Fig. 3. Phenotype landscape reversion efficacy.
(A) Converged behavior of the accuracy for the first order Taylor approximation used by the approximated ARC framework. The x axis is consecutive backward and forward state transition steps. Each dot represents the accuracy in a single network. (B) Comparison of target identification accuracy between sparse networks and dense networks (P value < 2.2 × 10−16). The accuracy in sparse networks is approximately twice that in highly connected networks. (C) Linear regression line represents the negative correlation between the distortion degree after alteration and reverse control score of the best single-node control (P value: 6.874 × 10−06, = 0.4845). Each dot represents the computed value for one of 33 networks. (D) Phenotype landscape reversion effectiveness comparison among control methods. Among all 33 biological Boolean networks, control targets identified by the ARC framework are always equally or more effective than other methods. Because the FVS method is only applicable to fixed-point attractors, it has zero phenotype landscape reversion effectiveness on oscillating attractors. LDOI refers to the logical domain of influence.