Environment-to-phenotype mapping and adaptation strategies in varying environments

Contributed by Stanislas Leibler, May 14, 2019 (sent for review February 26, 2019; reviewed by Armita Nourmohammad and Mikhail Tikhonov)
June 20, 2019
116 (28) 13847-13855

Significance

A fundamental difference between living and nonliving systems is that organisms can evolve responsive adaptation to external conditions. We present a theoretical framework, which unifies different adaptation strategies encountered in biology. Central to our approach is the introduction of an environment-to-phenotype mapping describing how organisms’ traits or behavior depend on the environment. In contrast to commonly considered genotype-to-phenotype mapping, our approach emphasizes an evolutionary rather than mechanistic understanding of organisms. Our phenomenological model, inspired by artificial neural networks, also allows us to study the importance of the dimensionality of internal representations for the adaptation strategies.

Abstract

Biological organisms exhibit diverse strategies for adapting to varying environments. For example, a population of organisms may express the same phenotype in all environments (“unvarying strategy”) or follow environmental cues and express alternative phenotypes to match the environment (“tracking strategy”), or diversify into coexisting phenotypes to cope with environmental uncertainty (“bet-hedging strategy”). We introduce a general framework for studying how organisms respond to environmental variations, which models an adaptation strategy by an abstract mapping from environmental cues to phenotypic traits. Depending on the accuracy of environmental cues and the strength of natural selection, we find different adaptation strategies represented by mappings that maximize the long-term growth rate of a population. The previously studied strategies emerge as special cases of our model: The tracking strategy is favorable when environmental cues are accurate, whereas when cues are noisy, organisms can either use an unvarying strategy or, remarkably, use the uninformative cue as a source of randomness to bet hedge. Our model of the environment-to-phenotype mapping is based on a network with hidden units; the performance of the strategies is shown to rely on having a high-dimensional internal representation, which can even be random.

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Acknowledgments

We thank Michael R. Mitchell, David A. Huse, Kunihiko Kaneko, and Lai-Sang Young for helpful discussions. This research has been partly supported by grants from the Simons Foundation (to S.L.) through the Rockefeller University (Grant 345430) and the Institute for Advanced Study (Grant 345801). B.X. and P.S. are funded by the Eric and Wendy Schmidt Membership in Biology at the Institute for Advanced Study.

Supporting Information

Appendix (PDF)
Dataset_S01 (TXT)

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Information & Authors

Information

Published in

Go to Proceedings of the National Academy of Sciences
Proceedings of the National Academy of Sciences
Vol. 116 | No. 28
July 9, 2019
PubMed: 31221749

Classifications

Submission history

Published online: June 20, 2019
Published in issue: July 9, 2019

Keywords

  1. evolutionary theory
  2. fluctuating environments
  3. phenotypic plasticity
  4. population dynamics
  5. survival strategies

Acknowledgments

We thank Michael R. Mitchell, David A. Huse, Kunihiko Kaneko, and Lai-Sang Young for helpful discussions. This research has been partly supported by grants from the Simons Foundation (to S.L.) through the Rockefeller University (Grant 345430) and the Institute for Advanced Study (Grant 345801). B.X. and P.S. are funded by the Eric and Wendy Schmidt Membership in Biology at the Institute for Advanced Study.

Authors

Affiliations

The Simons Center for Systems Biology, Institute for Advanced Study, Princeton, NJ 08540;
Laboratory of Living Matter, The Rockefeller University, New York, NY 10065;
Center for Studies in Physics and Biology, The Rockefeller University, New York, NY 10065
Pablo Sartori
The Simons Center for Systems Biology, Institute for Advanced Study, Princeton, NJ 08540;
Laboratory of Living Matter, The Rockefeller University, New York, NY 10065;
Center for Studies in Physics and Biology, The Rockefeller University, New York, NY 10065
Stanislas Leibler1 [email protected]
The Simons Center for Systems Biology, Institute for Advanced Study, Princeton, NJ 08540;
Laboratory of Living Matter, The Rockefeller University, New York, NY 10065;
Center for Studies in Physics and Biology, The Rockefeller University, New York, NY 10065

Notes

1
To whom correspondence may be addressed. Email: [email protected] or [email protected].
Author contributions: B.X., P.S., and S.L. designed research; B.X. and P.S. performed research; and B.X. and S.L. wrote the paper.
Reviewers: A.N., Max Planck Institute for Dynamics and Self Organization and University of Washington in Seattle; and M.T., Washington University in St. Louis.

Competing Interests

The authors declare no conflict of interest.

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    Environment-to-phenotype mapping and adaptation strategies in varying environments
    Proceedings of the National Academy of Sciences
    • Vol. 116
    • No. 28
    • pp. 13707-14387

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