BioOntologies: a New Means of Travel for Biological Facts

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Bio-Ontologies a New Means of Travel for
Biological Facts
International Society for the History, Philosophy
and Social Studies of Biology Biannual Meeting,
Exeter 25-29 July 2007

• Sabina Leonelli
• Project How Well Do Facts Travel?
• London School of Economics
• s.leonelli_at_lse.ac.uk
• www.sabinaleonelli.org

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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
  Hesses network view
• Implications data travel and use across research
  contexts
• 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 availability and usability of data
  across research contexts
• Means formal representations of areas of
  knowledge in which the essential 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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Search by GO
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Search returns children
Sum of MGI data
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Returns set of genes annotated to this term
Search returns annotations to terms and sub-terms
(children)
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BO Terms as Standards

• Standard Coordination device facilitating
  interdisciplinary research (Berg 2004)
• BO terms as neutral tools for scientific
  communication and exchange
• Data are attached to specific BO terms purely for
  the purposes of retrieval by biologists
  interested in investigating the phenomenon to
  which the term refer
• No theoretical interpretation involved BO terms
  are broad classificatory 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

• Abstraction processes Masking, distorting,
  simplifying or eliminating characteristics of
  entities to be standardised (data formatting)
• De-contextualisation processes Black-boxing
  specific interests, methods and goals of
  producers of data (non-locality
  decoupling marks from provenance)
• Knowledge-stabilisation processes Assemble
  precise definitions for each term and relation so
  as to mirror (what curators see as) the consensus
  in contemporary biology
• Situating processes Associate each dataset with
  a specific term (and thus a specific phenomenon)
• standardisation processes influence the
  database users understanding and use of data

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BO Terms as Situating Concepts

• Unambiguously defined as referring to specific
  phenomena (knowledge-stabilisation process)
• Through gene annotation, each available dataset
  is associated with one or more BO term (situating
  process). This makes it possible to retrieve data
  relevant to the phenomena captured by those
  terms. But also, ..
• .. it fixes the biological relevance of data as
  evidence BO terms determine the range of
  phenomena to be researched by reference to each
  dataset
• BO terms are situating concepts they determine
  the future applicability of data by fixing the
  research contexts in 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 from publication or repository

• Select data about gene product TSK from
  publication Suzuki et al., 2005 Plant Cell
  Physiol. 46736-742. TONSOKU Is Expressed in S
  Phase of the Cell Cycle and Its Defect Delays
  Cell Cycle Progression in Arabidopsis
• Associate with term G2/M transition of mitotic
  cell cycle, which is defined as progression
  from G2 phase to M phase of the mitotic cell
  cycle
• Looking for data on the mitotic cell cycle,
  researchers find gene product TSK as relevant to
  the G2/M transition
• Gene product TSK could be relevant to
  researching other parts of the mitotic cell cycle
  but there is no evidence for this, so the
  database does not report this possibility
• the biological relevance of dataset Y is
  restricted to the phenomenon captured by the term
  G2/M transition, thus excluding other, possibly
  relevant phenomena

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

• Network of concepts
• Situating concepts rather than unifying or
  explanatory concepts
• Observational and theoretical language
• Concepts are primarily meant to refer to existing
  phenomena mix of observational and theoretical
• Internal coherence and economy
• Consistency among terms should not have the
  same referents (otherwise redundant/obsolete)
• Minimalism the most useful standards are those
  that 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 data
  provenance (evidence codes)
• BUT users need to be aware of interpretive
  processes 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 as evidence 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 on
  expertise layers of complexity and detail
  reachable through 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 diffusion of facts about
  organisms to biologists that is, as a way to
  standardise the terminology and relations among
  terms used to describe biological processes, so
  that the immense amount of (especially
  microbiological) data recently accumulated on
  various aspects of the main model organisms can
  be brought together and made accessible to the
  whole biological community. In this paper, I
  argue that bio-ontologies are not a neutral
  vehicle for the diffusion of evidence. 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 research
  goals. Notably, one of these goals consists of
  integrating the available knowledge about various
  aspects of any organisms into an overall
  understanding of their biology. The main issues
  that I shall address in this paper are thus the
  following how well do biological facts circulate
  through bio-ontologies? How effective is the use
  of bio-ontologies towards obtaining integration
  in biology? And what kind of integration is that
  is it actually possible to distinguish it from
  a kind of theoretical unification? In addressing
  these questions, I focus on the use of one of the
  bio-ontologies, the so-called Gene Ontology, to
  structure and display data about Arabidopsis
  thaliana within The Arabidopsis Information
  Resource.

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No associated data!
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