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Facilitated variation: how evolution learns from past environments to generalize to new environments.

Parter M, Kashtan N, Alon U - PLoS Comput. Biol. (2008)

Bottom Line: The mechanisms for such enhanced generation of novelty (generalization) are analyzed, as is the way that organisms store information in their genomes about their past environments.Elements of facilitated variation theory, such as weak regulatory linkage, modularity, and reduced pleiotropy of mutations, evolve spontaneously under these conditions.Thus, environments that change in a systematic, modular fashion seem to promote facilitated variation and allow evolution to generalize to novel conditions.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.

ABSTRACT
One of the striking features of evolution is the appearance of novel structures in organisms. Recently, Kirschner and Gerhart have integrated discoveries in evolution, genetics, and developmental biology to form a theory of facilitated variation (FV). The key observation is that organisms are designed such that random genetic changes are channeled in phenotypic directions that are potentially useful. An open question is how FV spontaneously emerges during evolution. Here, we address this by means of computer simulations of two well-studied model systems, logic circuits and RNA secondary structure. We find that evolution of FV is enhanced in environments that change from time to time in a systematic way: the varying environments are made of the same set of subgoals but in different combinations. We find that organisms that evolve under such varying goals not only remember their history but also generalize to future environments, exhibiting high adaptability to novel goals. Rapid adaptation is seen to goals composed of the same subgoals in novel combinations, and to goals where one of the subgoals was never seen in the history of the organism. The mechanisms for such enhanced generation of novelty (generalization) are analyzed, as is the way that organisms store information in their genomes about their past environments. Elements of facilitated variation theory, such as weak regulatory linkage, modularity, and reduced pleiotropy of mutations, evolve spontaneously under these conditions. Thus, environments that change in a systematic, modular fashion seem to promote facilitated variation and allow evolution to generalize to novel conditions.

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Adaptation towards novel modular goals is more rapid in MVG organisms.(A) An example of new-comb goal in the RNA model, where the new goal structure is composed of previously seen sub-structures but in new combinations. (B) Maximal normalized fitness in RNA populations (mean±SE) as a function of generations for new-comb structures. The x-axis is generations, where zero is the point where the goal changes to a new-comb structure. Initial populations are FG-populations evolved toward G1 and MVG populations taken from the end of the last G1-epoch. Data are from 15 simulations for the four new-comb goals of (A). Inset: competition of FG and MVG populations under a new-comb goal (following the method of [44]). Initial populations were composed of equal fractions of FG-populations and MVG populations. Data are from 30 competition runs for each of the four new-comb goals. (C) Novel-module goal in the logic circuit model. In the novel-module goal, one of the 2 XORs or the OR operation was changed into a different 2-inputs Boolean function such as AND, NOR or XOR, not seen in the history of the evolution. (D) Maximal normalized fitness (mean±SE) as a function of generations for novel-module goals in the logic circuit model. At time zero the goal changes to a novel-module goal. Initial populations are as in (B). Data are from 30 simulations in each scenario, for 20 different novel-module goals (listed in Text S1 section 6.4.2). Inset: Competition of FG and MVG organisms in a novel-module environment. Starting populations were composed of equal fractions of FG and MVG populations. Data are from 30 simulations for 20 novel-module goals. (E) Same as in (D) but for non-MVG language goals. Here, the goal is a randomly chosen Boolean function, generated by randomly generating a 4-input 1-output truth table. Goals with a difficulty level similar to that of (D) were chosen, as evaluated (Text S1 section 6.2). Data are for 35 non-MVG language goals. Inset: Competition results as in the inset of (D).
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pcbi-1000206-g006: Adaptation towards novel modular goals is more rapid in MVG organisms.(A) An example of new-comb goal in the RNA model, where the new goal structure is composed of previously seen sub-structures but in new combinations. (B) Maximal normalized fitness in RNA populations (mean±SE) as a function of generations for new-comb structures. The x-axis is generations, where zero is the point where the goal changes to a new-comb structure. Initial populations are FG-populations evolved toward G1 and MVG populations taken from the end of the last G1-epoch. Data are from 15 simulations for the four new-comb goals of (A). Inset: competition of FG and MVG populations under a new-comb goal (following the method of [44]). Initial populations were composed of equal fractions of FG-populations and MVG populations. Data are from 30 competition runs for each of the four new-comb goals. (C) Novel-module goal in the logic circuit model. In the novel-module goal, one of the 2 XORs or the OR operation was changed into a different 2-inputs Boolean function such as AND, NOR or XOR, not seen in the history of the evolution. (D) Maximal normalized fitness (mean±SE) as a function of generations for novel-module goals in the logic circuit model. At time zero the goal changes to a novel-module goal. Initial populations are as in (B). Data are from 30 simulations in each scenario, for 20 different novel-module goals (listed in Text S1 section 6.4.2). Inset: Competition of FG and MVG organisms in a novel-module environment. Starting populations were composed of equal fractions of FG and MVG populations. Data are from 30 simulations for 20 novel-module goals. (E) Same as in (D) but for non-MVG language goals. Here, the goal is a randomly chosen Boolean function, generated by randomly generating a 4-input 1-output truth table. Goals with a difficulty level similar to that of (D) were chosen, as evaluated (Text S1 section 6.2). Data are for 35 non-MVG language goals. Inset: Competition results as in the inset of (D).

Mentions: Within this language, we defined two classes of possible future goals which are novel: (a) New-comb is a goal that presents previously seen subgoals but in a new combination (Figure 6A) (b) Novel-module refers to goals where one of the subgoals is a previously unseen one, while the other subgoals are kept unchanged (Figure 6C). This represents a novelty that is restricted to one of the modules of the goal.


