in Development Evolution and History Manfred D Laubichler Arizona State University Santa Fe Institute Marine Biological Laboratory Max Planck Institute for the History of Science Johns Challenge for Future Work ID: 926626
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Slide1
Extended Evolution:Regulatory Networks and Niche Construction in Development, Evolution and History
Manfred D. LaubichlerArizona State UniversitySanta Fe InstituteMarine Biological LaboratoryMax Planck Institute for the History of Science
Slide2John’s Challenge for Future Work:— How to fill in remaining conceptual gaps between autocatalysis and multiple networks?Or to quote Jorge Wagensberg:— Between an amoeba and man, something must have happened!!!
Slide3Reflections on “The Emergence of Organizations and Markets” from the Perspective of Evolutionary Theory1. A productive case of transdisciplinary exchange2. Needs to based on current (and future) evolutionary theory, not an outdated version
Main Challenges1. For Evolutionary Theory: — Integrating regulatory network and niche construction perspectives; — Integrating mechanisms related to the origin of variation (novelty) with evolutionary dynamics; — developing an adequate conception of history; — developing a unified conception for molecular to cultural and knowledge evolution
2. For P&P:
—
Incorporating developmental and evolutionary conceptions;
—
G
aining a better understanding of the relationships and dynamics between networks and contexts
Slide4The standard historical narrative of Evolutionary BiologyDarwin
Mendel/Morgan &Population GeneticsModern SynthesisEvo
Devo
Common Descent, Natural Selection, Gradualism,
Open Question of Inheritance
Rules of transmission genetics, Physical Basis of Heredity,
Genes as abstractions (factors), statistical approaches,
Open
Questions related to effects of genes (other than statistical)
Common explanatory framework:
(adaptive) dynamics of populations are the primary
explanation for phenotypic evolution
, developmental mechanisms are
secondary (complexity of the genotype-phenotype map)
Dynamics of Alleles connected to
Adaptation and Speciation;
Simple Genotype-Phenotype Map
Gradualism
Complex GT—PT Map, constraints, conservation, comparison
“to complete the Modern Synthesis”
Slide5An alternative history of Developmental EvolutionDarwin
Boveri, Cell Biology &EntwicklungsmechanikKühn, Goldschmidt &Developmental PhysiologicalGenetics
Regulatory Evolution,
GRNs
& Synthetic
Experimental Evolution
Common Descent, Natural Selection, Gradualism, Open Question of Inheritance,
Developmental Considerations about the Origin of Variation
Role of the Nucleus in Development and Heredity, Experimental Approaches, Speculative Ideas about the
Hereditary Material as a Structured System governing Development
Common explanatory framework:
Mechanistic Explanation of Development and Evolution as primary;
Development as the Origin of Phenotypic Variation, Adaptive Dynamics as
secondary
Physiological Gene Action,
Macroevolution, Gene
Pathways
Slide6The Britten-Davidson Model (1969
)— A conceptual/logical Framework for Developmental Evolution• Logical structure of “regulation of gene activity”• Based on
a hierarchical and functional structure
of the
genome
• Explicit recognition as a
mechanism of phenotypic evolution
• Offered a constructive-mechanistic alternative theory of phenotypic evolution
Open Question:
Specific Structure of the Network
(->experimental challenge)
Slide7Underlying Assumptions in Evolutionary Theory about Phenotypic Evolution: => “Mutations will get you there” => Problem: What is the Effect of a Mutation => Problem: What is the Structure of the Genotype- Phenotype Map
Part of the long quest to understand the origins of variation and the patterns of phenotypic diversity (think body plans)
Slide8ProblemBoth sides in the current debate between the primacy of regulatory or standard adaptive evolution have ample empirical evidence=> This is a debate about epistemology, not data (but data help)
Slide9Measuring Pleiotropy: Mouse Skeletal Characters
Slide10Measuring Pleiotropy: Stickleback Skeletal Characters
Slide11The data on genetic pleiotropysuggest
which, together with over three decades of molecular developmental biology, lead to =>
Slide12Eric Davidson’s Concept of Gene Regulatory Networks
Slide13Gene Regulatory Networks as the Foundation for Developmental Evolution
Process Diagram (from Peter and Davidson 2009)
Slide14The dynamic n-dimensional regulatory genome
Traditional definition: => Genome is often equated with the complete DNA sequenceHowever, => Genome is the entirety of the hereditary information of an organism => heredity involves a whole range of complex regulatory processes and mechanisms (development) => heredity therefore implies the unfolding of the genetic information in space and time during development and evolution (1) the regulatory genome is thus a spatial-temporal sequence of regulatory states
