Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
Formal representation of models in systems biology
1. Formal representation of models in systems biology 1 Michel Dumontier, Ph.D. Associate Professor of Bioinformatics, Department of Biology, School of Computer Science, Institute of Biochemistry, Carleton University Professeur Associé, Département d’informatique et de génielogiciel, Université Laval Ottawa Institute of Systems Biology Ottawa-Carleton Institute of Biomedical Engineering INRIA2011::Dumontier
5. efficient software to execute computationally demanding simulationsISMB2011::Dumontier|Hoehndorf::Formalizing Systems Biology with Biomedical Ontologies 2
6. Repressilator: A self-regulating system 3 INRIA2011::Dumontier A synthetic oscillatory network of transcriptional regulators. Elowitz MB, Leibler S. (2000). Nature 403: 335-338.
18. Are generally used for semantic annotation of data, which when reused, facilitate integration across domains (granularity, species, experimental methods)
20. Can be used to obtain explanations for inferencesdrawn
21. Can be efficiently processed by algorithms and softwareISMB2011::Dumontier|Hoehndorf::Formalizing Systems Biology with Biomedical Ontologies 6
22. Additional annotations are specified using the Resource Description Framework (RDF) Implicit subject and xml attributes <species metaid="_525530" id="GLCi" compartment="cyto" initialConcentration="0.097652231064563"> <annotation> <rdf:RDFxmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:vCard="http://www.w3.org/2001/vcard-rdf/3.0#" xmlns:bqbiol="http://biomodels.net/biology-qualifiers/" xmlns:bqmodel="http://biomodels.net/model-qualifiers/"> <rdf:Descriptionrdf:about="#_525530"> <bqbiol:is> <rdf:Bag> <rdf:lirdf:resource="urn:miriam:obo.chebi:CHEBI%3A4167"/> <rdf:lirdf:resource="urn:miriam:kegg.compound:C00031"/> </rdf:Bag> </bqbiol:is> </rdf:Description> </rdf:RDF> </annotation> </species> The annotation element stores the RDF subject predicate object The intent is to express that the species represents a substance composed of glucose molecules We also know from the SBML model that this substance is located in the cytosol and with a (initial) concentration of 0.09765M ISMB2011::Dumontier|Hoehndorf::Formalizing Systems Biology with Biomedical Ontologies 7
41. discover biological implications inherent in the models and the results of simulations.ISMB2011::Dumontier|Hoehndorf::Formalizing Systems Biology with Biomedical Ontologies 9
42. Approach We formally represent semantically annotated biomodelssuch that it becomes possible to: Capture the semantics of models and the biological systems they represent Check the consistency of biological knowledge represented through automated reasoning query the results of simulations in the context of the biological knowledge 10 INRIA2011::Dumontier
69. in general: P(C1, C2), where P is an OWL axiom (template)ISMB2011::Dumontier|Hoehndorf::Formalizing Systems Biology with Biomedical Ontologies 14
70. INRIA2011::Dumontier 15 Conceptualization:Models and their components represent physical entities (material entities, processes) Formalization: every element E of the SBML language represents a class Rep(E) and we assert that E subClassOf: represents some Rep(E)
71. Top-level ontologies can make additional commitment by enforcing disjointnessamong basic types Material object, Process, Function and Quality are mutually disjoint. ISMB2011::Dumontier|Hoehndorf::Formalizing Systems Biology with Biomedical Ontologies 16
72. Relations impose additional constraints, such that inconsistencies arise when incorrectly used ISMB2011::Dumontier|Hoehndorf::Formalizing Systems Biology with Biomedical Ontologies 17
73. For each model annotation, we make a commitment to what it represents OWL Axiom: Model SubClassOf: represents some MaterialEntity Conversion rule: a Model annotated with class C represents: If C is a SubClassOfMaterialEntity then M SubClassOf: represents some C If C is a SubClassOfFunction then M SubClassOf: represents some (has-function some C) If C is a SubClassOfProcess then M SubClassOf: represents some (has-function some (realized-by only C))
76. F1 is realized by processes of the type heterotrimeric G-protein complex cycleM SubClassOf: represents some O1 O1 SubClassOf: (has-function some (realized-by only GO:0031684)
92. Model verification After reasoning, we found 27 models to be inconsistent reasons our representation - functions sometimes found in the place of physical entities (e.g. entities that secrete insulin). better to constrain with appropriate relations SBML abused – e.g. species used as a measure of time constraints in the ontologies themselves mean that the annotation is simply not possible ISMB2011::Dumontier|Hoehndorf::Formalizing Systems Biology with Biomedical Ontologies 25
93.
