Dataset Information

Quantifying the Adaptive Cycle.

ABSTRACT: The adaptive cycle was proposed as a conceptual model to portray patterns of change in complex systems. Despite the model having potential for elucidating change across systems, it has been used mainly as a metaphor, describing system dynamics qualitatively. We use a quantitative approach for testing premises (reorganisation, conservatism, adaptation) in the adaptive cycle, using Baltic Sea phytoplankton communities as an example of such complex system dynamics. Phytoplankton organizes in recurring spring and summer blooms, a well-established paradigm in planktology and succession theory, with characteristic temporal trajectories during blooms that may be consistent with adaptive cycle phases. We used long-term (1994-2011) data and multivariate analysis of community structure to assess key components of the adaptive cycle. Specifically, we tested predictions about: reorganisation: spring and summer blooms comprise distinct community states; conservatism: community trajectories during individual adaptive cycles are conservative; and adaptation: phytoplankton species during blooms change in the long term. All predictions were supported by our analyses. Results suggest that traditional ecological paradigms such as phytoplankton successional models have potential for moving the adaptive cycle from a metaphor to a framework that can improve our understanding how complex systems organize and reorganize following collapse. Quantifying reorganization, conservatism and adaptation provides opportunities to cope with the intricacies and uncertainties associated with fast ecological change, driven by shifting system controls. Ultimately, combining traditional ecological paradigms with heuristics of complex system dynamics using quantitative approaches may help refine ecological theory and improve our understanding of the resilience of ecosystems.

SUBMITTER: Angeler DG

PROVIDER: S-EPMC4696843 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Quantifying the Adaptive Cycle.

Angeler David G DG Allen Craig R CR Garmestani Ahjond S AS Gunderson Lance H LH Hjerne Olle O Winder Monika M

PloS one 20151230 12

The adaptive cycle was proposed as a conceptual model to portray patterns of change in complex systems. Despite the model having potential for elucidating change across systems, it has been used mainly as a metaphor, describing system dynamics qualitatively. We use a quantitative approach for testing premises (reorganisation, conservatism, adaptation) in the adaptive cycle, using Baltic Sea phytoplankton communities as an example of such complex system dynamics. Phytoplankton organizes in recurr ...[more]

PMID: 26716453

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Project description:The cell cycle is known to regulate cell proliferation and cell fate decisions, the underlying mechanism of which is well-studied and conserved across species and cell types. To date, the cell cycle is receiving a renewed importance due to the rapid advancement of single-cell genomics technology. Especially in the analysis of single-cell gene expression, the cell cycle plays a key role in understanding the expression variation across cell states and cell types. The current study was designed to develop effective tools for assessing and predicting the cell cycle in single-cell gene expression data analysis. We collected both Fluorescent Ubiquitination-based Cell Cycle Indicator (FUCCI) – for determining the cell cycle – and single-cell RNA-seq (scRNA-seq) data from each individual using STRT-seq on the Fluidigm C1 platform. The cells were collected from iPSCs derived from 6 genotyped Yoruba cell lines. The experimental design controlled for C1 processing batch effects, as well as individual and gender effects. Using these data, we developed a supervised approach for predicting the cyclical ordering of single cells in the cell cycle using single-cell gene expression data. We used the FUCCI fluorescent intensities to determine a cyclical ordering of the individual cells, and to assign cell time labels for individual cells representing each cell's position in one complete cell cycle. We estimated the cyclical trend of expression levels for each gene based on the FUCCI-derived cell times, and identified candidate sets of cyclical genes for model training. We trained our model in in 5-fold cross-validation and evaluate the trained model in held-out validation samples and external datasets. We compared the prediction results with existing approaches for estimating the cell cycle using single-cell gene expression data: including unsupervised approaches to construct the cyclical ordering of single cells and approaches to categorically assign the cell cycle to individual cells. These results provide a benchmark for assessing the cell cycle in scRNA-seq data analysis and insights into the effects of the cell cycle on gene expression variation in stem cells.

Dataset Information

Quantifying the Adaptive Cycle.

Publications

Quantifying the Adaptive Cycle.

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