Available data in WormBase include gene models, allelic variations, mutant phenotypes, anatomy function, expression patterns, gene interactions, and human disease relevance.
For systems with stochastic parameters described by any kind of continuous probability distribution, the procedures for evaluating the stochastic flexibility and the expected stochastic flexibility metrics are identical for both linear and nonlinear models once the parametric feasibility function expressions have been generated.
Table 1 Model functions considered in this study along with their expressions.
Borrowing dynamic theory from engineering, we have used the second order ODE to model the expression function of the gene measured by RNA-seq.
In a mouse model the expression and function of the canalicular phospholipid flippase (Mdr2) significantly decreased under the influence of TPN [ 56].
To demonstrate this, we examined the in vivo accumulation of a specific P-gp substrate, LD800, in the spinal cord of the ALS mouse model where expression and function of P-gp increases as disease progresses 4. Symptomatic mice have a marked decrease in LD800 accumulation compared to nontransgenic and P-gp−/− mice (Fig. 2A and B).
The R package lme4 (Bolker, 2013) was used to fit linear mixed models using maximum likelihood to model expression as a function of genetic interactions.
In order to model protein expression as a function of mRNA expression (disregarding for the moment all other factors that may influence protein expression), a decision has to be made regarding the form of the functional relationship.
To determine whether 25(OH D or IFNB-1b was associated with gene expression, generalized linear models were used to model gene expression as a function of 25(OH D levels, or IFNB-1b, adjusting for age, sex, treatment at the time of 25(OH D measurement, and the interaction of 25(OH D level and IFNB-1b.
The linear model and differential expression functions were applied to all microarray data sets.
