@article{9381,
  abstract     = {A game of rock-paper-scissors is an interesting example of an interaction where none of the pure strategies strictly dominates all others, leading to a cyclic pattern. In this work, we consider an unstable version of rock-paper-scissors dynamics and allow individuals to make behavioural mistakes during the strategy execution. We show that such an assumption can break a cyclic relationship leading to a stable equilibrium emerging with only one strategy surviving. We consider two cases: completely random mistakes when individuals have no bias towards any strategy and a general form of mistakes. Then, we determine conditions for a strategy to dominate all other strategies. However, given that individuals who adopt a dominating strategy are still prone to behavioural mistakes in the observed behaviour, we may still observe extinct strategies. That is, behavioural mistakes in strategy execution stabilise evolutionary dynamics leading to an evolutionary stable and, potentially, mixed co-existence equilibrium.},
  author       = {Kleshnina, Maria and Streipert, Sabrina S. and Filar, Jerzy A. and Chatterjee, Krishnendu},
  issn         = {15537358},
  journal      = {PLoS Computational Biology},
  number       = {4},
  publisher    = {Public Library of Science},
  title        = {{Mistakes can stabilise the dynamics of rock-paper-scissors games}},
  doi          = {10.1371/journal.pcbi.1008523},
  volume       = {17},
  year         = {2021},
}

@article{9759,
  author       = {Bartlett, Michael John and Arslan, Feyza N and Bankston, Adriana and Sarabipour, Sarvenaz},
  issn         = {15537358},
  journal      = {PLoS Computational Biology},
  number       = {7},
  publisher    = {Public Library of Science},
  title        = {{Ten simple rules to improve academic work- life balance}},
  doi          = {10.1371/journal.pcbi.1009124},
  volume       = {17},
  year         = {2021},
}

@article{7212,
  abstract     = {The fixation probability of a single mutant invading a population of residents is among the most widely-studied quantities in evolutionary dynamics. Amplifiers of natural selection are population structures that increase the fixation probability of advantageous mutants, compared to well-mixed populations. Extensive studies have shown that many amplifiers exist for the Birth-death Moran process, some of them substantially increasing the fixation probability or even guaranteeing fixation in the limit of large population size. On the other hand, no amplifiers are known for the death-Birth Moran process, and computer-assisted exhaustive searches have failed to discover amplification. In this work we resolve this disparity, by showing that any amplification under death-Birth updating is necessarily bounded and transient. Our boundedness result states that even if a population structure does amplify selection, the resulting fixation probability is close to that of the well-mixed population. Our transience result states that for any population structure there exists a threshold r⋆ such that the population structure ceases to amplify selection if the mutant fitness advantage r is larger than r⋆. Finally, we also extend the above results to δ-death-Birth updating, which is a combination of Birth-death and death-Birth updating. On the positive side, we identify population structures that maintain amplification for a wide range of values r and δ. These results demonstrate that amplification of natural selection depends on the specific mechanisms of the evolutionary process.},
  author       = {Tkadlec, Josef and Pavlogiannis, Andreas and Chatterjee, Krishnendu and Nowak, Martin A.},
  issn         = {15537358},
  journal      = {PLoS computational biology},
  publisher    = {Public Library of Science},
  title        = {{Limits on amplifiers of natural selection under death-Birth updating}},
  doi          = {10.1371/journal.pcbi.1007494},
  volume       = {16},
  year         = {2020},
}

@article{6900,
  abstract     = {Across diverse biological systems—ranging from neural networks to intracellular signaling and genetic regulatory networks—the information about changes in the environment is frequently encoded in the full temporal dynamics of the network nodes. A pressing data-analysis challenge has thus been to efficiently estimate the amount of information that these dynamics convey from experimental data. Here we develop and evaluate decoding-based estimation methods to lower bound the mutual information about a finite set of inputs, encoded in single-cell high-dimensional time series data. For biological reaction networks governed by the chemical Master equation, we derive model-based information approximations and analytical upper bounds, against which we benchmark our proposed model-free decoding estimators. In contrast to the frequently-used k-nearest-neighbor estimator, decoding-based estimators robustly extract a large fraction of the available information from high-dimensional trajectories with a realistic number of data samples. We apply these estimators to previously published data on Erk and Ca2+ signaling in mammalian cells and to yeast stress-response, and find that substantial amount of information about environmental state can be encoded by non-trivial response statistics even in stationary signals. We argue that these single-cell, decoding-based information estimates, rather than the commonly-used tests for significant differences between selected population response statistics, provide a proper and unbiased measure for the performance of biological signaling networks.},
  author       = {Cepeda Humerez, Sarah A and Ruess, Jakob and Tkačik, Gašper},
  issn         = {15537358},
  journal      = {PLoS computational biology},
  number       = {9},
  pages        = {e1007290},
  publisher    = {Public Library of Science},
  title        = {{Estimating information in time-varying signals}},
  doi          = {10.1371/journal.pcbi.1007290},
  volume       = {15},
  year         = {2019},
}