Facilitated variation: how evolution learns from past environments to generalize to new environments.

Parter M, Kashtan N, Alon U - PLoS Comput. Biol. (2008)

Adaptation towards novel modular goals is more rapid in MVG organisms.(A) An example of new-comb goal in the RNA model, where the new goal structure is composed of previously seen sub-structures but in new combinations. (B) Maximal normalized fitness in RNA populations (mean±SE) as a function of generations for new-comb structures. The x-axis is generations, where zero is the point where the goal changes to a new-comb structure. Initial populations are FG-populations evolved toward G1 and MVG populations taken from the end of the last G1-epoch. Data are from 15 simulations for the four new-comb goals of (A). Inset: competition of FG and MVG populations under a new-comb goal (following the method of [44]). Initial populations were composed of equal fractions of FG-populations and MVG populations. Data are from 30 competition runs for each of the four new-comb goals. (C) Novel-module goal in the logic circuit model. In the novel-module goal, one of the 2 XORs or the OR operation was changed into a different 2-inputs Boolean function such as AND, NOR or XOR, not seen in the history of the evolution. (D) Maximal normalized fitness (mean±SE) as a function of generations for novel-module goals in the logic circuit model. At time zero the goal changes to a novel-module goal. Initial populations are as in (B). Data are from 30 simulations in each scenario, for 20 different novel-module goals (listed in Text S1 section 6.4.2). Inset: Competition of FG and MVG organisms in a novel-module environment. Starting populations were composed of equal fractions of FG and MVG populations. Data are from 30 simulations for 20 novel-module goals. (E) Same as in (D) but for non-MVG language goals. Here, the goal is a randomly chosen Boolean function, generated by randomly generating a 4-input 1-output truth table. Goals with a difficulty level similar to that of (D) were chosen, as evaluated (Text S1 section 6.2). Data are for 35 non-MVG language goals. Inset: Competition results as in the inset of (D).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2563028&req=5

pcbi-1000206-g006: Adaptation towards novel modular goals is more rapid in MVG organisms.(A) An example of new-comb goal in the RNA model, where the new goal structure is composed of previously seen sub-structures but in new combinations. (B) Maximal normalized fitness in RNA populations (mean±SE) as a function of generations for new-comb structures. The x-axis is generations, where zero is the point where the goal changes to a new-comb structure. Initial populations are FG-populations evolved toward G1 and MVG populations taken from the end of the last G1-epoch. Data are from 15 simulations for the four new-comb goals of (A). Inset: competition of FG and MVG populations under a new-comb goal (following the method of [44]). Initial populations were composed of equal fractions of FG-populations and MVG populations. Data are from 30 competition runs for each of the four new-comb goals. (C) Novel-module goal in the logic circuit model. In the novel-module goal, one of the 2 XORs or the OR operation was changed into a different 2-inputs Boolean function such as AND, NOR or XOR, not seen in the history of the evolution. (D) Maximal normalized fitness (mean±SE) as a function of generations for novel-module goals in the logic circuit model. At time zero the goal changes to a novel-module goal. Initial populations are as in (B). Data are from 30 simulations in each scenario, for 20 different novel-module goals (listed in Text S1 section 6.4.2). Inset: Competition of FG and MVG organisms in a novel-module environment. Starting populations were composed of equal fractions of FG and MVG populations. Data are from 30 simulations for 20 novel-module goals. (E) Same as in (D) but for non-MVG language goals. Here, the goal is a randomly chosen Boolean function, generated by randomly generating a 4-input 1-output truth table. Goals with a difficulty level similar to that of (D) were chosen, as evaluated (Text S1 section 6.2). Data are for 35 non-MVG language goals. Inset: Competition results as in the inset of (D).
Mentions: Within this language, we defined two classes of possible future goals which are novel: (a) New-comb is a goal that presents previously seen subgoals but in a new combination (Figure 6A) (b) Novel-module refers to goals where one of the subgoals is a previously unseen one, while the other subgoals are kept unchanged (Figure 6C). This represents a novelty that is restricted to one of the modules of the goal.

Bottom Line: The mechanisms for such enhanced generation of novelty (generalization) are analyzed, as is the way that organisms store information in their genomes about their past environments.Elements of facilitated variation theory, such as weak regulatory linkage, modularity, and reduced pleiotropy of mutations, evolve spontaneously under these conditions.Thus, environments that change in a systematic, modular fashion seem to promote facilitated variation and allow evolution to generalize to novel conditions.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.

ABSTRACT
One of the striking features of evolution is the appearance of novel structures in organisms. Recently, Kirschner and Gerhart have integrated discoveries in evolution, genetics, and developmental biology to form a theory of facilitated variation (FV). The key observation is that organisms are designed such that random genetic changes are channeled in phenotypic directions that are potentially useful. An open question is how FV spontaneously emerges during evolution. Here, we address this by means of computer simulations of two well-studied model systems, logic circuits and RNA secondary structure. We find that evolution of FV is enhanced in environments that change from time to time in a systematic way: the varying environments are made of the same set of subgoals but in different combinations. We find that organisms that evolve under such varying goals not only remember their history but also generalize to future environments, exhibiting high adaptability to novel goals. Rapid adaptation is seen to goals composed of the same subgoals in novel combinations, and to goals where one of the subgoals was never seen in the history of the organism. The mechanisms for such enhanced generation of novelty (generalization) are analyzed, as is the way that organisms store information in their genomes about their past environments. Elements of facilitated variation theory, such as weak regulatory linkage, modularity, and reduced pleiotropy of mutations, evolve spontaneously under these conditions. Thus, environments that change in a systematic, modular fashion seem to promote facilitated variation and allow evolution to generalize to novel conditions.

Show MeSH