ON THE ONTOLOGY OF DISEASE: part II
The Philosophy of Biology: Structure, Function, Evolution
Louis J. Goldberg
University at Buffalo
October 28, 2006
1
the complexity dilemma
mechanistic explanations cannot deal with the ever unfolding distributed
complexity of biological reality
2
complexity equivalence
all levels of organization follow the same structural pattern in
biological networks (Song et al, 2005).
the base level has equivalent complexity to the level from which it
emerges, and the level which will emerge above
3
networks
and
emergence
hierarchy
granularity
complexity
adaptability
the OBO Foundry
disease
4
the OBO foundry and the formation of a
new network in biology
5
biomedical theories of disease
humoral
miasmatic
germ
mechanistic
??
6
the humoral theory of disease
• biological basis: there are four bodily fluids, or humors;
blood, phlegm, yellow bile, and black bile
• definition of disease: imbalance among the humors causes
disease
7
the miasmatic theory of disease
miasma: a poisonous vapor or mist filled with particles from
decomposed matter (miasmata)
disease: breathing miasma causes disease
be clean and avoid putrescence
8
the germ theory of disease
the modern era begins
biological basis: microorganisms exist in nature
definition of disease: microorganisms invade human bodies
and are the direct cause of infectious diseases
replaces the humoral and miasmatic theories of disease
9
1879
Gonorrhea
1880
1881
Typhoid fever
Suppuration
1882
Tuberculosis
1883
1883
Cholera
Diphtheria
1884
1885
Tetanus
Diarrhea
1886
Pneumonia
1887
Meningitis
1888
Food poisoning
1892
Gas gangrene
Organism
Bacillus anthracis
Staphylococcus
Neisseria
gonorrhoeae
Salmonella typhi
Streptococcus
Mycobacterium
tuberculosis
Vibrio cholerae
Corynebacterium
diphtheriae
Clostridium tetani
Escherichia coli
Streptococcus
pneumoniae
Neisseria
meningitidis
Salmonella
enteritidis
Clostridium
perfringens
1894
Plague
Yersinia pestis
1896
Botulism
1898
Dysentery
1900
Paratyphoid
1877
1878
Year
Disease
Anthrax
Suppuration
Clostridium
botulinum
Shigella
dysenteriae
Salmonella
paratyphi
Treponema
pallidum
Discoverer
Koch, R.
Koch, R.
Neisser, A.L.S.
Eberth, C.J.
Ogston, A.
Koch, R.
Koch, R.
Klebs,T.A.E.
Loeffler, F.
Nicholaier, A.
Escherich, T.
Fraenkel, A.
Weischselbaum,
A.
Gaertner,A.A.H.
Welch, W.H.
Kitasato, S.,
Yersin, A.J.E.
van Ermengem,
E.M.P.
Shiga, K.
Schottmuller, H.
Schaudinn, F.R.,
and Hoffmann, E.
Bordet, J., and
1906
Whooping cough Bordtella pertussis Gengou, O.
Table 4. The discoverers of the main bacterial pathogens. From Brock (1988), p.
290.
1903
Syphilis
10
modern biomedicine
the mechanistic theory of disease
11
in what do working biomedical scientists believe?
is there a philosophy of biomedicine?
biomedical scientists believe:
in cells and the cell doctrine
12
the cell doctrine
(proposed in 1838 by Matthias Schleiden and by Theodor Schwann)
• cells are the fundamental structural and functional units of
all organisms
•
all organisms are composed of one or more cells
• all cells come from preexisting cells
• all vital functions of an organism occur within cells
• cells contain the hereditary information necessary for
regulating cell functions and for transmitting information
to the next generation of cells
13
in what do working biomedical scientists believe?
is there a philosophy of biomedicine?
biomedical scientists believe:
in cells and the cell doctrine
in mechanistic (scientific) explanations of intracellular
function that are based on biochemical/molecular dynamics
that functions and malfunctions at the organism level can
be explained by biochemical dynamics at the
cellular/molecular level
14
traditional biomedicine
is thoroughly mechanism focused
is dominated by notions of molecular causality
molecular mechanisms (operations) at the biochemical
level lead “upward” to the understanding of health and
disease at the organism level
15
the complexity dilemma
16
restlessness in biomedicine
“During the last fifty years the dominant stance in experimental
biology has been reductionism.”
“For the most part, research programs were based on the notion
that genes were in 'the driver's seat' controlling the
developmental program and determining normalcy and disease
(genetic reductionism and genetic determinism).”
