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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]