While the use of innovative models describing data and using evolving pharmacostatistical methodology is important, more focus should be given to how pharmacometrics affects patients, and communicating resulting effects on benefit:risk decisions, not on the models themselves.
Process models describing soil development are useful for portraying the interactions of complex variables as soils evolve toward steady states.
The model described in this article evolved from the initial propositions made in [1].
A leading model describing these conformational changes evolved initially from spectroscopic evidence combined with structural information available at the time [3] with later support from the atomic structures of myosin subfragment 1 (S1) [5], [5] and of the actin filament [6].
The Big Bang model describes how a tumor evolves from a few thousand cells to a full-grown tumor composed of billions of cells.
The model describes how tumor cells evolve and survive the brief encounter with the immune system mediated by natural killer (NK) cells and the activated CD8+ cytotoxic T lymphocytes (CTLs).
The model explicitly describes the evolving configuration of dipolar particles of arbitrary shapes and sizes, automatically treats short-range interaction between neighboring particles and accurately calculates long-range electrostatic/magnetostatic interaction, including dipole and all multipole contributions.
The model describes how a gene lineage evolves inside a species-tree with a degree one root, by starting at the root and subsequently evolving towards the leaves while being exposed to gene duplication, gene loss, and pseudogenization events at rates δ, μ, and ψ, respectively.
The intra-cortical model describes how post-synaptic fluxes evolve over time.
During the emulation, users' movement is driven by a macrocellular pseudorandom mobility model with semi-directed trajectories, which is an evolved version of the model described in [44].
