  Inference of gene regulatory networks has been an active area of research for
around 20 years, leading to the development of sophisticated inference
algorithms based on a variety of assumptions and approaches. With the always
increasing demand for more accurate and powerful models, the inference problem
remains of broad scientific interest. The abstract representation of biological
systems through gene regulatory networks represents a powerful method to study
such systems, encoding different amounts and types of information. In this
review, we summarize the different types of inference algorithms specifically
based on time-series transcriptomics, giving an overview of the main
applications of gene regulatory networks in computational biology. This review
is intended to give an updated overview of regulatory networks inference tools
to biologists and researchers new to the topic and guide them in selecting the
appropriate inference method that best fits their questions, aims and
experimental data.
