Model Equations Reference

Introduction

rumenGP implements a collection of nonlinear models for describing cumulative gas production during in vitro rumen fermentation.

This vignette summarizes:

Throughout this vignette:

\[ V(t) \]

represents cumulative gas production at time:

\[ t \]


Single-Pool Models

Brody

Equation

\[ V(t) = A \left( 1 - b e^{-kt} \right) \]

Parameters

Parameter Description
A Asymptotic gas production
b Integration constant
k Fractional rate constant

Advantages

  • Simple and robust
  • Stable convergence
  • Easy interpretation

Limitations

  • No lag parameter
  • Limited flexibility

Ørskov and McDonald

Equation

\[ V(t) = VF + b \left( 1-e^{-kt} \right) \]

Parameters

Parameter Description
VF Initial gas volume (intercept)
b Fermentable fraction
k Fractional rate constant

Advantages

  • Widely used in ruminant nutrition
  • Simple biological interpretation

Limitations

  • No explicit lag phase

EXP0

Equation

\[ V(t) = V_f \left( 1-e^{-kt} \right) \]

Parameters

Parameter Description
Vf Asymptotic gas production
k Fractional rate constant

Advantages

  • Very simple
  • Fast convergence

Limitations

  • No lag phase
  • Limited flexibility

EXPL

Equation

\[ V(t) = V_f \left( 1-e^{-k(t-\lambda)} \right) \]

Parameters

Parameter Description
Vf Asymptotic gas production
k Fractional rate constant
λ Lag time

Advantages

  • Explicit lag parameter
  • Easy interpretation

Limitations

  • Less flexible than sigmoidal models

Gompertz

Equation

\[ V(t) = A \exp \left[ - \exp \left( \frac{\mu e}{A} (\lambda-t) + 1 \right) \right] \]

Parameters

Parameter Description
A Asymptotic gas production
μ Maximum gas production rate
λ Lag time

Advantages

  • Explicit lag and growth-rate parameters
  • Excellent flexibility
  • Widely used in gas production studies

Limitations

  • More complex than exponential models

Logistic

Equation

\[ V(t) = \frac{A} { 1+\exp \left[ 2+ 4k(\lambda-t) \right] } \]

Parameters

Parameter Description
A Asymptotic gas production
k Fractional rate constant
λ Lag time

Advantages

  • Sigmoidal behavior
  • Stable convergence

Limitations

  • Assumes symmetric sigmoid shape

Mitscherlich

Equation

\[ V(t) = A \left[ 1 - \exp \left( -k(t-\lambda) - d \left( \sqrt{t+0.001} - \sqrt{\lambda+0.001} \right) \right) \right] \]

Parameters

Parameter Description
A Asymptotic gas production
k Fractional rate constant
d Shape parameter
λ Lag time

Advantages

  • Flexible curve shape
  • Explicit lag phase

Limitations

  • More parameters
  • Increased parameter correlation

LE0 (Logistic-Exponential Without Lag)

Equation

\[ V(t) = \frac{ A \left( 1-e^{-kt} \right) } { 1+\exp \left[ \ln\left(\frac{1}{d}\right)-kt \right] } \]

Parameters

Parameter Description
A Asymptotic gas production
k Fractional rate constant
d Shape parameter

Advantages

  • Flexible shape
  • No lag parameter required

Limitations

  • More complex than simple exponential models

LEL (Logistic-Exponential With Lag)

Equation

\[ V(t) = \frac{ A \left( 1-e^{-k(t-\lambda)} \right) } { 1+\exp \left[ \ln\left(\frac{1}{d}\right) - k(t-\lambda) \right] } \]

Parameters

Parameter Description
A Asymptotic gas production
k Fractional rate constant
d Shape parameter
λ Lag time

Advantages

  • Flexible shape
  • Explicit lag phase

Limitations

  • Additional complexity may affect convergence

Michaelis-Menten

Equation

\[ V(t) = A \frac{t^{c}} { t^{c}+K^{c} } \]

Parameters

Parameter Description
A Asymptotic gas production
K Half-time parameter
c Shape parameter

Advantages

  • Flexible
  • Strong biological interpretation

Limitations

  • Shape parameter may be difficult to interpret biologically

Groot

Equation

\[ V(t) = \frac{VF} { 1+\left(\frac{b}{t}\right)^k } \]

Parameters

Parameter Description
VF Asymptotic gas production
b Half-time parameter
k Shape parameter

Advantages

  • Excellent flexibility
  • Widely used in rumen gas production studies

Limitations

  • Requires positive incubation times

Multi-Pool Models

Dual Logistic

Equation

\[ V(t) = \frac{V_{1F}} { 1+\exp \left[ 2-4k_1(t-\lambda) \right] } + \frac{V_{2F}} { 1+\exp \left[ 2-4k_2(t-\lambda) \right] } \]

Parameters

Parameter Description
V1F Gas volume from rapidly fermentable fraction
V2F Gas volume from slowly fermentable fraction
k1 Rate constant of rapid fraction
k2 Rate constant of slow fraction
λ Lag time

Advantages

  • Represents multiple fermentation pools
  • Biologically meaningful decomposition

Limitations

  • More parameters
  • Greater convergence challenges

Model Equivalence

Groot and Michaelis-Menten

The Groot and generalized Michaelis-Menten models are mathematically equivalent.

Parameter correspondence:

\[ VF = A \]

\[ b = K \]

\[ k = c \]

Both formulations produce identical fitted values and model diagnostics when convergence is achieved.

Researchers may select either model according to the terminology commonly used in their field.


Choosing a Model

A practical progression is:

Simple Models

Use when:


Lag Models

Use when:


Flexible Sigmoidal Models

Use when:


Multi-Pool Models

Use when:


Custom Models

Researchers can also define their own equations using:

fit_custom()

See:

vignette("custom-models")

for additional details.


Summary

rumenGP provides a diverse collection of nonlinear kinetic models ranging from simple exponential equations to flexible multi-pool formulations.

Model choice should be guided by:

Researchers are encouraged to compare multiple models before selecting a final representation of fermentation kinetics.