Our model performs significantly better with data from the true distribution, suggesting that our predictions are due to the specific configuration of gene expression and the metabolites that we are testing and not due to a global trend in the expression data.
Each entity carries its expression levels in the set (Q_{v}= {0,ldots,n_{v}}.) (State) A state is a configuration of the expression levels of all entities at any instance of time and it is denoted by a tuple (s in S), where: begin{aligned} S = Pi _{ v in X} Q_{v}.
Assuming steady state dynamics would collapse the trajectories representing these shifts into a single attractor point at the final configuration of gene expression.
With this configuration, the onset of expression and tissue specificity of secreted, sub-cellular compartmentalized or short-lived gene products can be easily detected.
To identify the attractors of each model, we used the program Atalia [[ 56]; freely available] by following the dynamics of all possible initial configurations of gene expression.
Many chloroplast transformation vectors with different configuration of the chloroplast expression cassettes were designed and applied [ 7, 11, 16, 17].
Given the logical rules, it is possible to follow the dynamics of the network for any initial configuration of the nodes expression states.
Here we have investigated the impact of motifs with positional bias in the configuration of promoters driving expression in various body tissues, and used this property to uncover potentially novel tissue-specific regulators.
In such a genetic configuration, the expression of the Hprt1 locus can be theoretically used as an index of XP and XM activities by growing cells on two different selective media.
For conventional (zero pre-strain) configuration of the resonator, this expression turns out to be same as Lifshitz-Roukes original expression for thermo-elastic damping.