“The optimism of molecular biologists, fueled by early success in
tackling relatively simple problems, has now been tempered by
the difficulties found when attempting to understand complex
biological problems.”
Soto AM, Sonnenschein C. J Biosci 2005 Feb:30(1):103-18 Emergentism as a default: cancer as a problem
of tissue organization.
17
the limitations of molecular mechanistic
explanations
“Although molecular biology offers many spectacular
successes, it is clear that the detailed inventory of genes,
proteins, and metabolites is not sufficient to understand the
cell's complexity.”
Zoltan and Barabasi, Systems biology: life’s complexity pyramid. Science (2002) 298: 763-764.
18
a current trend in biomedical research
mechanistic biochemical explanation recedes into the
background
the foreground becomes occupied by newly discovered
components involved in unsuspected operations
19
MOVEMENT IN BIOMEDICINE TOWARDS A NEW
PERSPECTIVE ON HEALTH AND DISEASE
20
studies of mechanisms and mechanistic explanations at the
molecular level continue
a new omic entity comes into being
network theory is applied to the biomedical domain
21
the omic entity
out of the interactions of countless instances of particular types there
emerges a larger entity that acts as a community of instances which
forms new types at a “higher” level of organization
“information storage and processing, and the execution of cell
programs, is related to the distinct levels of omic organization, and
not to the operations of biochemical pathways”
Zoltan and Barabasi, Systems biology: life’s complexity pyramid. Science (2002) 298: 763-764
22
omic levels of organization
ome
component parts
discipline
genome
DNA
genomics
transcriptome
RNA
transcriptomics
proteome
proteins
proteomics
metabolome
metabolites
metabolomics
microbiome
microorganisms
metagenomics
23
G
T
P
this is foundational for all cells: eubacteria, prokaryotes, free-living
eukaryotes and eukaryotes in metazoa
Zoltan and Barabasi, Systems biology: life’s complexity pyramid. Science (2002) 298: 763-764.
24
what does omic thinking add to biomedicine?
it moves from a one-at-a-time mechanistic, test tube view of
molecular chemistry, to a population view
it conceives of the type, genome, interacting with the type,
transcriptome, to produce the type, proteome
the interactome: the entire set of all omic level components
and operations of the cell
25
beyond omics: the network
“…the distinctness of these (the omic) organizational
levels has recently come under fire.”
“…viewing the cell as a network of genes and proteins
offers a viable strategy for addressing the complexity of
living systems.”
Zoltan and Barabasi, Systems biology: life’s complexity pyramid. Science (2002) 298: 763-764.
26
intracellular organization
“There is remarkable integration of the various layers both at
the regulatory and the structural level. Insights into the logic
of cellular organization can be achieved when we view the cell
as a complex network in which the components are connected
by functional links.”
Zoltan and Barabasi, Systems biology: life’s complexity pyramid. Science (2002) 298: 763-764.
27
NETWORKS IN BIOMEDICINE
28
map of the C. elegans interaction network, or "interactome," links
2,898 proteins (nodes) by 5,460 interactions (edges
Li, S. A Map of the Interactome Network of the Metazoan C. elegans. Science (2004) 303: 540 - 543.
29
A Map of the Interactome Network of the Metazoan C. elegans. Science
(2004) 303: 540 - 543.
Li,1* Christopher M. Armstrong,1* Nicolas Bertin,1* Hui Ge,1* Stuart
Milstein,1* Mike Boxem,1* Pierre-Olivier Vidalain,1* Jing-Dong J. Han,1*
Alban Chesneau,1,2* Tong Hao,1 Debra S. Goldberg,3 Ning Li,1 Monica
Martinez,1 Jean-Fran腔is Rual,1,4 Philippe Lamesch,1,4 Lai Xu,5
Muneesh Tewari,1 Sharyl L. Wong,3 Lan V. Zhang,3 Gabriel F. Berriz,3
Laurent Jacotot,1 Philippe Vaglio,1 J Reboul,1 Tomoko HirozaneKishikawa,1 Qianru Li,1 Harrison W. Gabel,1 Ahmed Elewa,1|| Bridget
Baumgartner,5 Debra J. Rose,6 Haiyuan Yu,7 Stephanie Bosak,8
Reynaldo Sequerra,8 Andrew Fraser,9 Susan E. Mango,10 William M.
