We interpret G with each edge colored by read mappings as a flow network, considering the read volume assigned to every (super-) edge as a flux created by the expression of the underlying supporting transcripts T.Consequently, given an edge the contribution of the supporting transcripts to the flux observed along e can be described by a linear equation
where fi represents a factor that expresses the fraction of the respective transcript expression ti observed between taile and heade. In the trivial case, fi corresponds to the proportion of the interval [taile; heade] in comparison to the entire length of the processed transcript. The correction factor in Eq.1 is to compensate for divergence from the expectation created by stochastical sampling intrinsic to RNA-Seq experiments.
The crux of the flux is that an RNA-Seq experiment provides a series of observations on the underlying expression level ti along the transcript body. Following tradition in transportation problems, we model all of these observations as a system of linear equations by inferring Equation 1 on all . Subsequently, the linear equations spanned by a locus are resolved by the objective function
Solving the linear program (Eq.2) imposed by a locus intrinsically provides an estimate for the expression level ti of all alternative transcripts that are annotated.