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 Year Disease Organism Discoverer 1877 Anthrax Bacillus anthra cis Koch, R. 1878 Suppuration Staphylococcus Koch, R. Neisseria 1879 Gonorrhea Neisser, A.L.S. gonorrhoeae 1880 Typhoid fever Salmo nella typhi Eberth, C.J. 1881 Suppuration Streptococcus Ogston, A. 1882 Tuberculosis Mycobacterium Koch, R. tuberculosis 1883 Cholera Vibrio cholerae Koch, R. 1883 Diphthe ria Corynebacterium Klebs,T.A.E. diphthe riae Loeffler, F. 1884 Tetanus Clostridium tetani Nicholaier, A. 1885 Diarrhea Escherichia coli Escherich, T. Streptococcus 1886 Pneumo nia pneumo niae Fraenkel, A. Neisseria Weischselbaum, 1887 Meningitis meningitidis A. Salmo nella 1888 Food poisoning Gaertner,A.A.H. enteritidis 1892 Gas gangrene Clostridium Welch, W.H. perfringens Kitasato, S., 1894 Plague Yersinia pestis Yersin, A.J.E. Clostridium van Ermenge m, 1896 Botulism botulinum E.M.P. 1898 Dysentery Shigella Shiga, K. dysenteriae 1900 Paratyphoid Salmo nella Schottmuller, H. paratyphi Treponema Schaudinn, F.R., 1903 Syphilis pallidum and Hoffmann, E. Bordet, J., and 1906 Whooping cough Bordtella pertussis Gengo u, O. Table 4. The discoverers of the main bacterial pathoge ns. From Brock (1988), p. 10 290. 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 24 Zoltan and Barabasi, Systems biology: life’s complexity pyramid. Science (2002) 298: 763-764. 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 29 Li, S. A Map of the Interactome Network of the Metazoan C. elegans. Science (2004) 303: 540 - 543. 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 Hirozane- Kishikawa,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 Doucette- Stamm,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 39 Nolte, Human Brain, Fig. 8.4 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 distributed functionality underlying plasticit an behaviour, Molecular Systems Biology 2. Published 42 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 43 distributed functionality underlying plasticity and behaviour, Molecular Systems Biology 2. Published online: 17 January 2006 Article number: 2006.0023 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 44 January 2006 Article number: 2006.0023 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 46 Ziff, Trends Neurosci. 2002 May;25(5):251. 47 Ziff, Trends Neurosci. 2002 May;25(5):251. 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 network analysis primary scientific lit annotation 60
"ON THE ONTOLOGY OF DISEASE"