Analyzing and integrating probabilistic anddeterministic computational models in Synergy-COPDEISBM workshop, Lyon, June 14...
EISBM workshop (BDigital)Synergy-COPD presentationParticipants•   Barcelona Digital Technology Centre, BDigital (coordinat...
EISBM workshop (BDigital)Synergy-COPD presentationFocus and budget•   Simulation environment and a decision-support system...
EISBM workshop (BDigital)Synergy-COPD presentationThe point of view of the                               David’sbio-resear...
Relationships between Oxygen      Transport/Utilization and    mitochondrial ROS generation:       computational prototype...
1. Introduction
2. c)  Vertical model integrationVertical integration (deterministic models) CMISS            Dr. Kelly Burrowes (UOXF.BL)...
2. c)  Vertical model integrationVertical model integration (steady state conditions)   Oxygen transport and utilization  ...
2. c)  Vertical model integrationVertical model integration strategy                   Oxygen transport                   ...
3.  Computational prototypeVertical integration (Computational Prototype)  isaac.cano@linkcare.es                         ...
3.  Computational prototypeVertical integration (Computational Prototype)  isaac.cano@linkcare.es                         ...
3.  Computational prototypeVertical integration (Computational Prototype)  isaac.cano@linkcare.es                         ...
3.  Computational prototypeVertical integration (Computational Prototype)  isaac.cano@linkcare.es                         ...
3.  Computational prototypeVertical integration (Computational Prototype)  isaac.cano@linkcare.es                         ...
3.  Computational prototypeVertical integration (Computational Prototype)  isaac.cano@linkcare.es                         ...
3.  Computational prototypeVertical integration (Computational Prototype)  isaac.cano@linkcare.es                         ...
3.  Computational prototypeCurrent work: ‐ Sensitivity analysis of the vertical integrated model. ‐ Extend the computation...
Questions?isaac.cano@linkcare.es                6/18/2012   18
Probabilistic modelingDavid Gomez-Cabrero (Karolinska Institute)EISBM workshop, Lyon, June 14, 2012
EISBM Workshop.                  Lyon, France, 14th June 2012Mechanistic and Probabilisticmodels…                         ...
EISBM Workshop.                   Lyon, France, 14th June 2012Mechanistic and Probabilisticmodels…                        ...
EISBM Workshop.                   Lyon, France, 14th June 2012Mechanistic and Probabilisticmodels…                        ...
EISBM Workshop.                                                         Lyon, France, 14th June 2012Part I: predictive net...
EISBM Workshop.                           Lyon, France, 14th June 2012Part I: predictive networks.    DATA    BIOBRIDGE   ...
EISBM Workshop.                           Lyon, France, 14th June 2012  NETWORKCONSTRUCTION                               ...
EISBM Workshop.                           Lyon, France, 14th June 2012  NETWORK        Extended genes,CONSTRUCTION     Top...
EISBM Workshop.                                Lyon, France, 14th June 2012                                 STRATEGY:     ...
EISBM Workshop.                                                                 Lyon, France, 14th June 2012              ...
EISBM Workshop.                            Lyon, France, 14th June 2012                BAYESIAN NETWORKS: STRATEGY       N...
EISBM Workshop.                                                  Lyon, France, 14th June 2012                             ...
EISBM Workshop.                             Lyon, France, 14th June 2012Part I: predictive networks.              BAYESIAN...
EISBM Workshop.                  Lyon, France, 14th June 2012Mechanistic and Probabilisticmodels…                         ...
EISBM Workshop.                                     Lyon, France, 14th June 2012Deterministic models may not cover all the...
EISBM Workshop.                   Lyon, France, 14th June 2012  MECH MODEL                    GIVEN VALUES/PERTURBATIONS  ...
EISBM Workshop.                                                     Lyon, France, 14th June 2012         SELECTING MODELS ...
Clinical decision support systemLuigi Ceccaroni and Filip Velickovski(Barcelona Digital Technology Centre, BDigital)EISBM ...
