=Paper=
{{Paper
|id=Vol-432/paper-13
|storemode=property
|title=Situational Modeling: Defining Molecular Roles in Biochemical Pathways and Reactions
|pdfUrl=https://ceur-ws.org/Vol-432/owled2008eu_submission_24.pdf
|volume=Vol-432
|dblpUrl=https://dblp.org/rec/conf/owled/Dumontier08
}}
==Situational Modeling: Defining Molecular Roles in Biochemical Pathways and Reactions==
https://ceur-ws.org/Vol-432/owled2008eu_submission_24.pdf
Situational Modeling: Defining Molecular Roles in
Biochemical Pathways and Reactions
Michel Dumontier1,2,3
1
Department of Biology, 2School of Computer Science, 3Institute of Biochemistry,
Carleton University, 1125 Colonel By Drive,
K1S 5B6, Ottawa, Canada
michel_dumontier@carleton.ca
Abstract. Central to a coherent understanding of cellular biology is a faithful
representation of biochemical processes as it pertains to its molecular
participants. Current representations underspecify our knowledge because they
fail to indicate the roles of the molecular components during relevant processes.
Here, we describe a knowledge representation using OWL2 that overcomes
previous limitations in modeling biochemical events and has clear implications
for the accurate functional/role based annotation of molecular components.
Keywords: semantic web, knowledge representation, ontology, life sciences,
OWL-DL, biochemistry.
1 Introduction
Crucial to the success of in silico biology is the development of a comprehensive
biochemical knowledge base (BKB) capable of answering complex questions about
biochemical-related phenomena. To do so, a BKB should exhibit detailed and
accurate knowledge representation (KR) of biochemical events such as energy
generation or signal transduction while identifying the roles contributions from
involved components (from photons to organelles). While numerous biochemical
representations have been put forward over the past two decades to deal with the
exponentially increasing biological knowledge, these neither share a common
conceptualization (ontology) nor adopt a formal representation (syntax and
semantics). Importantly, the functions or roles of molecular components are generally
underspecified because they are either asserted without reference to the relevant
process and hence erroneously appear to occur under any condition, or do not allow
the semantic annotation of the parts of a molecule that are critically involved in the
process. Hence, lack of granularity and incompatible representational diversity
hinders knowledge discovery by increasing the time and effort of data integration,
semantic annotation and subsequent data mining.
To address these issues we present an outline for an expressive biochemical
knowledge representation in the context of recent additions to the Web Ontology
Language (OWL2). This KR is sufficiently developed to capture various aspects of
biochemical reactions by focusing on the roles/functions of molecular participants, at
various levels of processual detail.
Our example system examines the first reaction in glycolysis in liver cells: the
phosphorylation of glucose by the glucokinase enzyme. The reaction involves
glucokinase as the catalyst, glucose and magnesium complexed ATP (Mg2+ATP) as
the reactants and results in the formation of glucose-6-phosphate (G6P) and
Mg2+ADP as the products. During this reaction, the γ phosphate is transferred from
ATP to glucose. The reaction can be written as follows:
GK
Glc + Mg2+ATP Ù G6P + Mg2+ADP GKR
Our goal is to represent this reaction with sufficient knowledge to answer several
questions:
Q1: In which processes does glucokinase play the role of catalyst?
Q2: Glucose is a substrate in which biochemical reactions?
Q3: In which reactions is a phosphate transferred?
Q4: During which process does glucose form part of an enzyme complex?
Q5: What are the products of GKR?
Q6: What is the role of Mg2+?
Q7: From what molecules is G6P derived from?
2 Notation
Ontological entities are denoted using camel case. Class names start with a capital
letter (e.g. Molecule) with boldfaced natural language labels (e.g. molecule or
molecules), properties are italicized and the first letter is lowercase (e.g. hasPart).
Fully defined classes are underlined (e.g. Enzyme). All modeling is at the class level.
Queries are specified using the Manchester OWL syntax.
3 Biochemical Situational Modeling
Situational models represent a situation (an event, a sequence of events or a collection
of events). Situational models consider entities, their qualities, roles and functions, in
the context of temporal and spatial locations. Our representation is inspired by the
Basic Formal Ontology (BFO) [1, 2], although other upper level ontologies GFO[3],
DOLCE[4] have similar philosophies. Common to each is that there exists
continuants, a class of entities that persist in time (e.g. objects, qualities, spatial
regions), and occurrents, a class of entities that extend in time (processes, process
aggregates, temporal intervals). In turn, continuants may be divided into independent
continuants (e.g. objects) and dependent continuants (e.g. qualities, roles). Real
world objects x can be associated with numerous qualities y (e.g. hair color, weight).
