Information integration and decision making in flowering time control

• Published in: PloS one Vol. 15; no. 9; p. e0239417
• Main Authors: Zhao, Linlin, Richards, Sarah, Turck, Franziska, Kollmann, Markus
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 23.09.2020; Public Library of Science (PLoS)
• Subjects: Adaptation; Artificial neural networks; Biology and Life Sciences; Botanical research; Brittleness; Climate; Climate change; Computer and Information Sciences; Computer simulation; Data processing; Day length; Decision making; Earth Sciences; Engineering and Technology; Epigenetics; Flowering; Gene expression; Information processing; Neural networks; Neurons; Physical Sciences; Plant breeding; Plant climatology; Plant flowering; Plants (botany); Research and Analysis Methods; Seasonal variations; Signal processing; Stochastic models; Stochasticity; Temperature; Vernalization; Winter; Winter temperatures; Germany
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0239417

In order to successfully reproduce, plants must sense changes in their environment and flower at the correct time. Many plants utilize day length and vernalization, a mechanism for verifying that winter has occurred, to determine when to flower. Our study used available temperature and day length data from different climates to provide a general understanding how this information processing of environmental signals could have evolved in plants. For climates where temperature fluctuation correlations decayed exponentially, a simple stochastic model characterizing vernalization was able to reconstruct the switch-like behavior of the core flowering regulatory genes. For these and other climates, artificial neural networks were used to predict flowering gene expression patterns. For temperate plants, long-term cold temperature and short-term day length measurements were sufficient to produce robust flowering time decisions from the neural networks. Additionally, evolutionary simulations on neural networks confirmed that the combined signal of temperature and day length achieved the highest fitness relative to neural networks with access to only one of those inputs. We suggest that winter temperature memory is a well-adapted strategy for plants' detection of seasonal changes, and absolute day length is useful for the subsequent triggering of flowering.