How Philosophy of Science Can Help Biomedical Research

1
How Philosophy of Science Can Help Biomedical
Research

Barry Smith http//ontology.buffalo.edu/smith
2
How to Do Biology across the Genome?
3

• MKVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYED
  EKSGLIKVVKFRTGAMDRKRSFEKVVISVMVGKNVKKFLTFVEDEPDFQG
  GPISKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLFYLNRGYYNELSF
  RVLERCHEIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAP
  YMFLLLHVDELSIFSAYQASLPGEKKVDTERLKRDLCPRKPIEIKYFSQI
  CNDMMNKKDRLGDILHIILRACALNFGAGPRGGAGDEEDRSITNEEPIIP
  SVDEHGLKVCKLRSPNTPRRLRKTLDAVKALLVSSCACTARDLDIFDDNN
  GVAMWKWIKILYHEVAQETTLKDSYRITLVPSSDGISLLAFAGPQRNVYV
  DDTTRRIQLYTDYNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALG
  NSYDAFNHDPWMDVVGFEDPNQVTNRDISRIVLYSYMFLNTAKGCLVEYA
  TFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSRFETDLYESATSEL
  MANHSVQTGRNIYGVDFSLTSVSGTTATLLQERASERWIQWLGLESDYHC
  SFSSTRNAEDV

sequence of X chromosome in bakers yeast
4

• MKVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYED
  EKSGLIKVVKFRTGAMDRKRSFEKVVISVMVGKNVKKFLTFVEDEPDFQG
  GPIPSKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLFYLNRGYYNELS
  FRVLERCHEIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLA
  PYMFLLLHVDELSIFSAYQASLPGEKKVDTERLKRDLCPRKPIEIKYFSQ
  ICNDMMNKKDRLGDILHIILRACALNFGAGPRGGAGDEEDRSITNEEPII
  PSVDEHGLKVCKLRSPNTPRRLRKTLDAVKALLVSSCACTARDLDIFDDN
  NGVAMWKWIKILYHEVAQETTLKDSYRITLVPSSDGISLLAFAGPQRNVY
  VDDTTRRIQLYTDYNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICAL
  GNSYDAFNHDPWMDVVGFEDPNQVTNRDISRIVLYSYMFLNTAKGCLVEY
  ATFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSRFETDLYESATSE
  LMANHSVQTGRNIYGVDSFSLTSVSGTTATLLQERASERWIQWLGLESDY
  HCSFSSTRNAEDVVAGEAASSNHHQKISRVTRKRPREPKSTNDILVAGQK
  LFGSSFEFRDLHQLRLCYEIYMADTPSVAVQAPPGYGKTELFHLPLIALA
  SKGDVEYVSFLFVPYTVLLANCMIRLGRRGCLNVAPVRNFIEEGYDGVTD
  LYVGIYDDLASTNFTDRIAAWENIVECTFRTNNVKLGYLIVDEFHNFETE
  VYRQSQFGGITNLDFDAFEKAIFLSGTAPEAVADAALQRIGLTGLAKKSM
  DINELKRSEDLSRGLSSYPTRMFNLIKEKSEVPLGHVHKIRKKVESQPEE
  ALKLLLALFESEPESKAIVVASTTNEVEELACSWRKYFRVVWIHGKLGAA
  EKVSRTKEFVTDGSMQVLIGTKLVTEGIDIKQLMMVIMLDNRLNIIELIQ
  GVGRLRDGGLCYLLSRKNSWAARNRKGELPPKEGCITEQVREFYGLESKK
  GKKGQHVGCCGSRTDLSADTVELIERMDRLAEKQATASMSIVALPSSFQE
  SNSSDRYRKYCSSDEDSNTCIHGSANASTNASTNAITTASTNVRTNATTN
  ASTNATTNASTNASTNATTNASTNATTNSSTNATTTASTNVRTSATTTAS
  INVRTSATTTESTNSSTNATTTESTNSSTNATTTESTNSNTSATTTASIN
  VRTSATTTESTNSSTSATTTASINVRTSATTTKSINSSTNATTTESTNSN
  TNATTTESTNSSTNATTTESTNSSTNATTTESTNSNTSAATTESTNSNTS
  ATTTESTNASAKEDANKDGNAEDNRFHPVTDINKESYKRKGSQMVLLERK
  KLKAQFPNTSENMNVLQFLGFRSDEIKHLFLYGIDIYFCPEGVFTQYGLC
  KGCQKMFELCVCWAGQKVSYRRIAWEALAVERMLRNDEEYKEYLEDIEPY
  HGDPVGYLKYFSVKRREIYSQIQRNYAWYLAITRRRETISVLDSTRGKQG
  SQVFRMSGRQIKELYFKVWSNLRESKTEVLQYFLNWDEKKCQEEWEAKDD
  TVVVEALEKGGVFQRLRSMTSAGLQGPQYVKLQFSRHHRQLRSRYELSLG
  MHLRDQIALGVTPSKVPHWTAFLSMLIGLFYNKTFRQKLEYLLEQISEVW
  LLPHWLDLANVEVLAADDTRVPLYMLMVAVHKELDSDDVPDGRFDILLCR
  DSSREVGE

