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The first class of gene regulatory network models considers a gene regulatory network as a linear and time continuous system, with the transcriptional activity of genes described by a time continuous dynamical system of first order differential equations [ 5- 7] or by stochastic dynamical equations, a framework based on a state space model [ 8] or Dynamic Bayesian network [ 9, 10].
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Analytical solutions were developed for both models considering a pseudo-first order reaction.
The traditional inventory models consider a case in which depletion of inventory is caused by a constant demand rate.
We also provide a good comparison of FSO communication noise models considering a moderate atmospheric turbulence condition.
On the one hand, closed-loop full-buffer models consider a constant number of users with unlimited data to transmit.
Sub-cell FE models considered a meso-structure of textile composites in terms of simplified RUC representations.
Covering location models consider a demand "covered" if there is at least one facility sited within a preset threshold distance.
It is shown that models considering a continuum interface are to be used with care when dealing with patterned interfaces.
We address the design of M-bODE for dynamic models considering a continuous representation of the design.
We considered the same eight genetic models as Wolyn et al. [11] Five models considered a single QTL with various positions, additive effects, and dominance effects.
Models considering a number of covariates identified 637 OTUs associated with FI.
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