Although we would like to qualify the values of qualities with time (e.g. partial
charge y of atom x during process z), OWL currently only allows the expression of
binary relations. Thus, two choices present themselves to describe changing values: 1)
a single instance of the quality could be associated with multiple instances of
observed/measured values, and the latter are associated with an occurrent; 2) each
value is represented by a different quality instance that is associated with the
occurrent. We currently favor the latter approach in our representation, although we
note other efforts to develop a common representation (e.g. Ontology of Biomedical
Investigation).
Function vs Role: An important aspect of situational modeling involves the
contextual realization of functions or roles. The difference between functions and
roles is not particularly obvious in molecular systems, and may in fact be redundant.
For instance, the function of an enzyme is to catalyze a reaction, or more specifically,
to increase the rate of reaction by reducing the activation energy. Every time a protein
executes such functionality, it necessarily realizes the enzyme role. Functionality
appears intrinsic, while roles are extrinsic and context dependent [5]. Functionality is
therefore a kind of default description (e.g. that every enzyme has the function of
catalyzing a reaction), whether they actually do execute this function or not. Most of
our current biochemical knowledge, embodied as functional annotation based on the
Gene Ontology, captures this context independent aspect of functionality. This is
significantly problematic because molecules may exhibit conflicting functionality that
is only executed in different situations. In contrast to views expressed by Arp & Smith
[5], we do not believe that roles should be specified or instantiated unless they are
coupled with the situations in which they are realized. Roles can encompass context-
specific functionality as well as other descriptions in which no functionality is
executed (e.g. a molecule can act as a spectator – by simply being in close proximity
to the reaction). In this paper, we describe molecular situations using roles (see Figure
1 for examples).
Figure 1 Example ontology containing biomolecules and their qualities, roles and processes.
Roleplayers are defined classes for automatic classification based on existential restrictions to
roles.
Roles are realizable dependent entities, that is, they are borne by independent
continuants and are realized by occurrents. Two basic relations connect entities of
these types: realizes(x,y), relating an occurrent x to a realizable entity y, and
hasBearer(x,y), connecting an independent continuant x with a realizable entity y.
Figure 2 shows the relationship between occurrent, realizable entity and independent
continuant, and how it applies to modeling the role of GK in GKR. We can now query
the OWL KB to ask for reactions that have certain participants, and that these
reactions are realizing specific roles, such as the enzyme role (Question 1).
Process that realizes some (EnzymeRole that isRoleOf some Glucokinase)
Figure 2 A) Entities such as functions and roles are realized during processes. Realizable
entities are participants because realizes is a sub-property of hasParticipant. Objects are
inferred to be participants of the process via a realizes ◦ hasBearer role chain. B) The enzyme
role is realized during glucokinase-catalyzed glucose phosphorylation.
Roleplayers: As part of our natural language description of events, we often talk of a
protein being an enzyme, rather than playing the role of an enzyme. OWL provides
the means by which one can fully define the necessary and sufficient conditions for
class membership. A roleplayer is a defined class of entities that must have a relation
to a role as part of the necessary and sufficient conditions. The hasRole(x,y) predicate
defines a relation between an independent continuant and a role, and is a sub-property
of hasBearer. For instance, we define an enzyme as any object that holds at least one
instance of the enzyme role. Having a defined class automatically infers membership
using an OWL reasoner, and makes possible querying the knowledge base for role
holding objects. Thus, we can determine in which reactions is glucose a substrate
(Question 2) by asking:
BiochemicalReaction that hasParticipant some (Glc and Substrate)
3.1 Don’t forget roles for parts!
In biochemistry, functionality is often executed by parts of a molecule. Well
characterized parts are known as functional groups [6], and these also can have
important roles in biochemical events. In our representation, parts may realize roles or
functions by participating in biochemical events (Figure 3). For instance, we might
like to capture the fact that the gamma phosphate of the ATP molecule is transferred
to glucose. Thus, the transfer group role is realized during this process by the
phosphate. We can now ask Q3:
BiochemicalReaction that realizes some (TransferGroupRole that isRoleOf some
Phosphate)
Figure 3 A) Parts of objects can also realize roles during a process. The object whole is
inferred to be a participant of the process via the hasParticipant ◦ isPartOf role chain. B) The
gamma phosphate group of Mg2+ATP bears the role of the transfer group during glucokinase-
catalyzed glucose phosphorylation.
Usefully, any part of a molecule can be semantically annotated as having a function or
role during some biochemical event. Taken together, we represent GKR as a richer
description containing the roles of molecular components in a biochemical reaction
(Figure 4).
Figure 4 Role-based knowledge representation for the glucokinase-mediated phosphorylation of
glucose (GLC) to glucose-6-phosphate (G6P). Various roles are realized (dashed arrow:
realizes) by reaction participants (solid arrow: hasBearer) as the biochemical reaction unfolds.
