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Thus, mathematical modelling enabled us to assemble working hypotheses for the signalling processes that increase the rate of oxidative phosphorylation during changes in energy demand, to tease out conclusions in bioenergetic terms, and to use our data predicatively to generate new hypotheses.
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In summary, metabolic network modeling enabled us to interpret accumulated biological knowledge and to get a deeper understanding of global redox balancing mechanisms in PNSB.
Modelling enables us to estimate the long term and population-wide health effects of interventions.
The use of preclinical models enabled us to study the outcome of bevacizumab in combination with chemotherapy on EOC progression (first end point) and survival (second end point).
GWAS using the appropriate analysis model enabled us to identify several SNP markers significantly associated with QTL.
The use of a multilevel model enabled us to further account for unexplained variation and clustering across hospital trust sites.
The use of this neuronal model enabled us to assess endogenous DOP receptor trafficking in a non-transfected, non-immortalized model.
This analysis using dynamic logistic regression model enabled us to assess the full effect of adherence on mortality without confounding from factors on the causal pathway.
The use of the 2008 xenograft model enabled us to confirm that activation of NF-κB coincides with p110α upregulation in non-proliferating tumor cells in vivo.
The 2D model enabled us to exclude data correction by the empirical form factor used in 1D model.
Moreover, the BCR-ABL mouse ALL model enabled us to compare the efficacy of CY with several other commonly used anticancer drugs.
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