2nd International Conference on Metabolomics & Systems Biology
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This document summarizes a presentation on systems biology and metabolic diseases given at the 2nd International Conference on Metabolomics & Systems Biology. The presentation discussed how systems medicine uses systems biology approaches and computational modeling to study disease pathways and networks. It described how integrating metabolomics data with mathematical models of metabolic networks can provide insights into metabolic profiling of diseases. Network-based analysis and constraint-based modeling approaches like flux balance analysis were also discussed. The presentation concluded by discussing computational modeling of lipoprotein metabolism and how such models can be used for diagnostic and pharmaceutical applications.
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2nd International Conference on Metabolomics & Systems Biology
- 1. 2nd International Conference on Metabolomics & Systems Biology April 8, 2013 Natal van Riel Eindhoven University of Technology, the Netherlands Dept. of Biomedical Engineering, n.a.w.v.riel@tue.nl Systems Biology and Metabolic Diseases
- 2. Systems Medicine / biomedical engineering PAGE 219-8-2013 • ‘Systems Medicine involves the implementation of systems biology approaches in medical concepts, research and practice, through iterative and reciprocal feedback between data-driven computational and mathematical models as well as model-driven translational and clinical investigations and practice’ EC Coordinating Action Systems Medicine – CASyM • Understanding disease pathways / networks • Personalized Healthcare / Medicine • biomarkers • patient specific intervention • guide drug discovery
- 3. Metabolic profiling of diseases • Metabolome: • current physiological state • interaction of the genotype with the environment • clinical diagnostics • Metabolic networks: • structured information about how metabolites and reactions are interconnected and organized into pathways • Data integration concept: • metabolomics (metabolite profile) • mathematical models of metabolic networks / biomedical engineering PAGE 319-8-2013 time
- 4. Network-based analysis Mathematical model Modeling strategy, depending on type of data and questions • Constrained genome-wide modeling • stoichiometric model / Genome-Scale Metabolic Models (GSMM’s) / Constraint-Based Metabolic Models (CBMM) • Recon 2 • Thiele et al. 2013, Nat Biotech. • Total number of reactions 7,440 • Total number of metabolites 5,063 • Number of unique metabolites 2,626 / biomedical engineering PAGE 419-8-2013 Cytoscape http://humanmetabolism.org/
- 5. • Graphs • Stoichiometric matrix N • Mass balances (Differential Equations) • Steady-state (concentrations constant over time), Nr = 0 a metabolic fingerprint / snapshot Metabolic Balancing Analysis / biomedical engineering PAGE 519-8-2013 0v 1v 0v 1v 2v 0v 1v 2v 3v 1v 2v 3v 1v 2v System of algebraic equations An underdetermined system Measurements to constrain the underdetermined system Isotopic tracers, e.g. 13C Flux space
- 6. Fluxes in Metabolic Networks • Flexibility and variability in metabolic flux / biomedical engineering PAGE 619-8-2013 Two equivalent routes for converting an input substrate into an output metabolite If we know/assume that the system aims for minimization of total intracellular fluxes, both routes are not equivalent If the objective is to maximize ATP yield then also only one route will be utilized
- 7. Flux Balance Analysis • Assume the homeostatic behavior of the metabolic system somehow reflects an optimal situation • Introduce a mathematical objective function, for example • minimization of total intracellular fluxes • maximizing ATP production • maximizing the production of a particular metabolite • minimizing nutrient uptake • … • Optimizing (solving) the under-determined set of algebraic equations can be done by linear programming • Flux distribution • Visualization / biomedical engineering PAGE 719-8-2013 COnstraints Based Reconstruction and Analysis (COBRA) Toolbox for Matlab, http://opencobra.sourceforge.net http://sbml.org
