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Antagonistic Coevolution Drives Whack-a-Mole Sensitivity in Gene Regulatory Networks.

Shin J, MacCarthy T - PLoS Comput. Biol. (2015)

Bottom Line: Each time sensitive points in the network are mutated, new ones appear to take their place.We have therefore named this phenomenon "whack-a-mole" sensitivity, after a popular fun park game.We predict that this type of sensitivity will evolve under conditions of strong directional selection, an observation that helps interpret existing experimental evidence, for example, during the emergence of bacterial antibiotic resistance.

View Article: PubMed Central - PubMed

Affiliation: Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, United States of America.

ABSTRACT
Robustness, defined as tolerance to perturbations such as mutations and environmental fluctuations, is pervasive in biological systems. However, robustness often coexists with its counterpart, evolvability--the ability of perturbations to generate new phenotypes. Previous models of gene regulatory network evolution have shown that robustness evolves under stabilizing selection, but it is unclear how robustness and evolvability will emerge in common coevolutionary scenarios. We consider a two-species model of coevolution involving one host and one parasite population. By using two interacting species, key model parameters that determine the fitness landscapes become emergent properties of the model, avoiding the need to impose these parameters externally. In our study, parasites are modeled on species such as cuckoos where mimicry of the host phenotype confers high fitness to the parasite but lower fitness to the host. Here, frequent phenotype changes are favored as each population continually adapts to the other population. Sensitivity evolves at the network level such that point mutations can induce large phenotype changes. Crucially, the sensitive points of the network are broadly distributed throughout the network and continually relocate. Each time sensitive points in the network are mutated, new ones appear to take their place. We have therefore named this phenomenon "whack-a-mole" sensitivity, after a popular fun park game. We predict that this type of sensitivity will evolve under conditions of strong directional selection, an observation that helps interpret existing experimental evidence, for example, during the emergence of bacterial antibiotic resistance.

No MeSH data available.


Related in: MedlinePlus

Distribution of sensitivity throughout the network.A) Frequency of being a sensitive interaction in the N×N matrix of interactions (with N = 10) in a typical simulation. From generation 500 onwards we identified the sensitive gene interactions wij (SSij>0), then measured the frequency for each wij being sensitive within the population, at intervals of 50 generations. We sum the frequencies over time and normalize to the interval [0,1] as indicated by the colors. Generally there are no interactions that appear to dominate within each population over many generations. B) Detailed progression of sensitivity over time for two particular interactions in (A). These interactions had the lowest (blue) and highest (pink) overall sensitivity, as indicated by the black squares in (A). C) Distribution of the frequency of being sensitive in all N×N interactions for all host individuals (green dashed line: the host population of (A), red solid line: mean of 100 simulations). Since distributions are mostly right-skewed there are no interactions that dominate in terms of sensitivity. Error bars indicate one SD.
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pcbi.1004432.g004: Distribution of sensitivity throughout the network.A) Frequency of being a sensitive interaction in the N×N matrix of interactions (with N = 10) in a typical simulation. From generation 500 onwards we identified the sensitive gene interactions wij (SSij>0), then measured the frequency for each wij being sensitive within the population, at intervals of 50 generations. We sum the frequencies over time and normalize to the interval [0,1] as indicated by the colors. Generally there are no interactions that appear to dominate within each population over many generations. B) Detailed progression of sensitivity over time for two particular interactions in (A). These interactions had the lowest (blue) and highest (pink) overall sensitivity, as indicated by the black squares in (A). C) Distribution of the frequency of being sensitive in all N×N interactions for all host individuals (green dashed line: the host population of (A), red solid line: mean of 100 simulations). Since distributions are mostly right-skewed there are no interactions that dominate in terms of sensitivity. Error bars indicate one SD.

Mentions: Even though sensitive interactions are labile and are constantly being relocated, we thought there might be a specific subset of interactions with consistently high sensitivity. Alternatively there might be no persistence in the sensitive interactions or any such interactions would be rapidly lost. Consistent with the latter scenario we found there are no interactions with a significantly high frequency of being a persistent sensitive interaction within a population and throughout a simulation, as shown in Fig 4. Fig 4A shows, for a typical simulation, the frequency at which each interaction wij was sensitive over a period of 1500 generations while sensitivity and robustness were at steady state levels. Fig 4B shows the change in sensitivity over time for two particular interactions in Fig 4A (those that had the highest and lowest overall sensitivity respectively). Fig 4C shows the same data in histogram form (green curve) together with the mean value for many simulations (red curve). Even though there appears to be no preference for particular positions within the matrix, we tested whether there was a higher-level preference for particular rows of the interaction matrix W, which represent the cis-regulatory elements for each gene. For this, we considered the total sensitivity score for each row (i), SSi, and in particular, tracked the row imax for which the value of SSi is maximal within each individual (S9A Fig). We found that rarely does a particular imax dominate both the population and throughout generations (S9B Fig). We repeated this analysis for columns, which represent gene outputs regulating genes, finding similar results. Thus, there does not appear to be any predilection for sensitivity to be associated with particular genes.