Clinical decision support systemBarcelona Digital Technology Centre, BDigitalEISBM workshop, Lyon, June 14, 2012
EISBM workshop (BDigital)Topics1. Synergy-COPD presentation2. Summary of current status of CDSS in   Synergy-COPD3. Clinic...
EISBM workshop (BDigital)Synergy-COPD’s CDSS features•   Framework: Java-based•   Decision engine: Drools rules engine•   ...
EISBM workshop (BDigital)CDSS architecture                                            40
EISBM workshop (BDigital)Clinical-data representation: vMR•   Patient data in the CDSS are represented as a HL7 virtual   ...
EISBM workshop (BDigital)Clinical-data representation: vMR
EISBM workshop (BDigital)Clinical-data representation: vMR
EISBM workshop (BDigital)  Data representation: vMR example<!-- Clinical statement for dyspnea symptom --><clinicalStateme...
EISBM workshop (BDigital)A rule from case findingrule "COPD symptoms - Dyspnea"when  $state : SystemState(context == Conte...
EISBM workshop (BDigital)CDSS reasoning paradigm                                           46
EISBM workshop (BDigital)Running the CDSSINFO: Initiating Reasoning Engine...30-ene-2012 16:23:18 cdss.re.ReasoningEngine ...
EISBM workshop (BDigital)Running the CDSSOutputRecommendation: Diagnose patient as COPD.Reason: The most recent spirometry...
EISBM workshop (BDigital)Scope of CDSSCurrent the Synergy-COPD CDSS engine does:• Simple screening based on symptoms• Diag...
EISBM workshop (BDigital)Next steps in Synergy-COPD CDSS• To expand clinical scope:  • To make recommendations about     p...
EISBM workshop (BDigital)Clinical significance of models•   Which are the "outputs" of the models that can be used in the ...
EISBM workshop (BDigital)Clinical significance of models•   Which are the "outputs" of the models that can be used in the ...
EISBM workshop (BDigital)Clinical significance of models•   Which are the "outputs" of the models that can be used in the ...
EISBM workshop (BDigital)Patient data storage and retrieval• Currently the “EHR” is simulated as HL7  XML files in the CDS...
Analyzing and integrating probabilistic anddeterministic computational models in Synergy-COPDEISBM workshop, Lyon, June 14...
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Analyzing and integrating probabilistic and deterministic computational models in Synergy-COPD.

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Presentation made during the EISBM workshop, 13-15 June 2012 by Luigi Ceccaroni (BDigital), Isaac Cano (IDIBAPS), David Gomez-Cabrero (Karolinska Institute).

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Transcript of "Analyzing and integrating probabilistic and deterministic computational models in Synergy-COPD."

  1. 1. Analyzing and integrating probabilistic anddeterministic computational models in Synergy-COPDEISBM workshop, Lyon, June 14, 2012Luigi Ceccaroni and Filip Velickovski(Barcelona Digital Technology Centre, BDigital)Isaac Cano (IDIBAPS)David Gomez-Cabrero (Karolinska Institute)
  2. 2. EISBM workshop (BDigital)Synergy-COPD presentationParticipants• Barcelona Digital Technology Centre, BDigital (coordinator)• Biomax Informatics AG• Linkcare S.L.• IDIBAPS• Karolinska Institute• The University of Oxford• The University of Birmingham• Infermed Ltd.• Technical University of Budapest 2
  3. 3. EISBM workshop (BDigital)Synergy-COPD presentationFocus and budget• Simulation environment and a decision-support system to enable the deployment of systems medicine• Core elements: • knowledge base • inference engine and simulation environment • graphical visualization environments• The proposal focuses on patients with chronic obstructive pulmonary disease (COPD).• Total budget: 5 M€• Total EC contribution: 4 M€ 3