94.
95. represents some (has-function some C) and represents some (has-function some (realized-by only C)) are unsatisfiableISMB2011::Dumontier|Hoehndorf::Formalizing Systems Biology with Biomedical Ontologies 27
96. Species are further described with ‘modifiers’ in the context of a reaction 28 INRIA2011::Dumontier essential activator <listOfModifiers> <modifierSpeciesReferencesboTerm="SBO:0000461" species="X"/> </listOfModifiers> partial inhibitor <listOfModifiers> <modifierSpeciesReferencesboTerm="SBO:0000536" species="PX"/> </listOfModifiers>
97. Roles are realized in the context of processes by material entities INRIA2011::Dumontier model sio: has proper part sio: represents sio: has direct part species ChEBI:molecule ChEBI:substance sio: role of SBO: participant role sio: has participant SBMLHarvester+ sio: realizes sio: represents GO:Process reaction Class role chain: realizes o role of -> has participant Individual Datatype 29
98. Semanticscience Integrated Ontology (SIO) OWL2 ontology 1000+ classes covering basic types (physical, processual, abstract, informational) with an emphasis on biological entities 183 basic relations (mereological, participatory, attribute/quality, spatial, temporal and representational) axioms can be used by reasoners to compute inferences for consistency checking, classification and answering questions about life science knowledge embodies emerging ontology design patterns specifies a data model dereferenceable URIs searchable in the NCBO bioportal Available at http://semanticscience.org/ontology/sio.owl 30 INRIA2011::Dumontier
100. SBML Reactions may be specified by mathematical expressions, which contain quantitative variables that denote quantities SBO:Reaction Quantity sio: is specified by sio:denotes sio: has proper part SBO: systems description parameter SBO:mathematical expression sio: has value Literal Class sio:derives from SBMLFarmer Individual Datatype INRIA2011::Dumontier 32
101. When running a simulation, some attributes change with time 33 INRIA2011::Dumontier species double sio:has attribute sio: has value sio: has unit uo:unit attribute sio: measured at sio: has value datetime time sio: result of sio: has agent simulation software sio: conforms to sio: has participant KISAO: algorithm parameter model expression Class Individual Datatype
104. Query Answering over RDF/OWL Find those concentration measurements for species that represent molecular entities that contain ribonucleotide residues ‘concentration’ and (‘measured at’ some double[>20.0, <40.0]) and ‘is attribute of’ some ( ‘species’ and ‘represents’ some (‘has part’ some ‘ribonucleotide residue’) ) 36 INRIA2011::Dumontier ChEBI ontology
112. Can be inflection pointsTEDDY_0000144 Point: Stationary Point (Maximum) B A C Curve Segment Overall Change (Slope) Constituent Points Concentration strictly increasing TEDDY_0000008 strictly decreasing Curve: Overall Change D TEDDY_0000009 Time INRIA2011::Dumontier
113. Queries ‘local maximum’ and ‘is attribute of’ some ( species and represents some ( ‘has function’ some ‘dna binding’ )) 38 INRIA2011::Dumontier Biomodel + UniProt + GO
114. Get the non-monotonic curves for protein species ‘non-monotonic curve’ and ‘has part’ some ( ‘concentration’ and ‘is attribute of’ some ( ‘species’ and ‘represents’ some ‘protein’)) ) 39 INRIA2011::Dumontier
115. Conclusion The SBML-derived ontologies can be i) checked for their consistency, thereby uncovering erroneous curations ii) infer attributes and relations of the substances, compartments and reactions beyond what was originally described in the models iii) answer sophisticated questions across a model knowledge base iv) extended with modifiers, mathematical expressions and parameters, simulation Results (from tab files) to answer questions about simulation results with reference to the semantic annotations (GO) in biomodels, UniProt 40 INRIA2011::Dumontier