Antagonistic Coevolution Drives Whack-a-Mole Sensitivity in Gene Regulatory Networks.

Shin J, MacCarthy T - PLoS Comput. Biol. (2015)

Distribution of sensitivity throughout the network.A) Frequency of being a sensitive interaction in the N×N matrix of interactions (with N = 10) in a typical simulation. From generation 500 onwards we identified the sensitive gene interactions wij (SSij>0), then measured the frequency for each wij being sensitive within the population, at intervals of 50 generations. We sum the frequencies over time and normalize to the interval [0,1] as indicated by the colors. Generally there are no interactions that appear to dominate within each population over many generations. B) Detailed progression of sensitivity over time for two particular interactions in (A). These interactions had the lowest (blue) and highest (pink) overall sensitivity, as indicated by the black squares in (A). C) Distribution of the frequency of being sensitive in all N×N interactions for all host individuals (green dashed line: the host population of (A), red solid line: mean of 100 simulations). Since distributions are mostly right-skewed there are no interactions that dominate in terms of sensitivity. Error bars indicate one SD.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4599961&req=5

pcbi.1004432.g004: Distribution of sensitivity throughout the network.A) Frequency of being a sensitive interaction in the N×N matrix of interactions (with N = 10) in a typical simulation. From generation 500 onwards we identified the sensitive gene interactions wij (SSij>0), then measured the frequency for each wij being sensitive within the population, at intervals of 50 generations. We sum the frequencies over time and normalize to the interval [0,1] as indicated by the colors. Generally there are no interactions that appear to dominate within each population over many generations. B) Detailed progression of sensitivity over time for two particular interactions in (A). These interactions had the lowest (blue) and highest (pink) overall sensitivity, as indicated by the black squares in (A). C) Distribution of the frequency of being sensitive in all N×N interactions for all host individuals (green dashed line: the host population of (A), red solid line: mean of 100 simulations). Since distributions are mostly right-skewed there are no interactions that dominate in terms of sensitivity. Error bars indicate one SD.
Mentions: Even though sensitive interactions are labile and are constantly being relocated, we thought there might be a specific subset of interactions with consistently high sensitivity. Alternatively there might be no persistence in the sensitive interactions or any such interactions would be rapidly lost. Consistent with the latter scenario we found there are no interactions with a significantly high frequency of being a persistent sensitive interaction within a population and throughout a simulation, as shown in Fig 4. Fig 4A shows, for a typical simulation, the frequency at which each interaction wij was sensitive over a period of 1500 generations while sensitivity and robustness were at steady state levels. Fig 4B shows the change in sensitivity over time for two particular interactions in Fig 4A (those that had the highest and lowest overall sensitivity respectively). Fig 4C shows the same data in histogram form (green curve) together with the mean value for many simulations (red curve). Even though there appears to be no preference for particular positions within the matrix, we tested whether there was a higher-level preference for particular rows of the interaction matrix W, which represent the cis-regulatory elements for each gene. For this, we considered the total sensitivity score for each row (i), SSi, and in particular, tracked the row imax for which the value of SSi is maximal within each individual (S9A Fig). We found that rarely does a particular imax dominate both the population and throughout generations (S9B Fig). We repeated this analysis for columns, which represent gene outputs regulating genes, finding similar results. Thus, there does not appear to be any predilection for sensitivity to be associated with particular genes.

Bottom Line: Each time sensitive points in the network are mutated, new ones appear to take their place.We have therefore named this phenomenon "whack-a-mole" sensitivity, after a popular fun park game.We predict that this type of sensitivity will evolve under conditions of strong directional selection, an observation that helps interpret existing experimental evidence, for example, during the emergence of bacterial antibiotic resistance.

View Article: PubMed Central - PubMed

Affiliation: Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, United States of America.

ABSTRACT
Robustness, defined as tolerance to perturbations such as mutations and environmental fluctuations, is pervasive in biological systems. However, robustness often coexists with its counterpart, evolvability--the ability of perturbations to generate new phenotypes. Previous models of gene regulatory network evolution have shown that robustness evolves under stabilizing selection, but it is unclear how robustness and evolvability will emerge in common coevolutionary scenarios. We consider a two-species model of coevolution involving one host and one parasite population. By using two interacting species, key model parameters that determine the fitness landscapes become emergent properties of the model, avoiding the need to impose these parameters externally. In our study, parasites are modeled on species such as cuckoos where mimicry of the host phenotype confers high fitness to the parasite but lower fitness to the host. Here, frequent phenotype changes are favored as each population continually adapts to the other population. Sensitivity evolves at the network level such that point mutations can induce large phenotype changes. Crucially, the sensitive points of the network are broadly distributed throughout the network and continually relocate. Each time sensitive points in the network are mutated, new ones appear to take their place. We have therefore named this phenomenon "whack-a-mole" sensitivity, after a popular fun park game. We predict that this type of sensitivity will evolve under conditions of strong directional selection, an observation that helps interpret existing experimental evidence, for example, during the emergence of bacterial antibiotic resistance.

No MeSH data available.


Related in: MedlinePlus