Outline. Terminologies and Ontologies. Communication and Computation. Communication. Outline. Terminologies and Vocabularies.

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1 Page 1 Outline 1. Why do we need terminologies and ontologies? Terminologies and Ontologies Iwei Yeh yeh@smi.stanford.edu 04/16/ Controlled Terminologies Enzyme Classification Gene Ontology 3. Ontologies Definition Representations 4. Example Ontologies RiboWeb EcoCyc TAMBIS Peleg Process Model Communication and Computation Communication (conveying the meaning we intend): lots of biological and medical knowledge to be communicated Computation: making use of background knowledge in computational tasks organizing information meaningfully to help find significant relationships creating data structures for powerful algorithms Communication In order to communicate effectively we need: common language common understanding Example: Metabolic Pathways: language: names of products, enzymes, substrates and pathways knowledge: what is a reaction, how do enzymes and substrates participate, what makes a pathway Terminologies and Vocabularies Terminology: set of words, often hierarchical Vocabulary: terminology with definitions, semantics Outline 1. Why do we need terminologies and ontologies? 2. Controlled Terminologies Enzyme Classification Gene Ontology 3. Ontologies Definition Representations 4. Example Ontologies RiboWeb EcoCyc TAMBIS Peleg Process Model

2 Page 2 Enzyme Classification (e.g. EC X) Enzyme Classification First controlled terminology for enzyme function. Maps to function, one gene can have multiple functions. Only covers metabolic enzymes, not other functionality. Other Classification Systems SwissPROT EGAD GenProtEC TIGR role InterPRO Gene Ontology ( Used to classify function in human genome draft. A controlled listing of three types of function: Molecular Function Biological Process Cellular Component Molecular Function <molecular_function ; GO: %anti-toxin ; GO: %lipoprotein anti-toxin ; GO: %anticoagulant ; GO: %antifreeze ; GO: %ice nucleation inhibitor ; GO: %antioxidant ; GO: %glutathione reductase (NADPH) ; GO: ; EC: % flavin-containing electron transporter ; GO: % oxidoreductase\, acting on NADH or NADPH\, disulfide as acceptor ; GO: %thioredoxin reductase (NADPH) ; GO: ; EC: % flavin-containing electron transporter ; GO: % oxidoreductase\, acting on NADH or NADPH\, disulfide as acceptor ; GO: Biological Process <biological_process ; GO: %behavior ; GO: %adult behavior (sensu Insecta) ; GO: %adult feeding behavior ; GO: % feeding behavior ; GO: %response to cocaine ; GO: %chemosensory behavior ; GO: %chemosensory jump behavior ; GO: %proboscis extension reflex ; GO: %feeding behavior ; GO: %adult feeding behavior ; GO: % adult behavior (sensu Insecta) ; GO: %larval feeding behavior ; GO: % larval behavior (sensu Insecta) ; GO:

3 Page 3 Cellular Component <cellular_component ; GO: %cell ; GO: %ascus ; GO: <ascus lipid droplet ; GO: % lipid particle ; GO: <prospore membrane ; GO: % membrane ; GO: <spore wall (sensu Fungi) ; GO: % cell wall (sensu Fungi) ; GO: % extracellular ; GO: <chitosan layer of spore wall ; GO: <dityrosine layer of spore wall ; GO: <inner layer of spore wall ; GO: Current genome annotations Annotation of Human Genome UMLS Metathesaurus and MeSH Unified Medical Language System contains biomedical concepts formed from several different medical vocabularies Medical Subject Headings (MeSH) 15 different axes indexers at the NLM assign MeSH headings to articles

