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This chapter presents a dynamic metabolic modelMetabolic modeling for Ulva sp., incorporating key environmental constraintsEnvironmental constraints and nutrient dynamicsNutrient dynamics to predict biomass growth and chemical composition. The model is based on a modified mathematical framework adapted from previous studies and follows the Droop EquationDroop equation concept, which links nitrogen availability in the environment to intracellular nutrient content and subsequent growth rates. It integrates critical factors such as light intensity, temperature, and salinity, each influencing metabolic efficiency and nutrient uptake. The governing equations describe biomass density, internal nitrogen concentration, and external nitrogen availability, forming a comprehensive system of differential equations to simulate Ulva metabolism. Growth rate formulations follow Liebig’s law of the minimum, ensuring that the most limiting resource dictates growth while considering temperature and salinity as modifying background conditions. The model further incorporates energy losses through respiration, exudation, and mortality, adjusted to empirical data. By simulating nutrient uptake, nitrogen reserves, and external nutrient fluctuations, this model provides a predictive tool for optimizing Ulva cultivation under variable environmental conditions.

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A Dynamic Metabolic Model for Ulva sp.: Integrating Environmental Constraints and Nutrient Dynamics in Biomass Growth

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  • 发表日期：

    2025年

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  • Meiron Zollmann,
  • Alexander Liberzon,
  • Alexander Golberg

摘要

This chapter presents a dynamic metabolic modelMetabolic modeling for Ulva sp., incorporating key environmental constraintsEnvironmental constraints and nutrient dynamicsNutrient dynamics to predict biomass growth and chemical composition. The model is based on a modified mathematical framework adapted from previous studies and follows the Droop EquationDroop equation concept, which links nitrogen availability in the environment to intracellular nutrient content and subsequent growth rates. It integrates critical factors such as light intensity, temperature, and salinity, each influencing metabolic efficiency and nutrient uptake. The governing equations describe biomass density, internal nitrogen concentration, and external nitrogen availability, forming a comprehensive system of differential equations to simulate Ulva metabolism. Growth rate formulations follow Liebig’s law of the minimum, ensuring that the most limiting resource dictates growth while considering temperature and salinity as modifying background conditions. The model further incorporates energy losses through respiration, exudation, and mortality, adjusted to empirical data. By simulating nutrient uptake, nitrogen reserves, and external nutrient fluctuations, this model provides a predictive tool for optimizing Ulva cultivation under variable environmental conditions.