- 8. Conclusions (1) Advantages: • Genome-wide, especially good coverage of small, monomeric molecules and central metabolism • Comprehensive network topology (wiring) • Describes fluxes • Possible to integrate multivariate data Limitations: • Qualitative / semi-quantitative • Weak in polymeric metabolites with large heterogeneity, e.g., lipids, lipoproteins / biomedical engineering PAGE 819-8-2013
- 9. / biomedical engineering PAGE 919-8-2013 Profiling lipids and lipoproteins
- 10. Lipoprotein metabolism • 3 types of lipoproteins • Chylomicrons • Very low density lipoproteins (VLDL), apoB • High density lipoproteins (HDL), apoA • A continuum of particles of different size, different composition of TG, cholesterol and CE • With distinct apo-lipoproteins • Metabolic Syndrome (MetS) • Lipoprotein particle size codetermines metabolic and cardiovascular disease risks / biomedical engineering PAGE 1019-8-2013 0 10 20 30 40 50 FPLC(arbitrary units) Fraction number VLDL IDL/LDL HDL
- 11. Computational framework / biomedical engineering PAGE 1119-8-2013 • The molecular mechanisms that underlie the characteristics of plasma lipoprotein distributions are not fully understood • Fasted condition, no chylomicrons • Particle size and heterogeneity selective uptake CE index Triglycerides Cholesteryl ester
- 12. Processes in the model • ApoA-containing lipoprotein metabolism (HDL) • ApoB-containing lipoprotein metabolism (VLDL, LDL) / biomedical engineering PAGE 1219-8-2013 PLTP CETP CETP: cholesteryl ester transport protein PLTP: phospholipid transfer protein
- 13. Computational approach • Integration of model and data • Dealing with imperfect data (noisy, missing, inconsistent) • Inference of model parameters (parameter estimation) Maximum Likelihood Estimation, Bayesian • Identify control points (parameter sensitivity analysis) • Uncertainty analysis • Structural: multiple, competing hypotheses (hypothesis testing) • Numerical: propagation of uncertainty in data, to uncertainty in parameters and model predictions / biomedical engineering PAGE 1319-8-2013 Fit of measured profiles Prediction of unobserved quantities
- 14. Pharmaceutical intervention / biomedical engineering PAGE 1419-8-2013 • Liver X receptor (LXR) activation by T0901317 induces effects in both cholesterol and fatty acid homeostasis Targets: ABCA1, ApoE, PLTP, LPL, etc. + Reverse cholesterol transport + Large, anti-atherogenic HDL -Hepatic steatosis -- Production of large, triglyceride-rich VLDL Schultz et al, Genes Dev. 2000;14(22):2831-8 Grefhorst et al, 2012 Atherosclerosis 222(2): 382
- 15. Conclusions (2) / biomedical engineering PAGE 1519-8-2013 • Computational model-based diagnostics • Modeling lipoprotein metabolism Here: • Incorporates HDL (ApoA) and ApoB-containing lipoproteins (VLDL/IDL/LDL) • Particle heterogeneity − composition and size of the VLDL and HDL particles change independently − describes both triglyceride (TG) and cholesteryl ester (CE) content • Dynamics • Adaptive response, linking longitudinal phenotypic snapshots Analysis of Dynamic Adaptations in Parameter Trajectories (ADAPT) http://bmi.bmt.tue.nl/sysbio/ • Compartment models − Adiels et al, 2005, J Lipid Res, 46: 58-67 − van Schalkwijk et al, 2009, J Lipid Res, 50: 2398–2411 − Tiemann et al. 2011, BMC Syst Biol, 5:174 • Stochastic particle model − Hubner et al, 2008, PLoS Comput Biol, 4(5): e1000079
- 16. Acknowledgement • Kinetic modeling • Ceylan Çölmekçi Öncü • Gijs Hendriks • Anne Maas • Yvonne Rozendaal • Joep Schmitz • Sjanneke Zwaan • GSMM • Marijke Dermois • Robbin van den Eijnde • Huili Yuan • ADAPT • Christian Tiemann • Joep Vanlier • Fianne Sips • Roderick Snel • Collaborators • Peter Hilbers • Bert Groen • Jan Albert Kuivenhoven • Barbara Bakker / biomedical engineering PAGE 1619-8-2013 Brainbridge
Editor's Notes
- 20 min2 topics, common theme: title» Change the starting slide numberTo change the slide number that appears on the first slide in your presentation, do the following:On the Design tab, in the Page Setup group, click Page Setup.In the Page Setup dialog box, in the Number slides from box, enter the number that you want shown on the first slide in your presentation, and then click OK.» Handouts printen op eenzwart-wit printer: kies (toch) voorafdrukken in Color (en niet grayscale)» Powerpoint security warning: references to external pictures have been blockedIf you copy an image from a website with Ctrl+C, the chance is very high, that you involuntarily copy a bit of HTML code which causes that warning message.
