  In the past years, many computational methods have been developed to infer
the structure of gene regulatory networks from time-series data. However, the
applicability and accuracy presumptions of such algorithms remain unclear due
to experimental heterogeneity. This paper assesses the performance of recent
and successful network inference strategies under a novel, multifactorial
evaluation framework in order to highlight pragmatic tradeoffs in experimental
design. The effects of data quantity and systems perturbations are addressed,
thereby formulating guidelines for efficient resource management.
  Realistic data were generated from six widely used benchmark models of
rhythmic and non-rhythmic gene regulatory systems with random perturbations
mimicking the effect of gene knock-out or chemical treatments. Then,
time-series data of increasing lengths were provided to five state-of-the-art
network inference algorithms representing distinctive mathematical paradigms.
The performances of such network reconstruction methodologies are uncovered
under various experimental conditions. We report that the algorithms do not
benefit equally from data increments. Furthermore, for rhythmic systems, it is
more profitable for network inference strategies to be run on long time-series
rather than short time-series with multiple perturbations. By contrast, for the
non-rhythmic systems, increasing the number of perturbation experiments yielded
better results than increasing the sampling frequency. We expect that future
benchmark and algorithm design would integrate such multifactorial
considerations to promote their widespread and conscientious usage.
