Manfred D. Laubichler Arizona State University Santa Fe Institute Marine Biological Laboratory

1. Extended Evolution:Regulatory Networks and Niche Construction 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

2. John’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!!!

3. Reflections on “The Emergence of Organizations and Markets” from the Perspective of Evolutionary Theory 1. A productive case of transdisciplinary exchange 2. Needs to based on current (and future) evolutionary theory, not an outdated version Main Challenges 1. 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; — Gaining a better understanding of the relationships and dynamics between networks and contexts

4. The standard historical narrative of Evolutionary Biology Darwin Common Descent, Natural Selection, Gradualism, Open Question of Inheritance Mendel/Morgan & Population Genetics Rules of transmission genetics, Physical Basis of Heredity, Genes as abstractions (factors), statistical approaches, Open Questions related to effects of genes (other than statistical) Modern Synthesis Dynamics of Alleles connected to Adaptation and Speciation; Simple Genotype-Phenotype Map Gradualism EvoDevo Complex GT—PT Map, constraints, conservation, comparison “to complete the Modern Synthesis” Common explanatory framework: (adaptive) dynamics of populations are the primary explanation for phenotypic evolution, developmental mechanisms are secondary (complexity of the genotype-phenotype map)

5. An alternative history of Developmental Evolution Darwin 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 Boveri, Cell Biology & Entwicklungsmechanik Kühn, Goldschmidt & Developmental Physiological Genetics Physiological Gene Action, Macroevolution, Gene Pathways Regulatory Evolution, GRNs & Synthetic Experimental Evolution Common explanatory framework: Mechanistic Explanation of Development and Evolution as primary;Development as the Origin of Phenotypic Variation, Adaptive Dynamics as secondary

6. The 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)

7. Underlying 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)

8. Problem Both 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)

9. Measuring Pleiotropy: Mouse Skeletal Characters

10. Measuring Pleiotropy: Stickleback Skeletal Characters

11. The data on genetic pleiotropy suggest which, together with over three decades of molecular developmental biology, lead to =>

12. Eric Davidson’s Concept of Gene Regulatory Networks

13. Gene Regulatory Networks as the Foundation for Developmental Evolution Process Diagram (from Peter and Davidson 2009)

14. The dynamic n-dimensional regulatory genome Traditional definition: => Genome is often equated with the complete DNA sequence However, => 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

15. Analyzing and Expanding Gene Regulatory Networks

16. Sub-circuit Repertoire of Developmental GRNs

17. Logic Reconstruction of a Developmental GRN

18. The Developmental Evolution of the Superorganism

19. A Hierarchical Expansion of the GRN Framework Developmental Evolution in Social Insects: Regulatory Networks from Genes to Societies

20. More than a Century later — Boveri realized “to transform one organism in front or our eyes into another” Synthetic Experimental Evolution “to mold arbitrary abnormalities into true 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

21. Novel Computational Possibilities

22. Peter et al., 2012

23. Peter et al., 2012

24. Future Directions Synthetic in silicoexperimental evolution Further 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

25. Co-evolutionary Dynamics of Biology, Material Culture and Knowledge: The Neolithic Revolution

26. Spread of the neolithic revolution Jared Diamond, et al. Science 300, 597 (2003)

27. Computational 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.

28. 3. Conceptual relationships 2. Topology of research literature 1. Historical settings & relationships 1920 1930 1940 1950 1960 1970 1980 Change Over Time

29. Computational Analysis of Eric Davidson’s Investigative Pathway

30. Cytoscape • Text • https://www.youtube.com/embed/Zab15Jga8ro 616 unique nodes. 1591 edges. ~30% of stage 1 dataset

31. Genecology 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)

32. Question: How can we asses the influence of a Research Program?

35. Closeness Centrality

37. Conclusions 1. 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 innovation 4. Extended Evolution is a more adequate mechanistic framework for understanding innovation/invention than simple population dynamics

38. Acknowledgments For intellectual discussions/collaborations: Eric Davidson Günter Wagner Jane Maienschein Robert Page Bert Hölldobler Jürgen Renn Doug Erwin Colin Allen Hans-JörgRheinberger Horst Bredekamp OlofLeimar Sander van derLeeuw Graduate Students: Erick Peirson Kate MacCord Guido Caniglia Yawen Zhou Lijing Jiang Nah Zhang Steve Elliott Julia Damerow Mark Ulett For Financial Support: National Science Foundation Stiftung Mercator Smart Family Foundation Max Planck Society Wissenschaftskollegzu Berlin Arizona State University