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Signal transduction systems are often modeled as networks of biochemical kinetic equations implemented as continuous-time dynamical models using differential equations.
The extension includes a library of classical analytical pharmacokinetics models and allows the user to describe more complex models using differential equations.
In addition to being well suited for our formal verification based iterative improvement approach, using Qualitative network has several benefits compared to using other existing frameworks, such a quantitative models using differential equations and Boolean networks.
Various methods have been proposed to model GRNs; these include logical models [ 4], continuous models using differential equations [ 5, 6] and stochastic models at the single-molecule level [ 7, 8].
Methodologies for computational analysis can vary widely depending on the question being posed and the experimental data at hand, ranging from highly abstracted models using correlative regression to highly specified models using differential equations, with network component interaction and logic modeling techniques intermediate to these.
In the models that we have examined, estimates of reproduction numbers have been obtained by fitting case/mortality time series data from the past pandemics into models using differential equations [ 44], cumulative exponential growth equations [ 7], and Bayesian likelihood expressions [ 27].
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Traditional quantitative models use differential equations to represent continuous flows.
The problem is modeled using differential game theory.
Chapter 6 describes continuous-time modeling using differential equations with an exercise involving developing a model's equation.
The creation of the quantitative model using differential equations presented no problem for the IQS students.
The circuit was modeled using differential equations with all chemical reactions between species explicitly defined.
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