Merging and semantics of biochemical models

Transcription

1 Merging and semantics of biochemical models Wolfram Liebermeister Institut für Biochemie, Charite Universitätsmedizin Berlin

2 Bottom up Vision 1: Join existing kinetic models

3 Bottom up Vision 1: Join existing kinetic models Top down Vision 2: Translate large metabolic networks into kinetic models

4 Bottom up

5 Playing Model composition with biochemical models? Model 1 Model 2 Model 3

6 Playing Model composition with biochemical models? Model 1 Model 2 Model 3 Model merging

7 The mother of all nice attempts

8 Your models deserve more than that Most of the published quantitative models in biology are lost for the community because they are either not made available or they are insufficiently characterized to allow them to be reused. Le Novere et al, (2005)

9 <do you speak SBML?/> One exchange format over 200 tools that understand each other <?xml version="1.0" encoding="utf-8"?> <sbml xmlns=" level="2" version="3"> <model id="model" name="model"> <listofcompartments> <compartment id="c" name="c" size="1"/> <compartment id="ext" name="ext" size="1"/> </listofcompartments> <listofspecies> <species id="c00022_c" name="pyruvate" compartment="c"> </species>... <reaction id="reaction_8"> <listofreactants> <speciesreference species="c00022_c" stoichiometry="0.03"/>... <speciesreference species="o2_c" stoichiometry="0.01"/> </listofreactants> <listofproducts> <speciesreference species="c00008_c" stoichiometry="0.81"/>... </listofproducts> <listofmodifiers> <modifierspeciesreference species="enzyme_reaction_8_c"/> </listofmodifiers> </reaction> </listofreactions> </model> </sbml> SBML main site

10 <do you speak SBML?/> One exchange format over 200 tools that understand each other <?xml version="1.0" encoding="utf-8"?> <sbml xmlns=" level="2" version="3"> <model id="model" name="model"> <listofcompartments> <compartment id="c" name="c" size="1"/> <compartment id="ext" name="ext" size="1"/> </listofcompartments> <listofspecies> <species id="c00022_c" name="pyruvate" compartment="c"> </species>... <reaction id="reaction_8"> <listofreactants> <speciesreference species="c00022_c" stoichiometry="0.03"/>... <speciesreference species="o2_c" stoichiometry="0.01"/> </listofreactants> <listofproducts> <speciesreference species="c00008_c" stoichiometry="0.81"/>... </listofproducts> <listofmodifiers> <modifierspeciesreference species="enzyme_reaction_8_c"/> </listofmodifiers> </reaction> </listofreactions> </model> </sbml> SBML main site Database of curated annotated models JWS online: database of curated models Systems biology ontology

11 Identifying corresponding model elements? Metabolic Control Analysis of Glycerol Synthesis in Saccharomyces cerevisiae Garth R. Cronwright et al (2002) Can yeast glycolysis be understood in terms of in vitro kinetiocs? Testing biochemistry. Bas Teusink, et al. Eur. J. Biochem ,

12 Semantic annotations in SBML models SBO term Species called enzyme_r00001 represents an enzyme <species id="enzyme_r00001" sboterm="sbo: "/>

13 Semantic annotations in SBML models Species called enzyme_r00001 represents an enzyme SBO term MIRIAM annotation <species id="enzyme_r00001" sboterm="sbo: "/> Species called ATP represents KEGG C06262 (ATP) <species metaid=".." id="atp" name="atp concentration" compartment="cytosol"> <annotation> <rdf:rdf xmlns:rdf=" xmlns:bqbiol= xmlns:bqmodel=" <rdf:description rdf:about="#metaid_ "> <bqbiol:is> <rdf:bag> <rdf:li rdf:resource="urn:miriam:obo.chebi:chebi%3a15422"/> <rdf:li rdf:resource="urn:miriam:kegg.compound:c00002"/> </rdf:bag> </bqbiol:is> </rdf:description> </rdf:rdf> </annotation>... </species> Species Qualifier Database Identifier "A simple scheme for annotating SBML with references to controlled vocabularies and database entries" Le Novere and Finney, 2005

14 How can HowI can annotate I annotate my models my model? and data? Falko Krause, Humboldt-Universität Berlin

15 Annotation vectors allow to cluster models and biological concepts Concepts (LibSBAnnotation) Models (BioModels) Retrieval, alignment, and clustering of computational models based on semantic annotations. Schulz M., Krause F., Le Novère N., Klipp E., Liebermeister W.(2011) Molecular Systems Biology 7, Article number: 512.

16 Similarity scores for biochemical concepts libsbannotation joins biochemical ontologies... and provides similarity measures for biological concepts (substances, reactions...) Retrieval, alignment, and clustering of computational models based on semantic annotations. Schulz M., Krause F., Le Novère N., Klipp E., Liebermeister W.(2011) Molecular Systems Biology 7, Article number:

17 Elements similarity scores help to align models Hornberg et al., 2005 (BioModel 84) Retrieval, alignment, and clustering of computational models based on semantic annotations. Schulz M., Krause F., Le Novère N., Klipp E., Liebermeister W.(2011) Molecular Systems Biology 7, Article number: 512. Huang and Ferrell, 1996 (BioModel 9).