Glucokinase plays the role of enzyme by lowering the activation energy of the reaction, in
presence of the double charged magnesium ion (Mg2+) co-factor. As a donor, Mg2+ATP
transfers its gamma phosphate to the GLC acceptor which results in the formation of products
Mg2+ADP and G6P (contains the transferred phosphate).
At this point, we identify a critical weakness of OWL in that it cannot easily represent
cyclic class expressions. For instance, we would like to represent that the cofactor role
played by Mg2+ is also part of the Mg2+ATP complex that plays the donor role, where
both roles are realized in GKR (Figure 4). The resulting (partial) class expression fails
to capture this dependency:
GKR ::= Reaction
and realizes exactly 1 (DonorRole that isRoleOf (Mg2+ATP that hasProperPart
exactly 1 Mg2+ that hasRole some (CofactorRole that isRealizedIn some
GKR)))
and realizes exactly 1 (CofactorRole that isRoleOf (Mg2+ that isProperPartOf
exactly 1 (Mg2+ATP that hasRole some (DonorRole that isRealizedIn some
GKR)))
3.2 Event Decomposition
The breakdown of a complex process into simpler events is important in
biochemistry. For instance, the progress of a biochemical reaction can be described by
changes in substrate structure through one or more transition states to finally form the
products. In our knowledge representation (Figure 5), we ensure that knowledge
captured at these finer granular processual parts still relate to the process whole. This
is accomplished to a large part by invoking a hasPart ◦ hasParticipant ->
hasParticipant role chain.
Figure 5 A) Participants of sub-processes are also participants of process wholes using the
hasPart ◦ hasParticipant role chain. B) GKR can be broken down into a number of steps (intial
state, transition state, product formation) to indicate the progression of the chemical reaction.
The decomposition of the reaction mechanism is equally important and is enabled
by this representation. Thus, we can transform XML-based approaches [7] with a
more expressive OWL representation. However, as described above in section 3.1, the
representation requires a structured object rather than tree-like class expression.
3.3 Chemical Persistence and Transformation
Dependent continuants such as qualities, functions and roles act as pivots between
objects and processes, and our knowledge representation ensures that objects persist
with a single identity throughout their lifetime. That is to say, there is no need to
create another distinct instance of the same object in so as to place it in a particular
spatial-temporal context with certain attributes. Much debate in online forums
questions whether the slightest chemical modification leads to creation of an entirely
distinct entity, or whether it is the same entity with some attribute. However, a
fundamental aspect of chemistry is that identity is intrinsically linked to chemical
structure. As such, changes to structure lead to changes in identity.
A biochemical reaction results in the conversion of at least one object into at least one
other different object, represented using the derivesFrom predicate (Figure 6). In
OWL2, we can specify that the same instance cannot derive from itself with the
irreflexive characteristic. We can also specify which molecules can be derived from
by applying a universal restriction on derivesFrom.
Figure 6 A) Derivation is the transformation of objects into new fundamental entities. B) The
fate of chemically modified biochemical species can be captured as a result of chemical
transformations.
The formation of G6P from Glc occurs by preferential binding of glucose followed by
Mg2+ATP [8]. Two representations for this information are shown in Figure 7. The
first representation uses derivesFrom to indicate that the complex is formed from
components. The second representation associates roles of the molecules before the
formation of the complex, and after.
Figure 7 Two representations for the formation of molecular complexes involved in the kinetic
mechanism of Glucokinase-catalyzed glucose phosphorylation. A) The use of a predicate to
relate entities and B) the use of processual classes and roles to indicate complex substrates and
resulting complex products.
While the first provides a temporal progression of species via a predicate, the second
explicitly details the roles of each component at every part of the complex formation.
Therefore, it becomes possible to query the knowledge base with respect to the role of
the participant, such that it becomes possible to find reactions where glucose is a
component in complex formation. We can now during which reaction does glucose
form part of an enzyme complex (Q4):
BiochemicalReaction that hasPart some (ComplexFormation that hasParticipant some
Glucose)
4 Discussion
4.1 What about whether some do?
This class-based representation aims to capture the molecular behavior by assigning
roles during biochemical reactions. While we can ask the knowledge base about any
kind of biochemical reaction, we cannot ask about the roles or participants directly
(Q5-7). That is to say, we would like to learn what we know about a particular
concept – how it is used. In a sense, we would like to ask “are there some glucose that
are substrates?”, rather than “are all glucose substrates?”. We have noticed, however,
that a knowledge base could determine how objects related to the processes, and be
able to answer questions about “some” objects or roles. For instance, from Figure 4,
we know that some Mg2+ bear a co-factor role that is realized in the reaction. Protégé
4 does something akin to this with its “class usage” tab. Thus, this approach could
serve as a portal to querying circumstantial knowledge.
