While RNA-seq is a powerful hypothesis-free global sampling tool for single cell experiments, directed approaches for validation and absolute quantitation of individual gene targets in single cells are lacking.
Target prediction algorithms are undoubtedly valuable tools to researchers in the field providing a rapid appreciation of the potential processes impacted by a particular miRNA and prioritising potential direct targets for validation assays.
Relevant analytical results are compared, for validation purposes, to direct integration of the reference and reduced models.
This highlight both the quality of the network and the functional modules we predicted as guide for direct experimental validation.
For validation, the direct problem solver is verified by the existing solutions for some special cases.
Based on these simulations, a prototype room was constructed and experimental control measurements were carried out and used for validation and direct comparison with the CFD predictions.
Most studies of HRMA detection of cancer-specific mutations in tumour biopsies used direct sequencing to confirm positive results (Krypuy et al, 2006; Reed et al, 2007; Do et al, 2008; Simi et al, 2008; Ma et al, 2009; Deschoolmeester et al, 2010), but we argue that direct sequencing is not reliable for validation of positive HRMA results in cases of a low MUT allele concentration.
For direct validation, we study a metabolic branch point in Escherichia coli and experimentally confirm the model prediction that enzyme agglomerates can accelerate the processing of a shared intermediate by one branch, and thus regulate steady-state flux division.
The study design did not allow for direct validation, such as by assessing outcomes.
Simulated data have advantages in that the underlying causal mutations and simulated breeding values are available for direct validation.
In the first level, the aliquots of total RNA that was subjected previously to the RNAseq experiment were taken for direct validation of the RNAseq results.
