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Our initial FBA predictions used an objective function that relates to the ability of the bacterium to convert a particular carbon source into biomass.

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The MB_2K predictions used a BayesCπ model [ 18].

The Dutch/Flemish genomic predictions used a Bayesian mixture model for SNP effects, along with polygenic effects [ 16], assuming that most SNP had small effects and a few SNP had moderate or large effects and the French genomic predictions used a mixed linear model with a polygenic effect and random haplotype effects across the genome [ 17].

We performed DE predictions using an increasing number of replicates.

Retrospective evaluation of the model's predictions using an independent testing set.

We agree that validation of the predictions using an alternative method is important.

We tested the categorization predictions using an approach similar to Experiment 1.

One such modified gene model is commd10 (DDB_G0275249): the gene prediction used an incorrect splice site.

These analyses were based upon Latin Hypercube Sampling (LHS), a sampling technique that enables us to make predictions using a range of parameter values for each parameter.

The historic wildfire hazards are plotted against model predictions using a scatter plot, and a linear regression curve is fitted.

The parameters were obtained by comparing experimental values with theoretical predictions using a constrained nonlinear least squares approach.

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