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A data-driven framework to model the organism-environment system.

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Organisms and their environment dynamically interact. This study presents a new modeling framework to predict how organisms respond to environmental changes, even as they develop over time.

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Area of Science:

  • Developmental biology
  • Ecology
  • Systems biology

Background:

  • Organisms continuously interact with and modify their environment, a complex dynamic challenging to model.
  • Accurate models are needed for phenomena like phenotypic plasticity, enabling predictions of organismal responses to environmental signals.
  • Existing models often struggle to capture the coupled nature of organism-environment interactions and to be fitted with real-world data.

Purpose of the Study:

  • To introduce a novel modeling framework for coupled organism-environment systems.
  • To enable quantitative predictions of how organisms respond to environmental signals over time.
  • To model phenotypic plasticity as a dynamic, developmentally-regulated property.

Main Methods:

  • Developed a nonlinear black-box modeling framework representing organism and environment as a single coupled dynamical system.
  • Utilized time-series input (environmental signals) and output (system measurements) data to fit the model.
  • Employed in silico experiments to test the framework's predictive capabilities for phenotypic plasticity.

Main Results:

  • The framework successfully captures the dynamical nature of organism-environment interactions.
  • The model can be fitted using observational data and applied without deep system-specific knowledge.
  • Demonstrated accurate prediction of organismal responses to novel environmental signals in silico.
  • Showed that phenotypic plasticity can be modeled as a time-varying dynamical property during ontogeny.

Conclusions:

  • The proposed framework offers a powerful tool for studying organism-environment dynamics and phenotypic plasticity.
  • It allows for data-driven, predictive modeling of complex biological systems across development.
  • This approach advances our understanding of how environmental interactions shape organismal development and function over time.