Individual phenotype prediction performance.

<p><b>A</b>: Prediction performance of the top scoring solutions for the eight in silico data sets. Red and green circles indicate Team Crux’s and Team Whole-Sale Modelers’ highest scoring solutions, respectively. Blue circles indicate the highest scoring solutions of the seven other teams. <b>B</b>: Coefficients of variation of the eight in silico data sets across the life cycles of 32 mutant strain cells. This figure indicates that the reaction flux and ChIP-seq measurements are the most variable, meaning that individual in silico cells with identical parameter values stochastically exhibit significantly different metabolic reaction fluxes and protein-DNA binding patterns. This suggests that these measurements might be the most difficult to reproduce and the least informative for parameter identification.</p

Team methods, parameter and prediction errors, and overall scores.

<p>“Estimation problem” column indicates which teams used the parameter error data. Teams are listed by overall score in descending order. Team Uniandes did not submit a solution and therefore was not scored. Not reported (N/R) indicates teams that did not report their approach.</p><p>Team methods, parameter and prediction errors, and overall scores.</p

Estimation performance of individual parameters.

<p>log₂ ratios of estimated and true mutant values of each unknown parameter. Red and green circles indicate Team Crux’s and Team Whole-Sale Modelers’ highest scoring solutions, respectively. Blue circles indicate the highest scoring solutions of the seven other teams.</p

Comparison of employed parameter estimation methods.

<p>Comparison of employed parameter estimation methods.</p

Team Whole-Sale Modelers iterative random forest parameter estimation method.

<p>Team Whole-Sale Modelers estimated the unknown parameters using a combination of differential evolution to explore the parameter space and random forests to exploit the available phenotype measurements. <b>A.</b> In the exploration phase, Team Whole-Sale Modelers generated an initial population of parameter estimates, and computed their prediction error relative to the mutant phenotype. Three individuals were then randomly selected without replacement from the population, and recombined using differential evolution (DE) to create new individuals. <b>B.</b> Periodically, Team Whole-Sale Modelers used random forests to add new individuals to the population. This was designed to exploit deeper structure in the phenotype measurements of the population. First, Team Whole-Sale Modelers used principal component analysis to create a training set that accounted for the majority of the variance in the phenotype measurements. Then, they used the first principal components to iteratively train random forest estimators for each individual parameter. After all of the parameters were estimated, they added the estimated parameter set to the DE population.</p