5
(No Transcript)
6
Stelzl et al., Cell, 2005
7
network of gene interactions in E. coli
http//moebio.com/santiago/gnom/ingles.html
8
(No Transcript)
9
(No Transcript)
10
what cellular component?
what molecular function?
what biological process?
11
(No Transcript)
12
(No Transcript)
13
The Idea of Common Controlled Vocabularies
GlyProt
MouseEcotope
sphingolipid transporter activity
DiabetInGene
GluChem
14
The Idea of Common Controlled Vocabularies
GlyProt
MouseEcotope
Holliday junction helicase complex
DiabetInGene
GluChem
15
Gene Ontology

• male courtship behavior, orientation prior to
  leg tapping and wing vibration

16
Benefits of GO

• based in biological science
• links data to biological reality
• links people to software
• links data together
• across species (human, mouse, yeast, fly ...)
• across granularities (molecule, cell, organ,
  organism, population)

17
The goal

• all biological (biomedical) research data should
  cumulate to form a single, algorithmically
  processible, whole
• http//obofoundry.org

18
Ontologies already being applied to achieve this
goal

• Sjöblöm T, et al. analyzed 13,023 genes in 11
  breast and 11 colorectal cancers
• GO tells you what is standard functional
  information for these genes
• By tracking deviations from this standard 189
  genes could be identified as being mutated at
  significant frequency and thus as providing
  targets for diagnostic and therapeutic
  intervention.
• Science. 2006 Oct 13314(5797)268-74.

19
Towards Empirical Philosophy

• processualist vs. 3-dimensionalist
• reductionist vs. non-reductionist
• realist vs. nominalist
• If ontologies based on different philosophical
  principles are tested for their utility in
  support of scientific research, which types of
  ontologies will prove most useful?

20

• Some sample ontologies
• Cell Ontology (CL)
• Foundational Model of Anatomy (FMA)
• Environment Ontology (EnvO)
• Gene Ontology (GO)
• Infectious Disease Ontology
• Phenotypic Quality Ontology (PaTO)
• Protein Ontology (PRO)
• RNA Ontology (RnaO)
• Sequence Ontology (SO)

21
(No Transcript)
22
(No Transcript)
23
(No Transcript)
24
(No Transcript)
25
The problem

• High throughput experimentation data is
  meaningless unless the researcher is provided
  with detailed information concerning how it was
  obtained

26

• To make experimental data computationally
  accessible we need ontologies to describe the
  data
• (1) from the point of view of their relation to
  reality
• (2) from the point of view of their relation to
  experiments

27
Three solutions

• The MGED Ontology
• OBI The Ontology for Biomedical Investigations
• EXPO The Experiment Ontology

28
MGED (Microarray Gene Expression Data) Ontology
29
MGED Ontology

• Individual def. name of the individual organism
  from which the biomaterial was derived
• Experiment def. The complete set of bioassays
  and their descriptions performed as an experiment
  for a common purpose. ... An experiment will be
  often equivalent to a publication.

30
MGED Ontology

• Chromosome Def An abstraction used for
  annotation
• Chromosome Def A biological sequence that can be
  placed on an array

31
OBI

• The Ontology for Biomedical Investigations

with thanks to Trish Whetzel and Richard
Scheuermann
32
Purpose of OBI

• To provide a resource for the unambiguous
  description of the components of biomedical
  investigations such as the design, protocols and
  instrumentation, material, data and types of
  analysis and statistical tools applied to the
  data
• NOT designed to model biology

33
Hypothesis

• That it is possible to create ontology resources
  of genuine utility by drawing on logical and
  philosophical principles e.g. pertaining to
  consistency of definitions, avoidance of
  use-mention confusions.

