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The simulation results showed that the expression of the stable state current had the same form as that deduced from stable state model.
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It provides a data structure to manipulate logical models, as well as a set of analytic tools (e.g., stable state identification, model reduction) that are common to many scientific efforts relying upon a discrete modelling approach.
The fitness function and the stable state mathematical model are depicted as Figure 24.
The fitness function and the stable state mathematical model are shown in Figure 25.
Therefore, in order to analyse the dependence of the stable states on model parameters we selected a representative parameter set from the sampled distribution (see Additional file 3, parameter set 1 listed in the first data row of the table).
We describe an algorithm called ENPOP (energy function parameter optimization) that improves and sometimes optimizes the parameters for any given model and for any given search strategy that identifies the stable state of that model.
Consequently, although mutual cooperation would result in a higher fitness, defection is the only evolutionarily stable state in this model.
Stable steady states of ordinary differential equations (ODEs) are considered multiple times: initially while deducing the equations of ionic channel kinetics (Hodgkin and Huxley, 1952), then by exploring steady states of neuronal activity, and finally while considering a network model in which stable steady states model memorized items.
After climate change in our model, a stable state in which the focal species phenologies are closely coupled is always available.
After the model reaches a stable state, modifying it to knockout Notch or p21 allows to verify that the model also satisfies specifications H3, respectively H4.
In particular, it provides an efficient determination of the stable states of a model.
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