4.2 Comparison with Existing Approaches
Most conceptualization and representation of biochemical knowledge has been as
the result of representing knowledge in relational databases. Enzyme [9], and later
IntEnz [10], describe enzymatic reactions (as a string) primarily based on the
recommendations of the Nomenclature Committee of the International Union of
Biochemistry and Molecular Biology (IUBMB) for which an Enzyme Commission
(EC) number has been assigned. BRENDA [11] is a comprehensive resource on
biochemical reactions and enzyme kinetics for which publication references are given.
English descriptions of mechanistic details along with substrates and corresponding
products, regulation, co-factors, activators, inhibitors, kinetic parameters (km, kcat)
under varying conditions (pH, organism), effective temperature range, tissue/cell
distribution, subcellular localization, complex, and roles in disease. The data is
available under a highly restrictive license. MACIE [12] stepwise describes enzyme
mechanisms in natural language for a wide variety of reactions, and also identifies
over 15 molecular roles. BioCyc use a frame-based representation [13] which links
reactions to enzyme-catalyzed reactions in a relational manner, rather than that of
subsumption. Further, the two sides of the reaction are conceptualized as “left” and
“right” so as to avoid the directionality implied by using “reactants" and “products",
as many biochemical reaction are reversible. While this is true for mass action
kinetics (as opposed to micro-scale particle dynamics), the thermodynamic feasibility
(directionality) of a reaction is captured by the change in Gibbs free energy of the
system (defined by equilibrium between substrates and products) under standard
conditions. Thus, these are not representations of chemical reactions per se, but rather
the end concentrations of substrates and products from collections of billions of
chemical reactions occurring in both the forward and reverse directions.
BioPAX is an OWL-based knowledge representation for biochemical reactions and
pathways developed by a consortium of pathway and interaction databases as well as
interested parties. The development of BioPAX was largely influenced by BioCyc
which is reflected in the data model and property names (e.g. LEFT is the name of the
object property that links an object at the beginning of the biochemical event). Since
enzymes modulate processes, and roles are indicated by predicates “CONTROLLER”
and “CONTROLLED”, respectively, this representation is generally incompatible
with upper level ontologies. BioPAX also fails to capitalize on consistent URI naming
as a means to integrate data, and does not associate related knowledge in a way that
can be reasoned about (imports of controlled vocabularies are only that). Recent
demonstration of the utility of BioPAX data [14] was largely limited by an initial
syntactic matching of contents.
A a simple representation of a biochemical reaction in OWL was put forward as an
n-ary design pattern [15]. Role-based representation is achieved by use of special
predicates (e.g. has_substrate or has_product). This approach leads to a proliferation
of predicates, one for each role, and whose expressivity is limited to available OWL
property characteristics (e.g. transitive, reflexive, irreflexive, functional, inverse
functional, anti-symmetric, disjunction). Clearly, this approach cannot be combined in
such a way to take advantage of OWL’s class constructors (e.g. union, intersection,
negation, cardinality, existential and universal restrictions). Hence, the creation of
sophisticated expressions (e.g. a substrate role, but not an acceptor role) cannot be
realized using predicate expressivity alone.
5 Additional OWL Requirements
5.1 Need for Structured Objects/Description Graphs
Our knowledge representation could benefit from the incorporation of structured
objects (aka description graphs) [16] into OWL. For instance, class-based
representation of the biochemical reaction in Figure 4 and the sequential complex
formation in Figure 7B makes references to objects that are distantly linked in the tree
structure, and are better represented as a structured object. We have also previously
made the case for description graphs in the representation of cyclic molecules [17,
18], which cannot currently be done at the class level with OWL.
5.2 Nonstructural restrictions
We find that the benefits of role chains are challenged by the drawbacks of
nonstructural restrictions on properties, as they can no longer be used to define
cardinality restrictions. While we’ve managed to overcome such problems by
restructuring our representation, it would be infinitely more useful to have a better
explanation of the inconsistencies by OWL reasoners (FaCT++, Pellet).
6 Conclusion
We have presented a rich knowledge representation for biochemical events
compatible with upper level ontology. We use recent additions to the OWL language
to infer relations and facilitate knowledge discovery. We anticipate that the
instantiation of this representation with existing biochemical databases will create
new opportunities for data integration and knowledge discovery.
Acknowledgments: We would like to thank members of BioPAX-OBO, particularly
Alan Ruttenberg, Oliver Ruebenacker, Andrea Splendiani for valuable
discussions during our working group sessions. We thank our anonymous reviewers
for raising issues that have certainly improved the quality of this manuscript. This
work was supported in part by an NSERC Discovery Grant.
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