Saxton,6 Susan Strome,6 Sander van den Heuvel,11 Fabio Piano,12 Jean
Vandenhaute,4 Claude Sardet,2 Mark Gerstein,7 Lynn DoucetteStamm,8 Kristin C. Gunsalus,12 J. Wade Harper,5 Michael E. Cusick,1
Frederick P. Roth,3 David E. Hill,1ｦ Marc Vidal1ｦ#
30
Graphical representation of a highly interconnected subnetwork
31
Li, S. A Map of the Interactome Network of the Metazoan C. elegans. Science (2004) 303: 540 - 543.
TWO EXAMPLES OF RECENT RESEARCH STUDIES
EXEMPLIFYING THE NEW APPROACH:
GOING FROM MOLECULES TO CELLS TO ORGANISMS
Sporns, O., Chialvo, D. R., Kaiser, M. and Hilgetag, C. C. (2004)
Organization, development and function of complex brain networks,
Trends Cogn Sci, 8, 418-25
Andrew J Pocklington, Mark Cumiskey, J Douglas Armstrong and Seth G N
Grant, (2006) The proteomes of neurotransmitter receptor complexes form
modular networks with distributed functionality underlying plasticity and
behaviour, Molecular Systems Biology 2. Published online: 17 January
2006 Article number: 2006.0023
32
the universal nature of networks
the general principles in the structural and functional
organization of complex networks are shared by various
natural, social and technological systems
the interaction of architecture (the network's connection
topology) and dynamics (the behavior of the individual
network nodes), gives rise to global states [new continuants]
and ‘emergent’ behaviors [new occurrents].”
Sporns, O., Chialvo, D. R., Kaiser, M. and Hilgetag, C. C. (2004) Organization, development and function of complex brain
networks, Trends Cogn Sci, 8, 418-25
33
general network characteristics
networks are sets of nodes linked by connections
In many networks, clusters of nodes segregate into tightly coupled
neighborhoods, but maintain very short DISTANCES among nodes across
the entire network, giving rise to a small world within the network.
The degree to which individual nodes are connected forms a distribution
that, for many but not all networks, decays as a power law, producing a
SCALE-FREE architecture characterized by the existence of highly
connected nodes (hubs).
Sporns, O., Chialvo, D. R., Kaiser, M. and Hilgetag, C. C. (2004) Trends Cogn Sci, 8, 418-25.
34
is a physics-biology link on the horizon?
35
Sporn asks
what is the structural substrate of neuroanatomy and how
does it relate to the more dynamic functional and effective
connectivity patterns that underlie human cognition?
Sporn believes that “network analysis offers new fundamental
insights into global and integrative aspects of brain function,
including the origin of flexible and coherent cognitive states
within the neural architecture.”
36
Small-world and scale-free structural and functional brain networks.
37
Sporns, O., Chialvo, D. R., Kaiser, M. and Hilgetag, C. C. (2004) Trends Cogn Sci, 8, 418-25
POSTSYNAPTIC DENSITY (PSD)
post synaptic density-electron dense cytoskeletal specialization located on
the post synaptic membrane at the site of synaptic contact.
38
TEXTBOOK VIEW OF SYNAPSE
Nolte, Human Brain, Fig. 8.4
39
Ziff, Trends Neurosci. 2002 May;25(5):251.
40
the PSD
macromolecular complexes of neurotransmitter receptors
comprised of over 1000 proteins
perhaps the most complex molecular structures known in
mammals
proteins in these structures participate in information
processing in the brain, and also play roles in disease
41
The Pocklington study
determine the organization and function of the mammalian
neurotransmitter receptor complex N-methyl-d-aspartate (NRC/MASC)
using a systems biology approach.
a) use synapse proteomic data to present a detailed analysis of the MASC
complex using annotation, network and statistical approaches.
b) develop a model to explain the structural and functional aspects of
synapse molecular complexity
Pocklington et al., The proteomes of neurotransmitter receptor complexes form modular networks with
42
distributed functionality underlying plasticit an behaviour, Molecular Systems Biology 2. Published
online: 17 January 2006 Article number: 2006.0023
reductionist
methods
bioinformatics/
ontology
graph theory
and network
analysis
Fig. 1 Pocklington et al., The proteomes of neurotransmitter receptor complexes form modular networks with
distributed functionality underlying plasticity and behaviour, Molecular Systems Biology 2. Published online: 17
January 2006 Article number: 2006.0023
43
Modular structure and functional organization of the MASC
(N-methyl-d-aspartate receptor complex)
•
•
•
•
•
•
proteins are clustered
into modules
individual modules play
multiple functional roles
this permits distribution of
information processing and
regulation of effector
pathways over multiple
modules
there is a dynamical
balance between multiple
functional processes
synchronization of multiple
cell-biological processes
induces synaptic plasticity
that is
manifested at a higher
levels of neurological
function through
behavioural learning
Fig. 5 Pocklington et al., The proteomes of neurotransmitter receptor complexes form modular networks with
distributed functionality underlying plasticity and behaviour, Molecular Systems Biology 2. Published online: 17
January 2006 Article number: 2006.0023
44
Network cluster analysis. Clustering of the largest connected component of the
MASC network identified 13 clusters.
