Related Experiment Video
Updated: Jun 14, 2026
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
A multivariate nonlinear mixed effects method for analyzing energy partitioning in growing pigs.
A B Strathe1, A Danfaer, A Chwalibog
1Department of Basic Animal and Veterinary Sciences, Faculty of Life Sciences, University of Copenhagen, DK-1870 Frederiksberg, Denmark. abstrathe@ucdavis.edu
Journal of Animal Science
|March 30, 2010
Summary
This study introduces a new multivariate nonlinear mixed effects (MNLME) framework for animal energy metabolism, improving protein deposition (PD) and lipid deposition (LD) equations. The MNLME approach better accounts for animal variability and correlated errors in PD and LD measurements.
Area of Science:
- Animal Nutrition
- Metabolic Modeling
- Statistical Frameworks
Background:
- Simultaneous equations are increasingly used to model nutrition's effect on protein deposition (PD) and lipid deposition (LD) in animals.
- Accurate modeling of energy metabolism is crucial for optimizing animal growth and feed efficiency.
Purpose of the Study:
- To develop and evaluate a multivariate nonlinear mixed effects (MNLME) framework for estimating parameters in simultaneous equations describing energy metabolism.
- To propose new PD and LD equations based on the MNLME framework and compare them with existing models.
- To investigate the impact of animal variability and correlated errors on energy metabolism models.
Main Methods:
- Developed a multivariate nonlinear mixed effects (MNLME) framework implemented in SAS NLMIXED.
- Proposed new PD and LD equations incorporating Michaelis-Menten and Gompertz functions.
- Compared the new equations with van Milgen and Noblet (1999) equations using two datasets on growing pigs' energy metabolism.
- Evaluated models using information criteria and analyzed parameter estimates, including animal variation.
Main Results:
- The MNLME framework demonstrated superiority over multivariate nonlinear regression by accounting for correlated errors and animal random effects.
- New PD and LD equations showed improved performance for one dataset (II), while existing equations were better for the other (I).
- Estimated key parameters such as efficiencies of energy utilization (k(p), k(f)), maintenance requirements, and quantified animal variation in PD(Max) and k(f).
Conclusions:
- The MNLME framework provides a more robust approach for modeling animal energy metabolism, particularly for PD and LD.
- Accounting for animal variability and correlated measurement errors is essential for accurate multivariate models of energy metabolism in growing pigs.
- The study recommends the use of MNLME for future research in animal energy metabolism to better understand nutritional impacts.
Related Concept Videos
01:28Methods of Medium Optimization
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
00:51Energy Budgets and Reproductive Strategies
Organisms must balance energy intake with the energy required for growth, maintenance, and reproduction. These trade-offs result in a variety of survivorship and reproductive strategies, including semelparity and iteroparity. Semelparous species reproduce only once in their lifetime, often investing most available resources into that single reproductive event. Iteroparous species, by contrast, reproduce multiple times over their lifetimes, typically allocating fewer resources to any single...
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
00:59Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
01:13Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...