Resolution:
## Figure 2.
Scores and correlation between parameter and protein prediction distances for model
1. A. Graph representing the dynamics of the mRNAs from the 9 genes for model 1 network.
Dots are the data with noise, lines represent the data without noise and shades the
associated noise model B. Overall scores from the participants calculated from the p-values as indicated by
the formula. P-values were obtained from the two different metrics used for challenge
scoring described in Additional file 3: Figure S1. C. The participant distances defined for scoring the submitted predictions for the
parameters and the protein perturbation predictions are plotted respectively in the
y-axis Dparam and x-axis Dprot. Each team is represented by its rank number in the final scoring except for the
best performer Orangeballs. The R^{2} coefficient for a linear fit in log-scale is 0.23; the red line is a visual reference
for a perfect fit. D. For each of the 45 parameters in the model, the vector of parameter values submitted
by the 12 participants is correlated (R^{2}) to the unique vector of Dprot values, the protein perturbation prediction distance values. The graph shows the
parameters ordered by increasing correlation value, with from left to right, pro5_strength,
v10_Kd, pro3_strength, v9_Kd, v4_h, v8_Kd, v8_h, v1_Kd, v11_h, v1_h, pro7_strength,
v4_Kd, v12_Kd, pro8_strength, rbs9_strength, v10_h, pro2_strength, v9_h, pro1_strength,
v12_h, v5_h, pro4_strength, v3_h, v7_h, rbs7_strength, v3_Kd, rbs2_strength, pro9_strength,
v6_h, rbs1_strength, v7_Kd, pro6_strength, v6_Kd, v11_Kd, v2_Kd, v5_Kd, v13_h, p_degradation_rate,
v2_h, rbs3_strength, rbs6_strength, rbs5_strength, rbs8_strength, rbs4_strength, v13_Kd.
Meyer |