Adaptive Landscapes
in Changing Environments
Sandbox Session
BEACON Congress 2013
Ben Kerr (UW)
fitness
“It may metaphorically be said that natural selection is daily and
hourly scrutinising, throughout the world, the ...
Picking the Wright Metaphor
Wright
• In 1932, Sewall Wright was invited to give a non-
technical talk on his view of evolu...
Evolution in the Balance
Genotype Space
Physical Space
deme 1
deme 2
deme 3
deme 4
• Wright felt that the landscape was li...
Selectively Accessible Paths (to SSWiM Upstream)
• Maynard Smith imagined mutations as
steps on a walk through sequence sp...
Changing Environments: Changing Metaphors?
• The standard incarnation of the metaphor does not emphasize the role of the
e...
Evolution in Changing and Changed Environments
• When the landscape is
rugged (e.g., due to
genetic epistasis), the
popula...
Adapting the Adaptive Landscape
ab
Ab
aB
AB
environment
ab
Ab
aB
AB
environment
ab
Ab
aB
AB
environment
ab AbaB ABAB
• How...
Network Approach: Application to Multi-Drug Resistance
• Let’s consider microbial
evolution under
changing drugs.
• When d...
00 10
01 11
Network Approach: Application to Coevolution
• One of the most important
components of “the
environment” invol...
ISSUES TO DISCUSS
1. RESEARCH DIRECTIONS: What are the most exciting experimental directions to pursue in
exploring evolut...
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Ben Kerr - Adaptive landscapes in changing environments

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Sandbox session from BEACON 2013 Congress

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Ben Kerr - Adaptive landscapes in changing environments

