Figure 4. Experimental validation of the landscape of tomato agronomic properties by using genetic perturbations.

Heuristic exploration (A) and statistical significance (B) of the landscape of multiple desired agronomic properties of tomato fruit perturbing its effective TRN adding multiple genetic changes and, predictive power (C–F) for optimizing the levels of volatile compounds and identifying compounds in closed metabolic pathways. (A) Median efficiencies reached by transcriptional perturbation based in gene knockouts or over-expression to improve agronomic properties. (B) Average number of single gene perturbations that overcome an efficiency threshold in the top 5 RILs scored by single perturbation (light bars; error bars represent standard deviation for the selected RILs) and average probability of selecting the same multiple-perturbation commonly in a set of RILs (dark bars; error bars show standard deviation for all genes of the TRN). Precision, recall and F-score (green, red and blue lines, respectively) compare observed experimentally volatile compound correlations vs inferred set of potential genetic perturbations (gene knockout (C, D) or over-expression (E, F)) shared to optimize each compound independently. Note that experimental metabolite correlations r<0.5 were not considered in (D, F).