The Structure and Plasticity of the Phenotype as a

Network Phenomenon

George Kampis

Basler Chair Spring 2007, ETSU,

Johnson City, TN

Gene environment interaction

• Variability of expression (G)

• Plasticity of development (G x E)

• Variability of phenotype (E)

• Complete model (as large as the world)

• Here one segment, the organism

Phenotype based evolution

Historical/Autobiographical

• Waddington, C.H., ed. 1972. Towards a Theoretical Biology, vols. 1-4. Edinburgh: Edinburgh University Press.

• 1970s-1980s: Biological Systems Theory• S. Kauffman, R. Rosen, H. Pattee, B. Goodwin, S.

Oyama, F.J. Varela…• Ridicule, e.g. • „deconstructivists of the gene” (Dennett 1995: DDI)

Kampis, G. 1991: Self-Modifying Systems: A New Framework for Dynamics, Information, and Complexity, Pergamon, Oxford-New York, pp 543+xix.

A down-to-Earth picture

Genes• 1970-1980: 10-100 million genes• 1985: 1 million genes• Human Genome Project: 100,000 genes• 2001: 30,000 genes• 2003: 20,000 genes

– Out of which Drosophila alone has 5,000

Gene products (PIM)• 1980-85: 1,000- x,000 E.Coli• 2003: 20,000 Drosophila• 2007: 35,000 (?) yeast

More on down-to-Earth

time

complexity

Relevant complexity

Brute force

One sentence metaphors…

• Structure Genes

• Structure and function Genes „plus”

• Function Network

Complexity of representation

regulation, pleiotropy,epistasis, etc..

•Numbers shrinking (increasing): proportions change•Transparency disappears

Summary so far

• We are not smarter, we just know more.

Some examples for networks

• Drosophila PIM (2003)

• Yeast PIM (2006)

Causality and explanation

• Event view– If A then B (… if not A not B…) etc

• Contributing causes– If A (and B and C) then B

• Multiple causation– If A (or B or C) then B

• Network causation– If (network ) then B

Network causation

• Not event like (c.f. gravitation, symmetry)

• Not individuated

• Handles (on trait development):– „Classical” (vary nodes: percolation of effects)– „Nonclassical”: network transformations

The organism as a network

10 9 15 24 3 23 4 55 64 23 12 54 67 89 25 39 19 51 43 4 32

•(dyamic) phenotpye vector, PIM and developmental network (map) behind•Representation problem (mixed nodes, mixed edges)•Proteins, properties, …

e.g. blue eye

A unified framework for..

• Gene expression

• Development

• Adaptation

• Learning and environmental induction

• Phenotype plasticity

• …

Genes: your outside is in

• Genes are „hidden” inside net topology

• For phenotype to phenotype interactions

• Most nodes are distant, „nontransparent”

• The whole network is the target of selection

• How does it permit/evokedifferent subnetworks

w/ different propertiesinteractor

replicator

Network transformations

• Growing network: stability, connectivity

• Strong links vs weak links

• Edge removal/addition w. phase transition (e.g. star/SF)

Random vs real networks

Connectivity/stability in ecosystems

• Translates as a diversity/stability problem in ecology• May-Wigner theorem (1971): low connectivity stabilizes• McCann (2000): high diversity/generalist species stabilize

• In cells: e.g. the role of chaperons• Hubs (not the genes ?!)• Topological „side” effects

NaNu in a new skin

• Old question: how much responsibility is exported from G to E (e.g. default envir.)

• New question: how much of the environment effects is internally controlled

• Network props not modifiable are few and far between

Summary

• Not, G, E, or GxE

• But rather x, where x = network topology

• A dominant and independent causal factor

Not considered in this talk

• Modularity (e.g. HOX genes, segments)

• Self organization (e.g. spatial perturbation)

• Hierarchical levels (Cells, Organs, etc.)

• Modes of inheritance and their roles

• … and many other issues

Thank you!