  4. 4. EISBM workshop (BDigital)Synergy-COPD presentationThe point of view of the David’sbio-researcher presentation Isaac’s presentation 4
  5. 5. Relationships between Oxygen  Transport/Utilization and  mitochondrial ROS generation:  computational prototype Isaac Cano (IDIBAPS, Barcelona, Spain) isaac.cano@linkcare.esIsaac Canoisaac.cano@linkcare.es EISBM, Lyon 06/14/2012 6/18/2012 5Linkcare Health Services SL
  6. 6. 1. Introduction
  7. 7. 2. c)  Vertical model integrationVertical integration (deterministic models) CMISS Dr. Kelly Burrowes (UOXF.BL) • Spatial heterogeneities of lung ventilation and perfusion.FORTRAN Prof. Peter D. Wagner (UCSD) Mr. Isaac Cano (HCPB) • Central and peripheral O2 transport and utilization. • Pulmonary gas exchange. • Regional‐lung heterogeneities in ventilation and perfusion. C++ Dr. Marta Cascante (UB) Dr. Vitaly Selivanov (IDIBAPS, UB) • Skeletal muscle bioenergetics. • Mitochondrial ROS generation. isaac.cano@linkcare.es 6/18/2012 7
  8. 8. 2. c)  Vertical model integrationVertical model integration (steady state conditions) Oxygen transport and utilization ROS production Normal COPDThe higher the mitochondrial dysfunction,  The lower the PmO2/P50 ratio,  the lower the PmO2/P50 ratio the higher the ROS production 6/18/2012 8
  9. 9. 2. c)  Vertical model integrationVertical model integration strategy  Oxygen transport  and utilization Skeletal muscle bioenergetics &  Mitochondrial ROS generation Relationships between  Oxygen  Transport/Utilization  and mitochondrial ROS  generation isaac.cano@linkcare.es 6/18/2012 9
  10. 10. 3.  Computational prototypeVertical integration (Computational Prototype) isaac.cano@linkcare.es 6/18/2012 10
  11. 11. 3.  Computational prototypeVertical integration (Computational Prototype) isaac.cano@linkcare.es 6/18/2012 11
  12. 12. 3.  Computational prototypeVertical integration (Computational Prototype) isaac.cano@linkcare.es 6/18/2012 12
  13. 13. 3.  Computational prototypeVertical integration (Computational Prototype) isaac.cano@linkcare.es 6/18/2012 13
  14. 14. 3.  Computational prototypeVertical integration (Computational Prototype) isaac.cano@linkcare.es 6/18/2012 14
  15. 15. 3.  Computational prototypeVertical integration (Computational Prototype) isaac.cano@linkcare.es 6/18/2012 15
  16. 16. 3.  Computational prototypeVertical integration (Computational Prototype) isaac.cano@linkcare.es 6/18/2012 16
  17. 17. 3.  Computational prototypeCurrent work: ‐ Sensitivity analysis of the vertical integrated model. ‐ Extend the computational prototype to read/write model input parameters  encoded and stored in the Synergy‐COPD knowledge base. ‐ Direct connection with patient‐specific data from CT scan images.  ‐ Extend the computational prototype to work as an API for the Synergy‐COPD simulation environment. isaac.cano@linkcare.es 6/18/2012 17
  18. 18. Questions?isaac.cano@linkcare.es 6/18/2012 18
  19. 19. Probabilistic modelingDavid Gomez-Cabrero (Karolinska Institute)EISBM workshop, Lyon, June 14, 2012
  20. 20. EISBM Workshop. Lyon, France, 14th June 2012Mechanistic and Probabilisticmodels… All at once? 20
  21. 21. EISBM Workshop. Lyon, France, 14th June 2012Mechanistic and Probabilisticmodels… All at once? Part 1: Predictive networks Part II: Integration 21
  22. 22. EISBM Workshop. Lyon, France, 14th June 2012Mechanistic and Probabilisticmodels… All at once? Part 1: Predictive networks Part II: Integration 22
  23. 23. EISBM Workshop. Lyon, France, 14th June 2012Part I: predictive networks. DATA BIOBRIDGE PAC-COPD COPD at the time of a first hospital admission shows a wide variability on its physio-pathological and clinical characteristics Phenotypical heterogeneity in COPD can be classified in clinical / epidemiologically relevant subtypes These subtypes will differ on its clinical and functional course, use of services and survival Garcia-Aymerich J et al Thorax 2011 May;66(5):430-7