- Systems Medicine (application)Christina Kyriakopoulos, EuropeanCommission:Systems Medicine in Horizon 2020Systems Medicine is considered as the most innovative approach in biomedical sciencenshttp://www.eurekalert.org/pub_releases/2012-12/haog-cpr121912.phpCoordinating Action Systems Medicine – CASyM
- We will develop and apply pathway-based analyses to integrate multi-dimensional datasets and analyse how genetic differences affect the individual metabolic networks.Metabolome / metabolomics: the best indication of the current physiological state of a cell or organism, constituting the interaction of the genotype with the environmentcan provide the bridge to interpret genetic data in the context of individual patients in medical research and clinical diagnostics
- Most pathway-based methods are based on gene set enrichment analysis (GSEA), where gene sets are defined by annotated pathways from biochemical databases such as KEGG and BioCyc or by Gene Ontology terms. However, this approach is biased towards traditional pathway definitions and does not take into account the complex interactions that are present in metabolic networks.mathematical models to integrate the vast amount of knowledge that is available about metabolic networks. The stoichiometric models provide structured information about how metabolites and reactions are interconnected and organized into pathways. High-throughput data has been utilized to reconstruct so-called Genome-Scale Metabolic Models (GSMM’s).The approach enables the integration of subject-specific metabolomic data with information on the genome (such as SNPs, epigenetic information), transcriptome (gene expression, mRNA) and proteome if such data is available. pathway-based analysisNetwork-based pathway analysisGille 2010 Mol SystBiol 6 #411-HepatoNet Jerby,Shlomi,Ruppin 2010 Mol Sys Biol 6 #401-tissue-specific metabolic models human liver metabolism Jerby+Ruppin 2012 Clin Cancer Res 18(20): 5572–Genome-Scale Metabolic ModelingJerby 2012 Cancer ResThiele et al. 2013, Nat Biotech. A community-driven global reconstruction of human metabolismRecon2 in Cytoscape (from assignment Zandra Felix)https://gephi.org/www.cytoscape.org/Detailed kinetic models, acute response to metabolic changes, such as stress tests
- Analyzing pathway diagramsv0= 1Network BiologyMetabolic Flux AnalysisSupplement flux measurements with a mathematical approach…
- Fluxes in Metabolic Networks / Analyzing pathway diagramsFeatures of a metabolic network than can lead to flux variability in FBA. (A) Two equivalent routes (shown in green and blue) for converting an input substrate into an output metabolite. (B,C) illustrate non-equivalent routes that may be discriminated in FBA, depending on the objective function. (D) A substrate cycle. (E) Equivalent routes in different subcellular compartments (the dashed line indicating a membrane separating two subcellular compartments).A)Othercommonlyusedobjectivefunctions such as maximization of biomass per unit substrate (and the equivalent minimization of substrate consumed per unit biomass produced) which optimize the molar yield of the system would also fail to discriminate between the two routes if they are stoichiometrically equivalent with respect to carbon.if the two routes contain a different number of steps (Figure 1B) then the route with the fewest steps will be utilizedunder the minimization of flux objective function. Other differences between parallel pathways relate to energy production (Figure 1C). If the objective function is to maximize ATP yield then the objective function would select the ATP-producing pathwayAnother source of alternative solutions can be the presence of substrate cycles (Figure 1D).Subcellular compartmentation, especially the presence of equivalent pathways in different compartments (Figure 1E), can also lead to alternative flux solutions.From Sweetlove LJ, Ratcliffe RG. 2011. Flux-balance modelling of plant metabolism. Frontiers in Plant Physiology 2, 38.