34
OBI Collaborating Communities

• Crop sciences Generation Challenge Programme
  (GCP),
• Environmental genomics MGED RSBI Group,
  www.mged.org/Workgroups/rsbi
• Genomic Standards Consortium (GSC),
  www.genomics.ceh.ac.uk/genomecatalogue
• HUPO Proteomics Standards Initiative (PSI),
  psidev.sourceforge.net
• Immunology Database and Analysis Portal,
  www.immport.org
• Immune Epitope Database and Analysis Resource
  (IEDB), http//www.immuneepitope.org/home.do
• International Society for Analytical Cytology,
  http//www.isac-net.org/
• Metabolomics Standards Initiative (MSI),
• Neurogenetics, Biomedical Informatics Research
  Network (BIRN),
• Nutrigenomics MGED RSBI Group, www.mged.org/Workgr
  oups/rsbi
• Polymorphism
• Toxicogenomics MGED RSBI Group,
  www.mged.org/Workgroups/rsbi
• Transcriptomics MGED Ontology Group

35
OBI Tools and Documentation

• Open source, standards compliant and version
  management
• Ontology Web Language (OWL) using Protégé editor
• OBI.owl files are available from the OBI SVN
  Repository

36
The Problem of Clinical Investigations

• Regulatory bodies such as the FDA need to assess
  the evidentiary value of enormous volumes of data
  collected e.g. in trials on specific drug
  formulations
• For this, they need to impose standardization of
  terminologies used to express these data, e.g. as
  developed by the Clinical Data Interchange
  Standards Consortium (CDISC)

37
(No Transcript)
38
Clinical Investigations terminologies
39
Study Design

• Descriptive research
• Case study description of one or more patients
• Developmental research description of pattern
  of change over time
• Qualitative research gathering data through
  interview or observation
• Exploratory research
• Secondary analysis exploring new relationships
  in old data
• Historical research reconstructing the past
  through an assessment of archives or other
  records
• Experimental research
• Randomized clinical trial
• Meta-analysis statistically combining findings
  from several different studies to obtain a
  summary analysis

40
Population

• Recruited population
• Randomized population
• Eligible population
• Screened population
• Premature termination population
• Excluded population
• Excluded post-randomization population
• Not-eligible-population
• Analyzed population
• Study arm population
• Crossover population
• Subgroup population
• Intent-to-treat population - based on
  randomization

41
Overview of OCI
42
Development plan (CDISC) Standard operating
procedures (CDISC) Statistical analysis plan
(CDISC)
Meta-analysis (CDISC) Quality assurance
(CDISC) Quality control (CDISC) Baseline
assessment (CDISC) Validation (CDISC) Coding
(MUSC) Permuted block randomization
(MUSC) Secondary-study-protocol
(RCT) Intervention-step (RCT) Blinding-method
(RCT)
Study design
43
Negative findings (MUSC) Positive findings
(MUSC) Primary-outcome (RCT) Secondary-outcome
(RCT)
44
(No Transcript)
45
(No Transcript)
46
EXPO

• The Ontology of Experiments
• L. Soldatova, R. King
• Department of Computer Science
• The University of Wales, Aberystwyth

47
EXPO Experiment Ontology
48
EXPO Experiment Ontology
49
EXPO Experiment Ontology
50
experimental actions part_of experimental
design subject of experiment part_of experimental
design
51
Role of Philosophy of Science
EXPO Experiment Ontology
52
Towards Empirical Philosophy of Science

• rational statistical models of induction
• case-based / domain-based reasoning
• falsifiabilism
• Humeanism vs. laws
• logical, relative frequency, Bayesian, objective
  (chance) and epistemic theories of probability
• These generate different ontologies of
  scientific evidence
• which one is correct?

53

• Environment Ontology
• Phenotypic Quality Ontology
• Ontology for Personalized and Community
• Medicine
• Racial Phenotypes Social, Phylogenetic,
  Essentialistic ...

54

• Ontology for Personalized and Community
• Medicine
• to support studies of differential effects on
  health
• 1. of environmental qualities of different
  neighborhoods
• and
• 2. of different community behavior phenotypes