  24. 24. EISBM Workshop. Lyon, France, 14th June 2012Part I: predictive networks. DATA BIOBRIDGE BHAM BME Barabasi et al. Nature Reviews Genetics 12, 56-68 (2011) NO QUANTITATIVE PREDICTION
  25. 25. EISBM Workshop. Lyon, France, 14th June 2012 NETWORKCONSTRUCTION BME Experimental/Predicti Data Links Nodes ve Binary Interaction Network (binary Experimental/(Binary interactions from considered from Intact Y2Hybrid, Intact and Mint database) and Mint) 15315 6101 Binary Interaction network Y2Hhybrid 7190 3302 Experimental/Binary only Transfac regulatory 1340 781 Experimental network Metabolic coupling Experimental/ Interaction network 10642 921 Predictive (BIGG/KEGG) Curated HPRD- 74195 10890 Literature curated Biogrid-Intact-Mint CORUM-All 31276 2069 Experimental complexes data Kinase-substrate 327 (kinases) pairs 6110 Experimental/Literature (PhosphoSitePlus, 1771 Phospho.ELM) (substrates) Barabási, New England Journal of Medicine (2007) 25
  26. 26. EISBM Workshop. Lyon, France, 14th June 2012 NETWORK Extended genes,CONSTRUCTION Topological Features NETWORKQUANTIFICATION PREDICTIVE STRATEGY: BAYESIAN NETWORKS - Small networks, - Enough data? - Predictive capacity?... 26
  27. 27. EISBM Workshop. Lyon, France, 14th June 2012 STRATEGY: BAYESIAN NETWORKS Select regions of- Small networks, interest- Enough data?- Predictive capacity?... Prior information Validation by specific BIOBRIDGE DATA questions and PAC- COPD PRIOR INFORMATION Public Resources 27
  28. 28. EISBM Workshop. Lyon, France, 14th June 2012 The protein encoded by this gene is a Core subunit of the Krebs tricarboxylic acid cycle enzyme mitochondrial membrane that catalyzes the synthesis of citrate Plays a role in intermediary respiratory chain NADH metabolism and energy from oxaloacetate and acetyl coenzyme dehydrogenase (Complex I) A. The enzyme is found in nearly all cells production. It may tightly that is believed capable of oxidative metablism. This associate or interact with the to belong to the minimal assembly required for protein is nuclear encoded and pyruvate dehydrogenase catalysis. transported into the mitochondrial matrix complex where the mature form is found. NDUFV1 IDH2 CS Core subunit of the mitochondrial membrane respiratory NDUFS2 chain NADH VO2max dehydrogenase (Complex I) that is believed to belong to the SDHA UQCRC1 minimal assembly OGDH required for catalysis. This is a component of the ubiquinol-Flavoprotein (FP) subunit of succinate cytochrome c reductase complex The 2-oxoglutarate dehydrogenase (complex III or cytochrome b-c1dehydrogenase (SDH) that is involved in complex catalyzes the overallcomplex II of the complex), conversion of 2-oxoglutarate to which is part of the mitochondrialmitochondrial electron transport chain succinyl-CoAand is responsible for transferring respiratory chain. 28 and CO(2).electrons from succinate to ubiquinone
  29. 29. EISBM Workshop. Lyon, France, 14th June 2012 BAYESIAN NETWORKS: STRATEGY NDUFV1IDH2 CS VO2max NDUFS2SDHA UQCRC1 Not completely the same, OGDH Not necessary, Input as resource 29
  30. 30. EISBM Workshop. Lyon, France, 14th June 2012 BAYESIAN NETWORKS: STRATEGY COPD TRAININGHow is training affecting our network? NDUFV1 IDH2 CSHow is COPD affecting our network? Which genes do I need to affect in order to VO2max NDUFS2 increase VO2max in a COPD patient? SDHA UQCRC1 OGDH Is training enough for a COPD patient? 30
  31. 31. EISBM Workshop. Lyon, France, 14th June 2012Part I: predictive networks. BAYESIAN NETWORKS: STRATEGY DATA Links clinical phenotypes, transcriptomics,… BIOBRIDGE Pre-selected questions But no trancriptomics here… PAC-COPD We can use the models from BIOBRIDGE data?
