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Behavioral diversity in microbes and low-dimensional phenotypic spaces.

ABSTRACT: Systematic studies of phenotypic diversity--required for understanding evolution--lag behind investigations of genetic diversity. Here we develop a quantitative approach to studying behavioral diversity, which we apply to swimming of the ciliate Tetrahymena. We measure the full-lifetime behavior of hundreds of individual organisms at high temporal resolution, over several generations and in diverse nutrient conditions. To characterize population diversity and temporal variability we introduce a unique statistical framework grounded in the notion of a phenotypic space of behaviors. We show that this space is effectively low dimensional with dimensions that correlate with a two-state "roaming and dwelling" model of swimming behavior. Temporal variability over the lifetime of an individual is correlated with the fraction of time spent roaming whereas diversity between individuals is correlated with the speed of roaming. Quantifying the dynamics of behavioral variation shows that behavior over the lifetime of an individual is strongly nonstationary. Analysis of behavioral dynamics between generations reveals complex patterns of behavioral heritability that point to the importance of considering correlations beyond mothers and daughters. Our description of a low-dimensional behavioral space should enable the systematic study of the evolutionary and ecological bases of phenotypic constraints. Future experimental and theoretical studies of behavioral diversity will have to account for the possibility of nonstationary and environmentally dependent behavioral dynamics that we observe.

SUBMITTER: Jordan D

PROVIDER: S-EPMC3752258 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

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Behavioral diversity in microbes and low-dimensional phenotypic spaces.

Jordan David D Kuehn Seppe S Katifori Eleni E Leibler Stanislas S

Proceedings of the National Academy of Sciences of the United States of America 20130729 34

Systematic studies of phenotypic diversity--required for understanding evolution--lag behind investigations of genetic diversity. Here we develop a quantitative approach to studying behavioral diversity, which we apply to swimming of the ciliate Tetrahymena. We measure the full-lifetime behavior of hundreds of individual organisms at high temporal resolution, over several generations and in diverse nutrient conditions. To characterize population diversity and temporal variability we introduce a ...[more]

PMID: 23898201

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Project description:BackgroundA biological system's robustness to mutations and its evolution are influenced by the structure of its viable space, the region of its space of biochemical parameters where it can exert its function. In systems with a large number of biochemical parameters, viable regions with potentially complex geometries fill a tiny fraction of the whole parameter space. This hampers explorations of the viable space based on "brute force" or Gaussian sampling.ResultsWe here propose a novel algorithm to characterize viable spaces efficiently. The algorithm combines global and local explorations of a parameter space. The global exploration involves an out-of-equilibrium adaptive Metropolis Monte Carlo method aimed at identifying poorly connected viable regions. The local exploration then samples these regions in detail by a method we call multiple ellipsoid-based sampling. Our algorithm explores efficiently nonconvex and poorly connected viable regions of different test-problems. Most importantly, its computational effort scales linearly with the number of dimensions, in contrast to "brute force" sampling that shows an exponential dependence on the number of dimensions. We also apply this algorithm to a simplified model of a biochemical oscillator with positive and negative feedback loops. A detailed characterization of the model's viable space captures well known structural properties of circadian oscillators. Concretely, we find that model topologies with an essential negative feedback loop and a nonessential positive feedback loop provide the most robust fixed period oscillations. Moreover, the connectedness of the model's viable space suggests that biochemical oscillators with varying topologies can evolve from one another.ConclusionsOur algorithm permits an efficient analysis of high-dimensional, nonconvex, and poorly connected viable spaces characteristic of complex biological circuitry. It allows a systematic use of robustness as a tool for model discrimination.

Dataset Information

Behavioral diversity in microbes and low-dimensional phenotypic spaces.

Publications

Behavioral diversity in microbes and low-dimensional phenotypic spaces.

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