  An important task for many if not all the scientific domains is efficient
knowledge integration, testing and codification. It is often solved with model
construction in a controllable computational environment. In spite of that, the
throughput of in-silico simulation-based observations become similarly
intractable for thorough analysis. This is especially the case in molecular
biology, which served as a subject for this study. In this project, we aimed to
test some approaches developed to deal with the curse of dimensionality. Among
these we found dimension reduction techniques especially appealing. They can be
used to identify irrelevant variability and help to understand critical
processes underlying high-dimensional datasets. Additionally, we subjected our
data sets to nonlinear time series analysis, as those are well established
methods for results comparison. To investigate the usefulness of dimension
reduction methods, we decided to base our study on a concrete sample set. The
example was taken from the domain of systems biology concerning dynamic
evolution of sub-cellular signaling. Particularly, the dataset relates to the
yeast pheromone pathway and is studied in-silico with a stochastic model. The
model reconstructs signal propagation stimulated by a mating pheromone. In the
paper, we elaborate on the reason of multidimensional analysis problem in the
context of molecular signaling, and next, we introduce the model of choice,
simulation details and obtained time series dynamics. A description of used
methods followed by a discussion of results and their biological interpretation
finalize the paper.