  32. 32. EISBM Workshop. Lyon, France, 14th June 2012Mechanistic and Probabilisticmodels… All at once? Part 1: Predictive networks Part II: Integration 32
  33. 33. EISBM Workshop. Lyon, France, 14th June 2012Deterministic models may not cover all the relevant aspects related to COPD. IDENTIFY EXTEND Qualitative networks allow to Bayesian Networks allow to generate identify aspects not quantitative relations among data considered in the model. that can cover those aspects and the model, 33
  34. 34. EISBM Workshop. Lyon, France, 14th June 2012 MECH MODEL GIVEN VALUES/PERTURBATIONS PROVIDES NEW STEADY STATEBAYESIAN NETWORK GIVEN VALUES/PERTURBATIONS PROVIDES EXPECTATIONS/”UPDATE IN BELIEF” 34
  35. 35. EISBM Workshop. Lyon, France, 14th June 2012 SELECTING MODELS DEFINING OUTPUT/INPUTBAYESIAN NETWORK GIVEN VALUES/PERTURBATIONS MECH MODEL GIVEN VALUES/PERTURBATIONS PROVIDES NEW STEADY STATE PROVIDES EXPECTATIONS/”UPDATE IN BELIEF” IDENTIFY LINKS RUN MODEL A RUN MODEL B 35
  36. 36. Clinical decision support systemLuigi Ceccaroni and Filip Velickovski(Barcelona Digital Technology Centre, BDigital)EISBM workshop, Lyon, June 14, 2012
  37. 37. Clinical decision support systemBarcelona Digital Technology Centre, BDigitalEISBM workshop, Lyon, June 14, 2012
  38. 38. EISBM workshop (BDigital)Topics1. Synergy-COPD presentation2. Summary of current status of CDSS in Synergy-COPD3. Clinical significance of models for COPD patients4. Patient data storage and retrieval5. Scope of the CDSS6. Use-case scenario 38
  39. 39. EISBM workshop (BDigital)Synergy-COPD’s CDSS features• Framework: Java-based• Decision engine: Drools rules engine• Clinical-knowledge representation: Drool rules for: a. Case-finding / screening b. Confirming COPD diagnosis c. Initial disease assessment (based on the stages of GOLD)• EHR’s API: HL7 virtual medical record (vMR, XML format) (imported via JAXB interface) 39
  40. 40. EISBM workshop (BDigital)CDSS architecture 40
  41. 41. EISBM workshop (BDigital)Clinical-data representation: vMR• Patient data in the CDSS are represented as a HL7 virtual medical record (vMR) XML format.• vMR is an evolving HL7 information model for representing personal data relevant to clinical decision support in a formal format.• vMR enables the use of standardized nomenclatures for data inputs and outputs.• vMR is aligned with preexisting HL7 data models (RIM, CCD), with a structure especially suited for clinical inference. 41
  42. 42. EISBM workshop (BDigital)Clinical-data representation: vMR
  43. 43. EISBM workshop (BDigital)Clinical-data representation: vMR
  44. 44. EISBM workshop (BDigital) Data representation: vMR example<!-- Clinical statement for dyspnea symptom --><clinicalStatement xsi:type="ObservationResult"><!-- Unique ID of the clinical statement --> <id root="d0f9e07c-4867-4202-a1b9-bb1bb8946008"/> <observationEventTime> <low value="20101122"/> <high value="20120129"/> </observationEventTime> <observationValue xsi:type="CD" code="391125004" codeSystem="2.16.840.1.113883.6.96" codeSystemName="SNOMED-CT"> <displayName value="grade 4"/> </observationValue></clinicalStatement> 44
  45. 45. EISBM workshop (BDigital)A rule from case findingrule "COPD symptoms - Dyspnea"when $state : SystemState(context == Context.SCREENING) VMR( $patient : patient ) # Dyspnea code is 267036007 $obsr : ObservationResult(observationFocus.code ==“267036007”, observationFocus.codeSystem ==“2.16.840.1.113883.6.96”)then logger.info("Patient has dyspnea"); insert(new Tag($obsr, "COPD symptom"));end 45