(2) the regulatory genome anchors all other regulatory processes that affect development, heredity and therefore evolution
Slide15Analyzing and Expanding Gene Regulatory Networks
Slide16Sub-circuit Repertoire of Developmental GRNs
Slide17Logic Reconstruction of a Developmental GRN
Slide18The
Developmental
Evolution of
the
S
uperorganism
Slide19A Hierarchical Expansion of the GRN Framework
Developmental Evolution in Social Insects: Regulatory Networks from Genes to Societies
Slide20More than a Century later — Boveri realized“to transform one organism in front or our eyes into another”
Synthetic Experimental Evolution“to mold arbitrary abnormalities intotrue experiments…”• Requires both detailed knowledge AND a clear theoretical framework of developmental evolution
•
Transforms research on phenotypic evolution
=> Comparative GRN research
=> emphasis on the mechanisms of (genomic) regulatory control
=> Experimental intervention (re-constructing
GRNs
)
Erwin and Davidson, 2009
Slide21Novel Computational Possibilities
Slide22Peter et al., 2012
Slide23Peter et al., 2012
Slide24Further development of computational GRN models for multiple systems to: 1. Explore the future evolutionary potential of a given genome based on the introduction of known gain of function elements 2. Reconstruct specific evolutionary trajectories
(=> comparative analysis of GRNs based on phylogenetic hypotheses) 3. Develop predictions of evolutionary transitions (for experimental verification) 4. Further refine the hierarchical expansion of the GRN perspective to include the effects of post-transcriptional and environmental/epigenetic regulatory systems
Future Directions
Synthetic
in
silico
experimental evolution
Slide25Co-evolutionary Dynamics of Biology, Material Culture and Knowledge:
The Neolithic Revolution
Slide26Jared Diamond, et al. Science 300, 597 (2003)Spread of the neolithic
revolution
Slide27Computational History of Science uses a variety of computational tools and techniques to aid historical and philosophical study of the life sciences. The rapidly declining cost of computing power and the increasing availability of both primary and secondary materials in digital formats makes it possible to translate historical and philosophical questions into computationally tractable ones. Computational approaches can range from simple term-frequency analysis of large scientific corpora, to complex reconstructions of the social, material, and conceptual fabrics of scientific fields using both automated and supervised procedures.
Slide281.
2.
3.
Historical settings & relationships
Topology of research literature
Conceptual relationships
Change Over Time
1950
1940
1930
1920
1960
1970
1980
Slide29Computational Analysis of Eric Davidson’s Investigative Pathway
Slide30Cytoscape
616 unique nodes.
1591 edges.
~30% of stage 1 dataset
https://www.youtube.com/embed/Zab15Jga8ro
Text
Slide31Genecology Project
Collaborations among ecological geneticists and evolutionary ecologists surrounding key participants in a controversy over methods for modeling adaptive phenotypic plasticity during the early 1990s. Generated using the Vogon text-annotation and network-building tool. Each relationship is rooted in a precise location in a text stored in the Digital HPS Community Repository. Part of the doctoral dissertation research project, "Ecology, Evolution, and Development: The Conceptual Foundations of Adaptive Phenotypic Plasticity in Evolutionary Ecology." (
http://devo-evo.lab.asu.edu/phenotypic-plasticity
)
Slide32Question: How can we asses the influence of a Research Program?
Slide33Slide34Slide35Closeness Centrality
Slide36Slide37Conclusions1. Innovation/Inventions in CAS are the product of a complex interplay between internal and external conditions (regulatory networks and niche construction)2. The origin of variation (phenotypic of scientific) is a consequence of changes to the (extended) complex regulatory networks that govern CAS
3. These isomorphic properties enable a transfer of both concepts and methods between different fields concerned with innovation4. Extended Evolution is a more adequate mechanistic framework for understanding innovation/invention than simple population dynamics
Slide38AcknowledgmentsFor intellectual discussions/collaborations:Eric DavidsonGünter WagnerJane
MaienscheinRobert PageBert HölldoblerJürgen RennDoug ErwinColin AllenHans-Jörg Rheinberger
Horst
Bredekamp
Olof
Leimar
Sander van
der
Leeuw
Graduate Students:Erick PeirsonKate MacCordGuido
CanigliaYawen Zhou
Lijing JiangNah ZhangSteve ElliottJulia DamerowMark Ulett
For Financial Support:
National Science FoundationStiftung Mercator
Smart Family FoundationMax Planck SocietyWissenschaftskolleg
zu BerlinArizona State University