18 Model similarities: a BLAST for systems biology models Similarity scores are based on MIRIAM-compliant element annotations and libsbannotation ontology Retrieval, alignment, and clustering of computational models based on semantic annotations. Schulz M., Krause F., Le Novère N., Klipp E., Liebermeister W.(2011) Molecular Systems Biology 7, Article number:

19 Model merging and model validity

20 PlayingModel merging: with biochemical models? simple recipe A A v1 V1 B v2 B b + = v1 C V2 c C V3 v2 d D

21 PlayingModel merging: with biochemical models? simple recipe A A v1 V1 B v2 B b + = v1 C V2 c C V3 v2 d D Elements are characterised by: Name "How do you call it?" (need not be compatible between models) Semantics ("annotation") "What does it mean?" (used for matching) Statements: Additional information "What is stated?" (conflicts between models? choose!) (e.g., URN, position in graphics etc)

22 Why models may not fit semantically 1. Independent: completely different quantities, no constraints, no conflict ATP concentration ADP concentration

23 Why models may not fit semantically 1. Independent: completely different quantities, no constraints, no conflict ATP concentration ADP concentration 2. Identical: statement conflict possible; choose between statements ATP concentration ATP concentration

24 Why models may not fit semantically 1. Independent: completely different quantities, no constraints, no conflict ATP concentration ADP concentration 2. Identical: statement conflict possible; choose between statements ATP concentration ATP concentration 3. Interconvertible: need to be converted in advance (afterwards, see identical ) ATP concentration [mm] ATP concentration [M] ATP concentration ATP amount

25 Why models may not fit semantically 1. Independent: completely different quantities, no constraints, no conflict ATP concentration ADP concentration 2. Identical: statement conflict possible; choose between statements ATP concentration ATP concentration 3. Interconvertible: need to be converted in advance (afterwards, see identical ) ATP concentration [mm] ATP concentration [M] ATP concentration ATP amount 4. Semantic overlap: uncontrollable conflicts; automatic merging impossible lumped reaction individual reaction steps ribosome concentration total RNA concentration Semantic relations and conflicts

26 Why models may not fit semantically 1. Independent: completely different quantities, no constraints, no conflict ATP concentration ADP concentration 2. Identical: statement conflict possible; choose between statements ATP concentration ATP concentration 3. Interconvertible: need to be converted in advance (afterwards, see identical ) ATP concentration [mm] ATP concentration [M] ATP concentration ATP amount 4. Semantic overlap: uncontrollable conflicts; automatic merging impossible lumped reaction individual reaction steps ribosome concentration total RNA concentration

27 How How cancan I merge I annotate my SBML my model? models? Falko Krause, Humboldt-Universität Berlin

28 Top down: From metabolic networks to kinetic models

29 Biochemical pathways wall chart

30 Modelling formalisms for biochemical systems Kinetic models A B enzyme concentration C Constraint-based models (e.g., flux balance analysis) enzyme stoichiometry reaction rate parameters Metabolic control theory Systemic effect: flux or concentration Thermodynamic knowledge Slope at standard state = control coefficient Response curve Local cause: e.g., single enzyme level

31 Problems in building large kinetic models 1. Kinetic laws are often unknown Use simple yet plausible standard rate laws

32 Problems in building large kinetic models 1. Kinetic laws are often unknown Use simple yet plausible standard rate laws 2. Models should obey the laws of thermodynamics Be aware of relevant constraints Use independent parameters in fitting, sampling, optimisation etc

33 Problems in building large kinetic models 1. Kinetic laws are often unknown Use simple yet plausible standard rate laws 2. Models should obey the laws of thermodynamics Be aware of relevant constraints Use independent parameters in fitting, sampling, optimisation etc 3. Parameters show variation and may be uncertain Describe parameters by probability distributions Infer probabilistic statements about model outputs, dynamics etc

34 Problems in building large kinetic models 1. Kinetic laws are often unknown Use simple yet plausible standard rate laws 2. Models should obey the laws of thermodynamics Be aware of relevant constraints Use independent parameters in fitting, sampling, optimisation etc 3. Parameters show variation and may be uncertain Describe parameters by probability distributions Infer probabilistic statements about model outputs, dynamics etc 4. Data may not suffice to determine the parameters Use prior distributions and Bayesian statistics for estimation

35 Problems in building large kinetic models 1. Kinetic laws are often unknown Use simple yet plausible standard rate laws 2. Models should obey the laws of thermodynamics Be aware of relevant constraints Use independent parameters in fitting, sampling, optimisation etc 3. Parameters show variation and may be uncertain Describe parameters by probability distributions Infer probabilistic statements about model outputs, dynamics etc 4. Data may not suffice to determine the parameters Use prior distributions and Bayesian statistics for estimation 5. Parameter estimation in large models is expensive Use direct sampling methods that avoid steady-state calculation