  46. 46. EISBM workshop (BDigital)CDSS reasoning paradigm 46
  47. 47. EISBM workshop (BDigital)Running the CDSSINFO: Initiating Reasoning Engine...30-ene-2012 16:23:18 cdss.re.ReasoningEngine <init>cdss.re.Rule_spirometry_measurement_imples_pulmonary_obstructionINFO: spirometry measurement implies pulmonary obstructioncdss.re.Rule_spirometry_measurement_imples_pulmonary_obstructionINFO: FEV1 is :2.0 FVC is :3.630-ene-2012 16:23:22cdss.re.Rule_spirometry_measurement_is_recent_0INFO: Measurement is recent30-ene-2012 16:23:22 cdss.re.Rule_confirmation_of_COPD_0defaultConsequenceINFO: Recommendation: Diagnose patient as COPD.30-ene-2012 16:23:22 cdss.re.Rule_Derive_age_from_birthtime_0defaultConsequenceINFO: Age 60.0 added 47
  48. 48. EISBM workshop (BDigital)Running the CDSSOutputRecommendation: Diagnose patient as COPD.Reason: The most recent spirometry result forthis COPD candidate has met the criteria:1. Measurement is recent and of high quality.2. Spirometry is performed after bronchodilation.3. Measurement implies pulmonary obstruction (FEV1 / FVC < 0.7). 48
  49. 49. EISBM workshop (BDigital)Scope of CDSSCurrent the Synergy-COPD CDSS engine does:• Simple screening based on symptoms• Diagnosis based on GOLD guideline criteria using: • Symptoms • Spirometry measurements• Assessment: • COPD Severity based on GOLD guideline criteria. 49
  50. 50. EISBM workshop (BDigital)Next steps in Synergy-COPD CDSS• To expand clinical scope: • To make recommendations about prognosis and treatment.• To develop a graphical visualization environment: • GUI for the clinicians• Human readable rules: • To map a domain-specific language to drool rules. 50
  51. 51. EISBM workshop (BDigital)Clinical significance of models• Which are the "outputs" of the models that can be used in the decision support of a clinical task?• Suggested areas: • Screening: • Early prediction 51
  52. 52. EISBM workshop (BDigital)Clinical significance of models• Which are the "outputs" of the models that can be used in the decision support of a clinical task?• Suggested areas: • Assessment: • Better way of assessing COPD: • Via phenotyping • Via new severity indicator (better than GOLD, FEV1, BODE) • Future health status prediction based on current state • Which next tests to do given the current health state of the COPD patient and when? 52
  53. 53. EISBM workshop (BDigital)Clinical significance of models• Which are the "outputs" of the models that can be used in the decision support of a clinical task?• Suggested areas: • Management (treatment): • Predicting the outcomes of different treatment regimes for specific patient profiles: • Long acting Beta2-Agonists • vs. Short acting Beta2-Agonists • vs. Corticosteroids • vs. Combinations • Dosage calculator • Best therapy recommender 53
  54. 54. EISBM workshop (BDigital)Patient data storage and retrieval• Currently the “EHR” is simulated as HL7 XML files in the CDSS.• In a real system of this kind where are the patients personal clinical data that the CDSS needs stored?• Need to integrate our knowledge bases with healthcare institutions’ MHRs 54
  55. 55. Analyzing and integrating probabilistic anddeterministic computational models in Synergy-COPDEISBM workshop, Lyon, June 14, 2012Luigi Ceccaroni and Filip Velickovski(Barcelona Digital Technology Centre, BDigital)Isaac Cano (IDIBAPS)David Gomez-Cabrero (Karolinska Institute)

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