  1. 1. Adaptive Landscapes in Changing Environments Sandbox Session BEACON Congress 2013 Ben Kerr (UW)
  2. 2. fitness “It may metaphorically be said that natural selection is daily and hourly scrutinising, throughout the world, the slightest variations; rejecting those that are bad, preserving and adding up all that are good; silently and insensibly working, whenever and wherever opportunity offers, at the improvement of each organic being in relation to its organic and inorganic conditions of life.” Darwin, The Origin of Species beak depth aB ab AB Ab aB ab AB Ab • Darwin’s “opportunity” depends on the variants present in the population. • The genotype to phenotype (G→P) map carves out the possible. • The genotype to phenotype to fitness (G→P→F) map gives information on evolutionary accessibility and evolutionary constraints. Mapping out Adaptation beak depthaB ab AB Ab
  3. 3. Picking the Wright Metaphor Wright • In 1932, Sewall Wright was invited to give a non- technical talk on his view of evolution at the sixth International Congress of Genetics. • Wright (1932) started with a simple idea: a map from genotype to fitness, where “the entire field of possible gene combinations [could] be graded with respect to adaptive value.” • Thus, a genotype-to-fitness (G→F) map and specification of how genotypes are connected defines an adaptive landscape. Figure 2 from Wright (1932) Fitness
  4. 4. Evolution in the Balance Genotype Space Physical Space deme 1 deme 2 deme 3 deme 4 • Wright felt that the landscape was likely rugged; the problem that occupied him was how a population could move from a lower peak to a higher peak • His shifting balance theory rests on two assumptions: 1. Epistasis leading to distinct “peaks” (rugged landscape) 2. The population is structured (as semi-isolated sparsely populated demes) • Wright’s shifting balance invokes several processes (mutation, selection, drift, and migration): Phase 1: Demes drift over the adaptive landscape Phase 2: Selection drives demes to new peaks Phase 3: Competition between demes where the most fit pulls the metapopulation to its peak • Despite the importance of Wright’s shifting balance to evolution in natural populations, the accompanying metaphor has been very popular.
  5. 5. Selectively Accessible Paths (to SSWiM Upstream) • Maynard Smith imagined mutations as steps on a walk through sequence space. • Think of a game in which one word is changed to another via single letter changes and intermediates are words. • A path (multiple mutational steps) is selectively accessible if each step increases fitness (takes you uphill). • Epistasis occurs when the effect of a mutation changes given different background contexts in either: – Magnitude – Sign • Simple walks along selectively accessible paths occur when: - Selection is strong - Mutation is weak • In “SSWM” conditions, the population can be thought of as a point moving through a directed network, where the topology is dictated by the adaptive landscape. OPA OPO OVA EPA OVO EPO EVA EVO WORD  WORE  GORE  GONE  GENE AT IT ASAT AS ISIS WERD WEIRDWED  WERE  WIRED A T SI wordfreq. genotype space fitness Gavrilet’s (1997) “holey” landscape (necessary for ruggedness) fitness low high TS TS WORDS FROM BEACON PROPOSALS OE Neg. OE Pos. Burch Chao Weinreich
  6. 6. Changing Environments: Changing Metaphors? • The standard incarnation of the metaphor does not emphasize the role of the environment and the role of the evolving organism on the environment. • Partly in response to Wright’s shifting balance process, Fisher argued that environmental change could lead to movement from a former peak. • Wright also acknowledged that environmental change could move populations in genotype space fitness low high OPA OPO OVA EPA OVO EPO EVA EVO WORDS FROM BEACON PROPOSALS OPA OPO OVA EPA OVO EPO EVA EVO WORDS FROM 1950’s NEW ZEALAND OPA OPO OVA EPA OVO EPO EVA EVO WORDS FROM ENVIRONMENTAL LIT
  7. 7. Evolution in Changing and Changed Environments • When the landscape is rugged (e.g., due to genetic epistasis), the population can become trapped on a sub-optimal peak. • A changing environment can move a population to a new peak (perhaps a higher one). • An environment changed by organisms can also move a population to new places. • Coevolution or niche construction can have a diversifying or concentrating effect. reference environment alternate environment reference environment reference environment altered environment reference environment
  8. 8. Adapting the Adaptive Landscape ab Ab aB AB environment ab Ab aB AB environment ab Ab aB AB environment ab AbaB ABAB • How might we visualize evolution under SSWM conditions, but where the environment changes exogenously? • The landscapes can be simplified to directed networks. • The networks can be stacked in an ordered way. • Evolution can be understood as movement down the wall. • Also, epistasis can be read off the wall: – Genetic – Environmental
  9. 9. Network Approach: Application to Multi-Drug Resistance • Let’s consider microbial evolution under changing drugs. • When drugs change, selectively accessible paths can change. • Cycles are thus selectively feasible. - Cycles have been found (Goulart et al. 2013). Miriam Barlow • Possible “evolutionary flow” under multiple drugs is given by connections among the strongly connected clusters in the union of the separate networks. - In the b-lactam data, many (but not all) “sink” clusters are single genotypes. • Accessible paths between two genotypes under multiple drugs can be enumerated. - Certain combinations of drugs exposure may make multi-drug resistance more likely. 000 100 010 001 110 101 011 111 sink connector connector source Number of Accessible Paths Distribution of Sink Sizes size frequency
  10. 10. 00 10 01 11 Network Approach: Application to Coevolution • One of the most important components of “the environment” involves interacting species. • How might we represent “the landscape” for a coevolving pair of species? • One approach could involve consideration of an “expanded genotype”– simply the concatenation of genotypes into a single “super-genotype” • Under SSWM assumptions, coevolution involves movement in different dimensions and epistasis (intra- and inter-) can be gauged. 00 10 01 11 0000 0001 0010 0011 0100 0101 0110 0111 1000 1001 1010 1011 1100 1101 1110 1111
  11. 11. ISSUES TO DISCUSS 1. RESEARCH DIRECTIONS: What are the most exciting experimental directions to pursue in exploring evolution in changing environments? Are there obvious evolution experiments (with microbes, Avidians, etc.) that would contribute to deeper understanding? What are the most pressing theoretical issues to address? What computational/engineering contributions would have the highest impact? Does the form of environmental change (exogenous/endogenous) matter? 2. VISUALIZATION: What are the best ways to visualize evolution in changing environments? Can/should the landscape metaphor be salvaged? Are network approaches valuable (and should we be focusing on static topology or dynamic movement on networks)? How should environments be visually represented? Does it matter if the environment is abiotic/biotic or exogenous/endogenous? Does the best way to visualize adaptation in changing worlds depend on assumptions (e.g., SSWM) of the evolutionary process, and how might it change with different assumptions? 3. BROADER APPLICATIONS: What are the most pressing applications of these topics? Climate change? Antibiotic resistance? Are landscape metaphors (or network approaches) helpful in addressing these issues? What is currently most necessary for understanding evolution in changing environments in a way that has relevance for these broader applications? 4. WRITE-IN: Intersection between evolution and multi-modal optimization problems…

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