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Related Concept Videos

Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
Growth Models with Integration: Problem Solving01:27

Growth Models with Integration: Problem Solving

In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A higher...
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
Exponential Equations for Modeling Growth01:26

Exponential Equations for Modeling Growth

Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is the relative...

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Related Experiment Video

Updated: Jun 17, 2026

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
15:30

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

Modelling the crop: from system dynamics to systems biology.

Xinyou Yin1, Paul C Struik

  • 1Centre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University, PO Box 430, 6700 AK Wageningen, The Netherlands. Xinyou.Yin@wur.nl

Journal of Experimental Botany
|January 7, 2010
PubMed
Summary

Crop models can be improved by integrating physiological understanding and genetic data. This approach enhances the analysis of genotype x environment interactions for better crop breeding.

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Robotic Sensing and Stimuli Provision for Guided Plant Growth
08:02

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

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Last Updated: Jun 17, 2026

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
15:30

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

Robotic Sensing and Stimuli Provision for Guided Plant Growth
08:02

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

Area of Science:

  • Crop science
  • Systems biology
  • Quantitative genetics

Background:

  • Interplant competition significantly impacts crop development through complex regulatory mechanisms.
  • Current crop models often rely on empirical data, limiting biological interpretability.
  • Integrating physiological understanding and mathematical tools can reduce empiricism in crop modeling.

Purpose of the Study:

  • To review recent findings and perspectives on modeling genotype x environment interactions at the crop level.
  • To highlight the potential of crop systems biology in bridging the genotype-phenotype gap.
  • To discuss the integration of molecular genetic information into ecophysiological models.

Main Methods:

  • Review of existing literature on crop modeling, systems dynamics, and genomics.
  • Analysis of case studies integrating quantitative trait loci (QTL) or genes into ecophysiological models.
  • Discussion of the principles of crop systems biology.

Main Results:

  • Models can be made less empirical by incorporating physiological understanding and mathematical tools.
  • Genetic coefficients in crop models can be elucidated through functional genomics and systems biology.
  • Integrating molecular genetic data into ecophysiological models shows promise for analyzing genotype-phenotype relationships.

Conclusions:

  • Advanced crop models require upgrades based on lower organizational level understanding for complex phenomena.
  • Crop systems biology, combining genomics, physiology, biochemistry, and modeling, is crucial for narrowing genotype-phenotype gaps.
  • In silico modeling holds significant potential for crop breeding and understanding genotype x environment interactions.