Extended Evolution - Projects at Harvard

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
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
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outdated version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
The standard historical narrative of Evolutionary Biology
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
Evo Devo
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)
An alternative history of Developmental 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,
Boveri, Cell Biology & Experimental Approaches, Speculative Ideas about the
Entwicklungsmechanik Hereditary Material as a Structured System governing
Physiological Gene Action,
Kühn, Goldschmidt &
Developmental Physiological Macroevolution, Gene
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
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
Open Question: Specific Structure of the Network
(->experimental challenge)
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 GenotypePhenotype Map
Part of the long quest to understand the origins of
variation and the patterns of phenotypic diversity (think
body plans)
Both sides in the current debate between the primacy of
regulatory or standard adaptive evolution have ample empirical
=> This is a debate about epistemology, not data (but data help)
Measuring Pleiotropy: Mouse Skeletal Characters
Measuring Pleiotropy: Stickleback Skeletal Characters
The data on genetic pleiotropy
which, together with
over three decades of
developmental biology,
lead to =>
Eric Davidson’s Concept of Gene Regulatory Networks
Gene Regulatory Networks as the Foundation
for Developmental Evolution
Process Diagram (from Peter and Davidson 2009)
The dynamic n-dimensional regulatory genome
Traditional definition:
=> Genome is often equated with the complete DNA sequence
=> Genome is the entirety of the hereditary information of an
=> 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
Analyzing and Expanding Gene Regulatory Networks
Sub-circuit Repertoire of Developmental GRNs
Logic Reconstruction of a Developmental GRN
The Developmental Evolution of the Superorganism
A Hierarchical Expansion of the GRN Framework
Developmental Evolution in Social Insects: Regulatory Networks from Genes to Societies
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
Erwin and Davidson, 2009
•Transforms research on phenotypic
=> Comparative GRN research
=> emphasis on the mechanisms of
(genomic) regulatory control
=> Experimental intervention (reconstructing GRNs)
Novel Computational Possibilities
Peter et al., 2012
Peter et al., 2012
Future Directions
Synthetic in silico experimental 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
4. Further refine the hierarchical expansion of the GRN perspective to include
the effects of post-transcriptional and environmental/epigenetic regulatory
Co-evolutionary Dynamics of Biology, Material Culture and
The Neolithic Revolution
Spread of the neolithic revolution
Jared Diamond, et al. Science 300, 597 (2003)
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
Computational approaches can range from simple termfrequency analysis of large scientific corpora, to complex
reconstructions of the social, material, and conceptual
fabrics of scientific fields using both automated and
supervised procedures.
Conceptual relationships
Topology of research literature
Historical settings & relationships
Change Over Time
Computational Analysis of Eric Davidson’s Investigative Pathway
616 unique nodes.
1591 edges.
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 textannotation 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://devoevo.lab.asu.edu/phenotypic-plasticity)
Question: How can we asses the influence of a Research Program?
Closeness Centrality
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
For intellectual discussions/collaborations:
Eric Davidson
Günter Wagner
Jane Maienschein
Robert Page
Bert Hölldobler
Jürgen Renn
Doug Erwin
Colin Allen
Hans-Jörg Rheinberger
Horst Bredekamp
Olof Leimar
Sander van der Leeuw
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
Wissenschaftskolleg zu Berlin
Arizona State University

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