Data-based reconstruction of gene regulatory networks of fungal pathogens.
(2016) Data-based reconstruction of gene regulatory networks of fungal pathogens. Front Microbiol 7, 570.
In the emerging field of systems biology of fungal infection, one of the central roles
belongs to the modeling of gene regulatory networks (GRNs). Utilizing omics-data,
GRNs can be predicted by mathematical modeling. Here, we review current advances
of data-based reconstruction of both small-scale and large-scale GRNs for human
pathogenic fungi. The advantage of large-scale genome-wide modeling is the possibility
to predict central (hub) genes and thereby indicate potential biomarkers and drug
targets. In contrast, small-scale GRN models provide hypotheses on the mode of gene
regulatory interactions, which have to be validated experimentally. Due to the lack of
sufficient quantity and quality of both experimental data and prior knowledge about
regulator–target gene relations, the genome-wide modeling still remains problematic for
fungal pathogens. While a first genome-wide GRN model has already been published for
Candida albicans, the feasibility of such modeling for Aspergillus fumigatus is evaluated
in the present article. Based on this evaluation, opinions are drawn on future directions of
GRN modeling of fungal pathogens. The crucial point of genome-wide GRN modeling is
the experimental evidence, both used for inferring the networks (omics ‘first-hand’ data
as well as literature data used as prior knowledge) and for validation and evaluation of
the inferred network models.
doi: 10.3389/fmicb.2016.00570 PMID: 27148247