36 Thermodynamic constraints

37 Inserting kinetic constants into models Kinetic constants in convenience kinetics Substrate A KM Kcat +/- Reaction x Substrate B KM KM KI Enzyme X Inhibitor I Product C

38 What if kinetic constants do not fit? Kinetic constants in convenience kinetics Substrate A KM Kcat +/- Reaction x Substrate B KM Product C KM KI Enzyme X Substrate A G(0) Substrate B Inhibitor I KM Reaction x K KM M (0) G KI Enzyme X Inhibitor I Product C G(0)

39 Dependence schemes for kinetic constants Kinetic constants in convenience kinetics Substrate A KM Kcat +/- Reaction x Substrate B KM Product C Full dependent set of kinetic constants KM KI Enzyme X Substrate A G(0) Substrate B Choosing the kinetic constants Inhibitor I KM Reaction x K KM M (0) G Product C G(0) Independent parameters for entire model KI Enzyme X Inhibitor I Liebermeister W. and Klipp E. (2006), Theoretical Biology and Medical Modelling 3:42

40 Parameter balancing Convenience kinetics A C B enzyme Thermodynamic balancing Thermodynamic Kinetic Independent parameters

41 Parameter balancing Modelling workflow Convenience kinetics Reaction list A C Structure model MetNetDB B enzyme Kinetic constants (data) Thermodynamic balancing Kinetic constants (balanced) Thermodynamic Kinetic Independent parameters Dynamic model (prior version) Liebermeister and Klipp, Theor. Biol. Med. Mod. (2006) Borger, Uhlendorf, Helbig, and Liebermeister, In silico Biology (2007) Liebermeister, Uhlendorf, Klipp (Bioinformatics, under revision)

42 Steady state elasticity sampling Steady-state elasticity sampling Instead of computing a steady state: Predefine the steady state and sample elasticities from a random distribution Limitation Constraints in reversibe reactions dependencies between elasticities Sampled elasticities may correspond to unfeasible parameter sets Rate EA A B EB Elasticities satisfy

43 Thermodynamically consistent elasticity sampling Elasticity coefficients of modular rate law Can be chosen independently Computable from chemical potentials Thermodynamic elasticity sampling Network structure Fix consistent steady state (fluxes, concentrations, chemical potentials) Sample correlated reaction elasticities Control coefficients 1st and 2nd order Liebermeister, Uhlendorf, Klipp (Bioinformatics, 2010)

44 What is a valid model? Aspects of validity Example validity criteria 1. Syntax Model can be read and processed Liebermeister W. (2008), Validity and combination of biochemical models. correct file format...

45 What is a valid model? Aspects of validity Example validity criteria 1. Syntax Model can be read and processed 2. Computation Model can be used for predictive simulations correct file format... statements unique + complete sequential evaluation possible... Liebermeister W. (2008), Validity and combination of biochemical models.

46 What is a valid model? Aspects of validity Example validity criteria 1. Syntax Model can be read and processed 2. Computation Model can be used for predictive simulations correct file format... statements unique + complete sequential evaluation possible Semantic correctness Model statements agree with the model semantics Liebermeister W. (2008), Validity and combination of biochemical models. valid statements no semantic dependencies...

47 What is a valid model? Aspects of validity Example validity criteria 1. Syntax Model can be read and processed 2. Computation Model can be used for predictive simulations correct file format... statements unique + complete sequential evaluation possible Semantic correctness Model statements agree with the model semantics 4. Empirical correctness Model agrees with physical and biochemical facts Liebermeister W. (2008), Validity and combination of biochemical models. valid statements no semantic dependencies... realistic numerical values correct thermodynamics correct reaction balances,...

48 What is a valid model? Aspects of validity Example validity criteria 1. Syntax Model can be read and processed 2. Computation Model can be used for predictive simulations correct file format... statements unique + complete sequential evaluation possible Semantic correctness Model statements agree with the model semantics 4. Empirical correctness Model agrees with physical and biochemical facts 5. Context Model performs well and suits its purpose Liebermeister W. (2008), Validity and combination of biochemical models. valid statements no semantic dependencies... realistic numerical values correct thermodynamics correct reaction balances,... agreement with data plausible assumptions no irrelevant parts,...

49 My questions.. (.. your answers?) How can we make models reusable (for modification or merging)? What problems are expected when merging existing models? Which possible qualities do we expect from models? Can we formally describe these qualities? What explicit information has to be stored within models?

50 Acknowledgements SemanticSBML HU Berlin Falko Krause Timo Lubitz Jannis Uhlendorf Marvin Schulz!!! The SBML community!!! Financial support by

51 ! u o y k n a h T