Our data are better understood in terms of models that assign a role to both factors.
Then, an engineered system can be decomposed into subsystems or components of which computational models are better understood in terms of PoF mechanisms or system performances of interest.
Furthermore, electronic nose model classifications need to be better understood in relationship with consumers acceptance and HPLC concentrations altogether.
In vitro 3D models (e.g. explants and traditional 2D cultures in matrix scaffolds) have been developed as an intermediate between the 2D cultures and animal models to better understand in vivo 3D physiological conditions/environment [ 29, 31, 32].
In both cases an apparently simply reaction system displays phenotypes that are surprising and can be better understood when modelled mathematically in a systems biological context.
Such complex dynamics is better understood by studying model responses to variations in the avidity ratio within each clone and investigating the impact of such variations in inducing their corresponding autoantibodies.
To better understand the model in Fig. 7B, we used gene network analyses in GeneMania (Mostafavi et al., 2008; Warde-Farley et al., 2010) to identify known or predicted pair-wise gene interactions for orthologues of cisd2, Ppt1 and cln3 (i.e. seed genes) in S. cerevisiae, C. elegans, Drosophila, mice and humans.
Use named ranges so you (and others) can better understand your model in the future.
In order to better understand and model developments in innovation and futures research, relational data inherent to the social network perspective is needed.
For example, so-called Phase 0 trials preclinical trials designed to gather PK/PD data from subtherapeutic doses given directly to humans will bypass animal experiments in this earliest stage in order to better understand and model the drug's action in humans.