50% of its proteins are essential to normal synaptic plasticity;
40% are implicated in schizophrenia; cognitive function
assimilates signals; co-ordinates
common effector mechanisms
45
Ziff, Trends Neurosci. 2002 May;25(5):251.
46
Ziff, Trends Neurosci. 2002 May;25(5):251.
47
HIPPOCAMPUS
48
Nolte Fig 32-15
the behavioral level
cognitive processes
49
what makes biological complexity work?
high quality components (atoms and molecules)
formal language (physical chemistry)
common architecture (physical rules of network assembly)
stability of macro-assemblies (network physics)
because of the above, networks can stack (platform stability)
is there semantic interoperability in biological networks? a) at
a single level: the GTP? b) between levels: PSD and behavior?
50
weakness in biological complexity?
network multiplication: large systems (the human organism) are composed
of multiple, semi-independent networks
it becomes difficult to maintain the coordination of marginally connected
networks (e.g. metagenomics: the interface between the microbial genome
and the organism’s cell genome)
destructive competition between networks can occur
51
the OBO foundry
a family of interoperable gold standard biomedical reference
ontologies to serve the annotation of inter alia



scientific literature
model organism databases
clinical trial data
52
THE OBO FOUNDRY
undergoing reform
Gene Ontology (GO)
Chemical Ontology (ChEBI)
Cell Ontology (CL)
Foundational Model of Anatomy (FMA)
Phenotype Quality Ontology (PaTO)
Sequence Ontology (SO)
new
Common Anatomy Reference Ontology (CARO)
Clinical Trial Ontology (CTO)
Functional Genomics Investigation Ontology (FuGO)
Protein Ontology (PrO)
RNA Ontology (RnaO)
Relation Ontology (RO )
under consideration
disease ontology (DO)
biomedical image ontology (BIO)
upper biomedical ontology (OBO UBO)
environmental ontology (EnvO)
systems biology ontology (SBO)
53
criteria
a common formal language
for any particular domain, there is community convergence on a single
controlled vocabulary.
the ontology has a clearly specified and clearly delineated content
common architecture: The ontology uses relations which are unambiguously
defined following the pattern of definitions laid down in the OBO Relation
Ontology
the developers of each ontology commit to its maintenance in light of
scientific advance: annotation
54
disease ontology (DO)
does not belong in the OBO foundry
we do not now have a unified theory of disease
there can only be the category, ontologies of disease, under which are
listed the ontologies of specific diseases (e.g. multiple sclerosis MSO)
if the OBO foundry is constructed properly, then, as our understanding of
disease changes, old names can disappear (in ten years, there may not be a
disease we call MS) and new ones appear
permitting the foundry to persist
55
the structural organization of ontologies in the foundry
is nonexistent
which makes it all the more crucial that the criteria be met
the boundaries between ontologies will reconfigure (self-assemble) as our
understanding of biology changes
56
tracking emerging organization and information flows within
the network of ontologies
will they conform to the principles of organization and function found in other
complex structures?
will we be able to identify nodes, modules, networks, etc?
57
complexity and the OBO foundry
is it appropriate to think of the suite of interoperable biomedical
ontologies currently being forged in the OBO foundry as an evolving
network?
will the network “give rise to global states and ‘emergent’ behaviors” the
nature of which are unpredictable?
58
formation of a new network
will creation of massively interoperable biomedical ontologies that are
semantically interoperable with human minds be the equivalent of the
creation of a new network in nature not unlike those that currently
exist in biological organisms?
is semantic interoperability to be found in the interaction of the
human cognitive network with the computer-based foundry
network which it is constructing?
will the network “give rise to global states and ‘emergent’
behaviors” the nature of which are unpredictable?
59
human understanding
semantic interoperability
biological reality
Foundry network
computer systems
biomedical science
ontology
biochemistry
primary scientific lit
network analysis
annotation
60