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 R2 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 (R2) 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 et al. BMC Systems Biology 2014 8:13   doi:10.1186/1752-0509-8-13
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