Modeling gene-regulatory networks to describe cell fate transitions and predict master regulators

TETRAMER workflow to reconstruct TF regulatory networks by the integrating publicly available GRN information into temporal transcriptomes. a TETRAMER reconstructs first a temporal GRN for a cell fate transition by integrating publicly available GRN sources in the temporal transcriptomes established for this transition. b Then the temporal propagation of the flux of transcription regulatory information is simulated across the entire GRN, thus establishing a comprehensive connectivity map between all nodes, which represent essentially TFs. For computation, the transcriptional state of each node is discretized (0, 1, -1), as shown. c Propagation of the transcription regulatory information applies three logical rules: (i) any connectivity to unresponsive nodes is eliminated, as the signal propagation is terminated; (ii) the flux of information should be coherent between the type of transcription regulation (positive or negative) and the discretized expression level of the interconnected nodes; (iii) the directionality of the transcriptional regulation should comply with the temporal signal flux. Nodes/edges that do not comply with these rules are excluded from the GRN map, as they are not considered specific for the cell fate transition event. Furthermore, nodes/edges downstream of the excluded events are neither considered (herein depicted in gray). d Within the reconstituted GRN all nodes are ranked by their master regulator index (MRI), corresponding to the fraction of nodes that are regulated by a given TF upon its activation and signal propagation. The relevance of this ranking is challenged by performing the same procedure in a GRN with randomized connectivities. Thus, TETRAMER identifies master regulator TFs among several thousand differentially expressed genes during cell fate transition