Even though we assumed specific molecular mechanisms underlying this collective antibiotic response, our model illustrates that the predictive power of the recovery time is maintained for different specific molecular mechanisms and for different initial conditions.

Predictive power of recovery time for intravenous-drip protocols

Thus, we also modeled the predictive power of the recovery time in intravenous (IV)drip based protocols, where a set concentration of antibiotic is delivered over a set duration during each dosing period.

Predictive power of the recovery time for injection-based protocols

We first tested the predictive power of the recovery time in injection-based dosing protocols.

Our modeling results confirmed the predictive power of the recovery time: as long as the initial antibiotic concentration is sufficiently high to cause significant initial lysis, the population will reach a high final density if the period is greater than the recovery time; the population goes extinct otherwise (Fig.

We thank Dr. Stephan Feller for providing the Raf-RBD construct, Dr. Bettina Hahn for the mass spectrometry measurements and Dr. Clemens Kreutz for the support for the analysis of the predictive power of the models.

We hypothesized that the advantage of a reduced model resides in an improved predictive power .

To compare the predictive power of the complete model and the model 4_8_12, we analyzed with the model the dynamic behaviour of a protein that has not been measured experimentally.

These results show that the selected reduced model 4_8_12 has a better predictive power than the complete model.

Predictive power .

To compare the predictive power of the selected candidate model 4_8_12 and the complete model, we utilize prediction profiles as described [39].

The coordination of the moth’s downstroke muscles is a very simple synergy hypothesis: that one variable describing the combination of these two muscles’ activities has as much predictive power as considering the two muscles independently.

If the two-variable independence model has greater predictive power than the one-variable synergy or redundancy models, then each muscle contributes significantly to the decoding of torque.

To test the predictive power of the reconstructions, we cross-validated the feature analysis using 70% of each decile of the data as a training set to predict the remaining 30%.