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Table 1 Comparison of the methodology applied to single-cell data [15] and our method on averaged data

From: Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations

Signalling Posterior Correctness Agreement
interaction probability (%)   with [15]
PLC → PIP2 25.5 n n
PLC → PIP3 28.1 y n
PIP3 → PIP2 58.7 y y
PIP3 → AKT 35.4 n y
ERK → AKT 70.8 y y *
PKC → JNK 100 y y
PKC → P38 100 y y
PKC → PKA 0 n n *
PKC → RAF 50.9 y y
PKC → MEK 89.9 y y
PKA → JNK 100 y y
PKA → P38 100 y y
PKA → RAF 100 y y
PKA → MEK 100 y y
PKA → ERK 100 y y
PKA → AKT 100 y y
RAF → MEK 48.3 n n
MEK → ERK 87.5 y y
PLC → PKC 95.6 y y
PIP2 → PKC 57.3 y n
  1. Signalling interactions are represented by the molecular components of the signaling cascades detailed in reference [15]
  2. *Inferred as novel in reference [15]