimisation, Aerobic Condition

37. Prediction of T.brucei Glycolytic Phenotype under Anaerobic Condition

38. T.brucei Aerobic Condition