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Modelling of Protein Networks

Modelling of Protein Networks. Workshop Computational Life Sciences 2005 Innsbruck – 2005-10-14 Marc Breit , M.Sc. Institute for Biomedical Engineering UMIT - Hall in Tyrol - Austria. Healthcare & Life Sciences. Early diagnosis of diseases. Drugs without side effects. Improved

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Modelling of Protein Networks

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  1. Modelling of Protein Networks Workshop Computational Life Sciences 2005 Innsbruck – 2005-10-14 Marc Breit, M.Sc. Institute for Biomedical Engineering UMIT - Hall in Tyrol - Austria

  2. Healthcare & Life Sciences Early diagnosis of diseases Drugs without side effects Improved Healthcare Reliable diagnosis Tracing of drug treatments Targeted therapeutic treatment

  3. www.gradschool.purdue.edu/CLS/ Computational Life Sciences The development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems. The research, development or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data. The development of quantitative, mechanistic based models of the whole cell, collections of cells or large pieces of the cellular machinery, where the objective is an integrated picture that compliments the reductionist viewpoint of molecular biology.

  4. Research fields Data Mining Metabolomics Genomics Bioinformatics Proteomics Systems biology Evolution Phylogeny Biological diversity

  5. Introduction Systematic analysis of biological systems Integrating the „–omics“ Genomics Transcriptomics Proteomics Metabolomics Understanding of systems Structure Dynamics – Analysis & models Control-Methods Design-Methods Multidisciplinarity www.systemsbiology.org Systems biology History • Systems-theory in biology • 1948: Wiener N – Cybernetics • 1970: Metabolic Control Theory • Former problems • Appropriate data & experiments • Quantitative biology • Cell biology • 1838: Cell Theory • 2000: Human genome project • Improvements • Accuracy of measuring methods • Quantitative experiments

  6. Tumor Necrosis Factor α (TNFα) Plays an important role in various diseases Cancer, sepsis, diabetes Osteoporosis, Multiple sclerosis Morbus Crohn Drugs e.g. Infliximab Mode of operation Patient-dependent Non-specific impact Problems Blocking of the receptor Various complications Immune-suppression Generation of antibodies Clinical questionInflammatory diseases – Morbus Crohn

  7. Idea Examination of the TNFα signalling cascade TNFα signalling cascade Apoptosis, Cell growth Proteins controlled through signalling cascade Activation of gene-transcription Generation of proteins Controls level of expression Objective Specific inhibition Tumor Necrosis Factor α (TNFα) Biological Cartoon • Abstract representation • Compartments • Interactions Cho KH et al. Simulation. 2003 Dec;79(12):726-39.

  8. Modelling and simulation of biological systems • Literature, Databases • Cartoons • abstract • Proteins • Interactions • not: time • Problem • Reaction equations • Estimation • Kinetic parameters • Initial conditions Dhar P. pawan_sysbio_lect11.pdf. Biocarta: EGF MAPK Kaskade Wolkenhauer O. Briefings in Bioinformatics. 2001;2:258-270.

  9. Simulation Hypothesis and predictions Modelling and simulation of biological systems • Graphical representation • Mathematical model Schoeberl B et al. Nat Biotechnol. 2002 Apr;20(4):370-5. dm1/dt = -k1*m1*m2 + k2*m3

  10. Model for the kinetics of enzyme-catalysed reactions • Michaelis Menten 1913 • Set of differential equations • Fundamental assumptions • Slowly time-varying system • Steady-state

  11. State of Research Analysis und Modelling • Examination of structure and dynamics of cellular function • Necessity of mathematical models Examples for signalling cascades • 1996 Huang • MAPK • 18 rate equations • 1997 Ferrell • MAPK • Mathematica • 1999 Bhalla • Networks • Michaelis-Menten • 2001 Schoeberl • TNFα • 280 ODEs • 110 Parameter • 2001 Astaghiri • MAPK • Matlab ode23s • 2002 Schoeberl • MAPK EGF • 94 Variable • 95 Parameter • 2003 Cho • RKIP ERK • ODEs • 2003 Cho • TNFα NF-κB • 18 ODEs • Initial Values • 2003 Cho • TNFα NF-κB • 31 ODEs • MPSA • 2004 Babu • EGFR • 29 molecules [Kitano; Tyson; Astaghiri]

  12. The TNFα - NF-κB signalling cascade A quantitative mathematical model • Graphical representation • Set of differential equations Cho KH et al. Simulation. 2003 Dec;79(12):726-39.

  13. Kinetic parameters The TNFα - NF-κB signalling cascade A quantitative mathematical model • Initial concentrations

  14. In the model Variation of kinetic parameters t: m k2 αi k(1) k k1 Ordinary differential equations • Corresponds with the form • Matlab Routine sens_ind • 3-dimensional array • gradient • time-dependent dm1/dt = -k1*m1*m2 + k2*m3 [t,m,dmdk] = sens_ind(odefile,tspan,m0,options,k)

  15. Parametric sensitivity analysis m4 m4 m4 k1 m4 k3

  16. Mathematical model Dataimport Sensitivity analysis Calculation of concentration Calculation of gradient Analysis of the 3-dimensional array Parameter-Variation Calculation of the solutions depending from the actual value Saving the results Matlab Workspace .mat-file Matlab Software tool

  17. Validation through sensitivity analysis • Parameters • with highest values of gradients • involved with various components, • supposed to be the most sensitives • Identified parameters • TNFα/TNFR1 association (k1), • TNFα/TNFR1/TRADD association (k3), • TNFα/TNFR1/TRADD/RIP1 association (k5), • TNFα/TNFR1/TRADD/TRAF2 association (k7), • RIP1/Caspase-8 association (k17), • NF-κB→c-IAP (k19), • TNFα/TNFR1/TRADD/FADD association (k20) • caspase-8/Effector association (k25) • k1, k3, k5, k7, k17, k19, k20, k25 • Parameter with effect on small number of components • RIP1/Caspase-8→RIP1c+RIP1n (k18)

  18. Variation of parameter k7 • TRAF2 and TNFα/TNFR1/TRADD complex (k7) • Nominal value • 0.185 μM-1s-1 • Range • from 0.037 μM-1s-1 • to 0.925 μM-1s-1, • Number of values 50

  19. FADD and TNFα/TNFR1/TRADD complex (k20) Nominal value 0.185 μM-1s-1 Range from 0.037 μM-1s-1 to 0.925 μM-1s-1, Number of values 50 Variation of parameter k20

  20. RIP1/Caspase-8 → RIP1c+RIP1n (k18) Nominal value 0.37 s-1 Range from 0.074 s-1 to 1.85 s-1 Number of values 50 Variation of parameter k18

  21. Literature Known sensitive kinetic parameters k1, k3, k7, k17, k19, k20 With our research Eight parameters identified k1, k3, k5, k7, k17, k19, k20, k25 Primary key positions Parameter k18 Border area of the model Validation of the approach of sensitivity analysis

  22. Software platform Systematic analysis Examination of any pathway possible Databases, eg DOQCS Pathway-Interactions Extensions Examination of initial concentrations Development of a framework Further activities • Tool for visualisation of dynamical behaviour • Enhancement of the TNFα signalling cascade • Application and analysis • Development of a database for biological knowledge • Interfacing and connection

  23. Acknowledgement • Christian Baumgartner • Bernhard Pfeifer • Mahesh Visvanathan • Bernhard Tilg • Robert Modre Institute for Biomedical Engineering UMIT - Hall in Tyrol - Austria

  24. Thank you for your attention

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