Evolution of Transcription Networks in Response to Temporal Fluctuations

Roh, Kyoungmin; Safaei, Farshad; Hespanha, João P.; Proulx, Stephen R.

doi:10.1111/evo.12012

Cited by 8 publications

(9 citation statements)

References 31 publications

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“…Finally, environmental variation is also known to result in correlated variation in mean population traits, as natural selection favors different phenotypes over evolutionary time (adaptive tracking) (12). Although an increasing amount of attention has been recently devoted to the conditions that promote these different forms of evolutionary response to environmental variation (hereafter "response modes") (2, 9, 13-18), most studies have considered only one or a small subset of response modes (16,17), and few have explored the general conditions under which one (or more) may be selected above the others (2,18). Addressing these issues will be critical for improving our ability to predict whether and how populations will adapt to both natural and human-induced environmental change.…”

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confidence: 99%

Evolutionary tipping points in the capacity to adapt to environmental change

Botero

Weissing

Wright

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

410

523

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In an era of rapid climate change, there is a pressing need to understand how organisms will cope with faster and less predictable variation in environmental conditions. Here we develop a unifying model that predicts evolutionary responses to environmentally driven fluctuating selection and use this theoretical framework to explore the potential consequences of altered environmental cycles. We first show that the parameter space determined by different combinations of predictability and timescale of environmental variation is partitioned into distinct regions where a single mode of response (reversible phenotypic plasticity, irreversible phenotypic plasticity, bet-hedging, or adaptive tracking) has a clear selective advantage over all others. We then demonstrate that, although significant environmental changes within these regions can be accommodated by evolution, most changes that involve transitions between regions result in rapid population collapse and often extinction. Thus, the boundaries between response mode regions in our model correspond to evolutionary tipping points, where even minor changes in environmental parameters can have dramatic and disproportionate consequences on population viability. Finally, we discuss how different life histories and genetic architectures may influence the location of tipping points in parameter space and the likelihood of extinction during such transitions. These insights can help identify and address some of the cryptic threats to natural populations that are likely to result from any natural or human-induced change in environmental conditions. They also demonstrate the potential value of evolutionary thinking in the study of global climate change.fluctuating selection | global change | phenotypic plasticity | bet-hedging | adaptive tracking

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mentioning

confidence: 99%

Evolutionary tipping points in the capacity to adapt to environmental change

Botero

Weissing

Wright

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

410

523

View full text Add to dashboard Cite

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“…Organisms optimize their growth and thereby their chances of survival by finding a matrix of transition rates that maximizes Λ in fluctuating environments. Predicting this optimum presents a key problem across multiple fields, from biology to control theory and finance [25, 27, 30, 32]. …”

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confidence: 99%

“…3C, E), and biologically it could correspond to a tuning of interactions among genes in the network (see e.g. [27]).…”

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confidence: 99%

Evolutionary Phase Transitions in Random Environments

Skanata

Kussell

2016

Phys. Rev. Lett.

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We present analytical results for long-term growth rates of structured populations in randomly fluctuating environments, which we apply to predict how cellular response networks evolve. We show that networks which respond rapidly to a stimulus will evolve phenotypic memory exclusively under random (i.e. non-periodic) environments. We identify the evolutionary phase diagram for simple response networks, which we show can exhibit both continuous and discontinuous transitions. Our approach enables exact analysis of diverse evolutionary systems, from viral epidemics to emergence of drug resistance.

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“…My results suggest that information flow within a module should be selected to minimize signal attenuation; models by Roh et al. () and Draghi and Whitlock () found that gene networks can evolve to reduce stochastic noise in gene expression. Similarly, game theory models show that unimodal signaling will be favored over multimodal signaling absent costs or constraints on signal reliability (Wilson et al.…”

Section: Discussionmentioning

confidence: 92%

“…In my model, loci were completely unlinked, the mutation rate was relatively high, and there was no mechanism for the evolution of noise reduction, such as in the models of Roh et al. () and Draghi and Whitlock ().…”

Section: Discussionmentioning

confidence: 97%

The genetics of phenotypic plasticity. XVI. Interactions among traits and the flow of information

Scheiner

2018

Evolution

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Although the environment varies, adaptive trait plasticity is uncommon, which can be due to either costs or limitations. Currently there is little evidence for costs of plasticity; limitations are a more promising explanation, including information reliability. A possible cause for a decrease in information reliability is the channeling of environmental information through one trait that then affects the phenotype of a second trait, the information path. Using an individual-based simulation model, I explored the ways in which configurations of trait interactions and patterns of environmental variation in space and time affect the evolution of phenotypic plasticity. I found that genotypes and phenotypes evolved to shorten and simplify the information path from the environment to fitness. A shortened path was characterized by a decrease in the amount of plasticity for traits that had a less direct connection between the environment of development and fitness. A simplified path was characterized by a decrease in the amount of plasticity for traits that had multiple paths between the environment and their phenotype. These results suggest that an eighth proposition be added to the theory of the evolution of phenotypic plasticity: Trait plasticity will evolve to minimize the information path between the environment and fitness.

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Evolution of Transcription Networks in Response to Temporal Fluctuations

Cited by 8 publications

References 31 publications

Evolutionary tipping points in the capacity to adapt to environmental change

Evolutionary tipping points in the capacity to adapt to environmental change

Evolutionary Phase Transitions in Random Environments

The genetics of phenotypic plasticity. XVI. Interactions among traits and the flow of information

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