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Bio-Ontologies:

a New Means of Travelfor Biological Facts

DMSR

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Outline

The role of Bio-Ontologies [BOs] in biological databases

Four interpretive steps in standardization

The epistemic status of BO terms: situating concepts

A new type of theory in biology? Back to Mary Hessesnetwork view

Implications: data traveland use across researchcontexts

Conclusion: on technology and theory-making

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Biological Ontologies [BOs]

Context: Fast accumulation of data on model organisms, esp. genomics

Fragmentation of biology into local epistemic cultures

Common yearning for integrative understanding of organisms

Goal: enhance availabilityand usabilityof data acrossresearch contexts

Means: formal representations of areas of knowledge in which theessential terms are combined with structuring rules that describe the

relationship between the terms. Knowledge that is structured in a bio-ontology can then be linked to the molecular databases (Bard and Rhee 2004)

Precisely defined terms related through DAGs structures

Association of terms with datasets

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.E.g. Gene Ontology:

Precise definition, large set of associated data

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Wnt receptor signaling pathway

Search by GO

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Searchreturnschildren

Sum ofMGIdata

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Returns

set of

genes

annotated

to this

term

Search returnsannotations toterms and sub-

terms (children)

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BO Terms as Standards

Standard = Coordination device facilitatinginterdisciplinary research (Berg 2004)

BO terms as neutral tools for scientific communicationand exchange:

Data are attached to specific BO terms purely for the purposes ofretrieval by biologists interested in investigating the phenomenonto which the term refer

No theoretical interpretation involved: BO terms are broadclassificatory concepts conceived to pass on information

without distorting or interpreting it

However: Interpretation in standardisation is unavoidable(Bowker & Star 1999)

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Interpreting to Standardize: 4 Steps

1. Abstraction processes: Masking, distorting, simplifying oreliminating characteristics of entities to be standardised(data formatting)

2. De-contextualisation processes: Black-boxing specificinterests, methods and goals of producers of data

(non-locality: decoupling marks from provenance)

3. Knowledge-stabilisation processes: Assemble precisedefinitions for each term and relation so as to mirror (whatcurators see as) the consensus in contemporary biology

4. Situating processes: Associate each dataset with aspecific term (and thus a specific phenomenon)

= standardisation processes influence the database usersunderstanding and use of data

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BO Terms as Situating Concepts

Unambiguously defined as referring to specificphenomena (knowledge-stabilisation process)

Through gene annotation, each available dataset isassociated with one or more BO term (situating process).This makes it possible to retrieve data relevant to thephenomena captured by those terms. But also, ..

.. it fixes the biological relevance of data as evidence:BO terms determine the range of phenomena to beresearched by reference to each dataset

BO terms are situating concepts = they determine thefuture applicability of data by fixing the research contextsin which data can be of use Vs. Unifying or explanatory concepts: do not aim at explaining

phenomena, but rather at describing a phenomenon so that data

associated with it can easily be retrieved

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Select data about gene product TSK frompublication: Suzuki et al., 2005 Plant Cell Physiol.

46:736-742. TONSOKU Is Expressed in S Phaseof the Cell Cycle and Its Defect Delays Cell CycleProgression in Arabidopsis

Associate with term G2/M transition of mitoticcell cycle, which is defined as progression fromG2 phase to M phase of the mitotic cell cycle

Looking for data on the mitotic cell cycle,researchers find gene product TSK as relevant tothe G2/M transition

Gene product TSK could be relevant toresearching other parts of the mitotic cell cycle but there is no evidence for this, so the databasedoes not report this possibility

= the biological relevance of dataset Y is restrictedto the phenomenon captured by the term G2/Mtransition, thus excluding other, possibly relevantphenomena

Select data from

publication or

repository

Associate data

with GO term

GO term refers to

phenomenon X

Data are situated

as relevant to

phenomenon X

and not to other

phenomena

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A New Type of Theory in Biology?

Mary Hesses three criteria:

1. Network of concepts: Situating concepts rather than unifying or

explanatory concepts

2. Observational and theoretical language Concepts are primarily meant to refer to existing

phenomena: mix of observational and theoretical

3. Internal coherence and economy: Consistency among terms = should not have the

same referents (otherwise redundant/obsolete)

Minimalism = the most useful standards are thosethat consist of the minimal number of the most

informative parameters (Brazma et al 2006, 594)

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Implications

Data travel made easier: Easy retrieval and comparison

Easy to check and form new hypotheses

Relatively simple access skills: IT skills &acquaintance with BOs

What about data use?

Easy retrieval of information about dataprovenance (evidence codes)

BUT users need to be aware of interpretiveprocesses involved in standardization

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Conclusion: When Technology Makes a

Difference to Theory-Making

Digital technology does not guarantee objectivity:

Curators estimate the biological relevance of data asevidence for phenomena

Curators define situating concepts

Yet, technology efficiently mediates between different(local) expertises:

Integration of data from various sources

Opportunity for comparisons and queries

Differential access to information depending onexpertise: layers of complexity and detail reachablethrough a mouse click

BOs & bioinformatics: towards integration without unification?

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Abstract

Bio-ontologies are often presented as a neutral tool for the diffusionof facts about organisms to biologists: that is, as a way tostandardise the terminology and relations among terms used todescribe biological processes, so that the immense amount of(especially microbiological) data recently accumulated on variousaspects of the main model organisms can be brought together andmade accessible to the whole biological community. In this paper, I

argue that bio-ontologies are not a neutral vehicle for the diffusion ofevidence. Rather, they constitute a new type of biological theory,incorporating a specific perspective on biological phenomena,through which data are re-interpreted in order to fit specific researchgoals. Notably, one of these goals consists of integrating theavailable knowledge about various aspects of any organisms into anoverall understanding of their biology. The main issues that I shall

address in this paper are thus the following: how well do biologicalfacts circulate through bio-ontologies? How effective is the use ofbio-ontologies towards obtaining integration in biology? And whatkind of integration is that is it actually possible to distinguish it froma kind of theoretical unification? In addressing these questions, Ifocus on the use of one of the bio-ontologies, the so-called GeneOntology, to structure and display data about Arabidopsis thaliana

within The Arabidopsis Information Resource.

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No

associated

data!

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