4 Page 4 Outline 1. Why do we need terminologies and ontologies? 2. Controlled Terminologies Enzyme Classification Gene Ontology 3. Ontologies Definition Representations 4. Example Ontologies RiboWeb EcoCyc TAMBIS Peleg Process Model What is an ontology? A formal specification of the conceptualization of some domain of discourse. What are the concepts and how do they interact? Concepts: represent a set of objects with common properties example: enzymatic reaction catalyzed by enzyme produces substrates characterized by rate constant Methods of Representing Ontologies Frames (Minsky, 1987): a concept is represented by a frame Frame: Enzymatic Reaction Slot: Enzyme of Type Protein Slot: Substrates of Type Chemical Slot: rate constant of type Number Hierarchical structure, attributes and relationships. α α Description Logics: using a subset of first order logic, we represent concepts with sets of axioms a, b, are variables which can be T or F (and) a b (or) a b (equivalence) a b ( a b) (implies) a b Satisfiability: sometimes true Validity: always true Entailment: KB = α α is true in all possible worlds of KB Predicates where arguments are constants: Female (Joan) Mother(Joan, Paul) Mother(A,B) Female (A) :Valid Overall goal: given a KB (set of axioms) is a given axiom entailed? Satisfiable? Can create a model of a system and see if it is consistent (satisfiable?) Why use frame-based ontologies? These are data structures. Data structures exist to facilitate the creation of algorithms that are effective. Ontologies should enable powerful applications to be built.

5 Page 5 Why use description logic ontologies? Expressive. Well formulated solutions for decision problems. Compositional and Dynamic. Outline 1. Why do we need terminologies and ontologies? 2. Controlled Terminologies Enzyme Classification Gene Ontology 3. Ontologies Definition Representations 4. Example Ontologies RiboWeb EcoCyc TAMBIS Peleg Process Model Raw data in this form not useful for computers. Large data set summarized graphically. Powers & Noller 400 observations on RNA Is sufficient to compute a low resolution model. A-priori-target Footprinting-Agent Protecting-Moiety Footprinting-Strength Protected-part

6 Page 6 Powers Paper, Sample Data A-priori-target: Intact 30S subunits Footprinting-agent: Hydroxyl radical Protecting-moiety: S2 Protected-part: Base 698 Footprinting-strength: Strong

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8 Page 8 EcoCYC (Karp et al) An ontology-based environment for representing metabolic compounds, reactions and pathways. Careful representation of these allows for: Graphical display Query Building new databases by analogy A Reaction in EcoCyc EcoCyc Ontology TAMBIS: Transparent Access to Multiple Biological Information Sources Bioinformatics 16(2), p 184-5, 2000

9 Page 9 TAMBIS Integration Tool Biological Ontology for TAMBIS (Bioinformatics 15 (6) p 510, 1999) Example Concepts from TAMBIS Ontologies for Biological Processes and Functions Not just controlled terminology Need to represent sequential aspects Need to represent temporal aspects Need to represent hierarchical decomposition of processes Need to be able to attach quantitative information

10 Page 10 Modeling biological processes using workflow and Petri net models Mor Peleg, Iwei Yeh, and Russ Altman BMI 210A Components of a biological model Biological process Molecular function Proteolysis Transport Gene regulation Gene products Cellular location Sequence components DB entries 3 aspects of a biological system Dynamic aspect Static-structural (concept model) Biological concepts (e.g., protein, cellular component) Relationships gene gene product protein complex protein sub-units protein cellular location Functional Biological reactions Dynamic substrate catalyst Reaction Inhibitor product F1 is before F2 F4 is done in parallel to {1,2,3} If we knockout or inhibit F4, can we get from A to B? Molecular function 1 Molecular function 2 Molecular function 3 State A AND Molecular function 4 State B Desired properties of a biological processes model 3 aspects of a biological system Biological concept model Graphical representation Hierarchical model (complexity) Formal semantics to verify correctness Biological queries (reasoning) Proteins that have the same (diff.) functional roles, scoped to cellular location Reactions that have same substrates, products, inhibitors All processes that are a kind of Adhesion Mapping business workflow to biological systems Business Workflow model Process model Organizational model Organizational Unit (Medical School) member Role Human (Dean) Biological Process Model Process model Structural model Biomolecular complex (Replication complex) member Biopolymer (Helicase) Role (DNA unwinding)

11 Page 11 Graphic dynamic & functional model Mapping to Petri Nets Explicitly represent states Verification of properties liveness, safety, soundness Reasoning on dynamics without t1, can we reach P4 from P1? P1 t1 P2 t2 P3 AND t4 t3 Queries: All processes inhibited by neuraminadase All adhesion activities that occur in erythrocytic plasma membrane P4 AND Conclusions The development of ontologies is linked to the design of associated applications. Ontologies are being used for: Controlled terminologies Standard representation of data Modeling biological processes Thanks They are a stepping stone to full quantitative mathematical models.