In most cases, ordinary differential equations are used to build stage structure models [1, 2].
Reduced-order nonlinear models are derived from a stage-by-stage balance model by partitioning the column into distinct compartments and applying time-scale arguments to convert most of the ordinary differential equations (ODE) describing each compartment into simpler algebraic equations.
The most common representations are ordinary differential equations (ODEs) for the deterministic framework and continuous-time Markov chain (CTMC) for the framework [ 3, 4].
The Biomodel Database of the European Bioinformatics Institute comprises over 400 published and curated models in its repository, most of them being ordinary differential equation (ODE) models (Li et al., 2010).
Most models consist of coupled ordinary differential equations developed by the researchers, restricted to deal with the interactions of a limited number of molecules.
However, some of these methods are reliable and applicable for solving ordinary differential equations; the most of these methods provide the solution only for a particular kind of differential equations or a particular kind of boundary conditions.
The model features a system of ordinary differential equations, where most of the model parameters are either assigned or individualized for a given horse, using literature data and correlations.
Its most well-known application is in ordinary differential equations, particularly, in the proof of the Picard-Lindelöf theorem which guarantees the existence and uniqueness of solutions of first-order initial value problems.
Many techniques for constructing GNs have been proposed of which the most popular techniques are based on ordinary differential equations [13] [15] or Bayesian networks [16].
Since systems biology focuses on dynamical behavior at the molecular level, where change of properties of molecular constituents is observed through time, ordinary differential equations are most commonly used to formulate mathematical models.
