Time-series of high throughput gene sequencing data intended for gene
regulatory network (GRN) inference are often short due to the high costs of
sampling cell systems. Moreover, experimentalists lack a set of quantitative
guidelines that prescribe the minimal number of samples required to infer a
reliable GRN model. We study the temporal resolution of data vs quality of GRN
inference in order to ultimately overcome this deficit. The evolution of a
Markovian jump process model for the Ras/cAMP/PKA pathway of proteins and
metabolites in the G1 phase of the Saccharomyces cerevisiae cell cycle is
sampled at a number of different rates. For each time-series we infer a linear
regression model of the GRN using the LASSO method. The inferred network
topology is evaluated in terms of the area under the precision-recall curve
AUPR. By plotting the AUPR against the number of samples, we show that the
trade-off has a, roughly speaking, sigmoid shape. An optimal number of samples
corresponds to values on the ridge of the sigmoid