- Fluxes in Metabolic Networks (Fluxomics)Metabolic Flux Analysis-Measurements to constrain the underdetermined system- Isotopic tracer measurements, e.g. 13CMetabolic balancing analysis
- Focus on fluxes, which can be considered more closely related to (dys)function than concentrationsNo information about concentrations, whereas metabolomics provides information about concentrationsMetabolic flexibility (variability / uncertainty analysis)
- Profiling metabolites with large heterogeneitylipidomics
- Transport of lipids in the bodyVLDL vs HDL markers for metabolic and cardiovascular disease risksHowever, more information is present in the metabolic profilesThe molecular mechanisms that underlie the characteristics of plasma lipoprotein distributions are not fully understood
- Computational model- Data integration- Understanding / hypothesis testing The molecular mechanisms that underlie the characteristics of plasma lipoprotein distributions are not fully understoodA calibration function was determined to relate FPLC fraction to the lipoprotein concentrations in the computational framework. 16 parameters in phenomenological ODE’s
- 16 parameters in phenomenological ODE’s6 HDL processen, 4 VLDL processen, 12 deelvergelijkingen.De grids zijn beide 8 x 40, dus er zijn in totaal in principe 2 x 320 differentiaalvergelijkingenbecause mice lack CETP (cholesteryl ester transport protein)ApoB lipoproteins (VLDL, LDL) can be converted into HDL by the transfer protein PLTP (phospholipid transfer protein)CETP: cholesteryl ester transport protein, converts HDL into ApoB lipoproteins (VLDL, LDL)
- Wild-type C57BL/6J mice(n=6), Fasted (no chylomicrons)Wild-type mouse model can describe the FPLC dataTargetted analysis/metabolomicsData:Plasma cholesterol and triglycerides (TG) in fractions separated by gel filtration FPLC (fastproteinliquidchromatographyProduction fluxes VLDL particles Integration of model and multivariate dataModel is fitted to cholesterol and TG FPLC profilesModel provides informationaboutunderlyingcompositional contributions of free cholesterol (C), cholesterylester (CE), and phospholipidsDealing with imperfect datanoisymissinginconsistentSample-to-sample varianceUncertaintypropagation (from data to prediction)analysis and quantificationUncertainty Analysis in Systems Biology
- LXR activationby T0901317Despite (or as a result of) the many known perturbations, the precise mechanismof HDL enlargement has not been fully elucidated.Extending the model to describe LXR activation3 hypotheses:1. ApoEmediated cholesterol effluxApoEcanstimulate (SR-B1 mediated) cholesterol efflux, e.g. Chroni et al, 2005Model: additional cholesterol uptake, minimum surface2. Uptake of ApoE-richparticlesApoE is a ligandforreceptors LRP/VLDLr/LDLr/CRR (Kurano et al, 2011)Model: Gaussianuptakefunction (as in VLDL)3. Production of large, ApoE-rich HDLKurano et al, 2011: in vitro, hapotocytesstimulatedwith T0901317 producelarge, ApoE-richnascent HDLModel: Large (log-normallydistributed) TG-poornascent HDL inputParameter estimation of the extended models was performed, initialized by randomly initiating the 2, 3 or 3 parameters of the extended equation while keeping the 15 core model parameters to their wild-type value.The data for LXR activated mice shows a clear increase in the LDL range, which is due to the appearance of enlarged HDL. All extended models were found to explain the enlarged HDL peak.The decrease in TG is rather unexpected (??), but is explained by the modelNote: mice lack CETP (cholesteryl ester transport protein)Model predicts / identifies Fluxes over timeThe flux necessary to accomplish the second peak is small -> testable predictionsCholesterol flux due to cholesterol uptake by the HDL particle, Cholesterol flux due to HDL catabolismLXR activation in KO phenotypes -SR-B1 (Grefhorst, 2012), Sameconditions (14 days T09) -PLTP (Cao, 2002),Different conditions (7 days T09, different dose)
- Kinetic modelingAccute effectSee Project idea for internship computational modelingCholesterol and triglyceride fluxes in ApoB-lipoproteinsAlbert A de GraafNiek C.A. van de Pas Developing computational model-based diagnostics to analyse clinical chemistry dataDaniel B. van Schalkwijk,
- FP7-HEALTH.2012.2.1.2-2: Systems medicine: Applying systems biology approaches for understanding multifactorial human diseases and their co-morbidities, starting in 2013large-scaleintegrating research project (Cooperationprogramme)Metabolic Profiling and PhenotypingProgressivemetabolicdiseasesSynthetic Biology