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Starting from a large-scale, physics-based model parameterization with an extensive parameter space, the best identifiable reduced dimensional parameterization is constructed.
The parameters of the model were either directly extracted from observation (e.g. observed initiation rate, percentage of emergence, number of simultaneously developing primordia), or estimated through extensive parameter space exploration and comparison between model outputs and observation (see Supplementary Figure S3 for a detailed outlook of the reference used for parameters calibration).
In the previous work, we obtained the optimal τ c by stochastic simulations, but this method was computationally too costly to explore the extensive parameter space defined by the rate constants and N mol (Lee et al., 2012).
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Computational studies provide an important complement to experimental measurements and allow a more extensive probe of parameter space.
While speed itself is not an overriding optimality criterion, rapid analyses will permit robust and extensive exploration of parameter space.
Currently, such extensive evaluation of parameter space is not conducted by transcriptome assemblers such as Trans-ABySS and Trinity, and thus will likely produce suboptimal assemblies with some datasets.
The optimal parameters obtained by our extensive search of the parameter space was not, however, the best minimum in the sense of making the model interpretable.
We make use of the high computational power of the Blue Gene technology to perform an extensive study of the parameter space in a model of avascular tumor growth.
In IsoCleft Finder, these parameters are fixed with default values previously obtained through an extensive heuristic search in parameter space.
However, a more comprehensive mathematical representation of the biological processes involved comes at the expense of an increased number of free parameters, making more difficult an extensive exploration of the parameter space.
As a preliminary step, an extensive exploration of the parameter space was carried out in order to determine which parameter combinations allow for long-term coexistence of pathogens and hosts.
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