Abstract
Most studies of gene regulatory network (GRN) inference have focused extensively on identifying the interaction map of the GRNs. However, in order to predict the cellular behavior, modeling the GRN in terms of logic circuits, i.e., Boolean networks, is necessary. The perturbation techniques, e.g., knock-down and over-expression, should be utilized for identifying the underlying logic behind the interactions. However, we will show that by using only transcriptomic data obtained by single-perturbation experiments, we cannot observe all regulatory interactions, and this invisibility causes ambiguity in our model. Consequently, we need to employ the data of multiple omics layers (genome, transcriptome, and proteome) as well as multiple perturbation experiments to reduce or eliminate ambiguity in our modeling. In this paper, we introduce a multi-step perturbation experiment to deal with ambiguity. Moreover, we perform a thorough analysis to investigate which types of perturbations and omics layers play the most important role in the unambiguous modeling of the GRNs and how much ambiguity will be eliminated by considering more perturbations and more omics layers. Our analysis shows that performing both knock-down and over-expression is necessary in order to achieve the least ambiguous model. Moreover, the more steps of the perturbation are taken, the more ambiguity is eliminated. In addition, we can even achieve an unambiguous model of the GRN by using multi-step perturbation and integrating transcriptomic, protein-protein interaction, and cis-element data. Finally, we demonstrate the effect of utilizing different types of perturbation experiment and integrating multi-omics data on identifying the logic behind the regulatory interactions in a synthetic GRN. In conclusion, relying on the results of only knock-down experiments and not including as many omics layers as possible in the GRN inference, makes the results ambiguous, unreliable, and less accurate.
Highlights
Provided that a transition edge in a single perturbation state diagram (SPSD) is represented by a dashed-line arrow, we can consider it as a solid-line arrow if other omics layers can give evidence of the effect of its corresponding regulatory factors (RFs) on the target gene
R1: a perturbation Di or Oi on RFi in the SPSD changes the state of the target gene; R2: the corresponding cis-element of RFi exists in the upstream of the target gene; R3: RFi has a physical protein-protein interaction (PPI) with a previously proven regulator of the target gene
For the initial state 10 we do not have any evidence for the effect of RF1 on the target gene; any PPI between RF1 and RF2 cannot help to reveal more regulatory interactions for this initial state
Summary
Analysis of logic-based modelling of gene regulatory networks access the data in the same manner as the authors. The authors did not have any special access privileges that others would not have
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