Random sampling of the kinetic behaviour for this enzyme showed that the experimental Hill coefficient for this enzyme lies surprisingly fairly close to the average Hill coefficient sampled, highlighting the importance of the architecture of its reaction mechanism.

To this end, we uniformly sampled the kinetic space for this enzyme and counted the frequency of parameter sets displaying positive cooperativity for glucose (estimated Hill coefficient , nH>1).

Remarkably, the sampled kinetics contained the experimentally observed Hill coefficient for this enzyme (nHJeal = 1.70 :01 [49]), within one standard deviation (nH,sampled = 1.77:0.5), which confirms the suitability of the mnemonic model for modelling this kinetic behaviour.

In order to compare our results, we also show experimentally measured Hill coefficients reported by Izui et al.

B-lactams’ killing rate is time, not dose, dependent and is reflected in the model’s lysis rate’s nonlinear dependence on the antibiotic concentration ( Hill coefficient = 3) [59].

Additionally, the predictive power of the recovery time is maintained for an antibiotic with dose-dependent killing (Hill coefficient = 1) or an antibiotic with time-dependent killing ( Hill coefficient = 10): a multi-dose regimen will clear a population if the time between doses is less than one recovery time, regardless of effective antibiotic concentration and degree of antibiotic-mediated killing (82 Fig).

The effect of the Hill coefficient on the predictive powers of recovery time.

(A,C) Recovery time depends less on antibiotic concentration if the Hill coefficient (H) is high enough.

Despite the different Hill coefficients , both models followed the trend Where periods less than one recovery time eliminate the population as long as the initial antibiotic concentration is sufficiently high to cause significant initial decline.

In our gene expression model, we idealize the relationship between dl concentration and gene expression rate as a hard-threshold ( Hill coefficient 11 H = 100).

( Hill coefficient 11H 2 100.)

( Hill coefficient 11 H = 100).

Validation of some stable motif control intervention targets in Table 1 for different Hill coefficients (n) in the T -LGL leukemia differential equation network model.

In the ODE models the node state variables 6,. can take values in the range [0, 1]; the differential equa-smooth Hill-type function parameterized by Hill coefficients and threshold parameters, and T,is a timescale parameter.

We also find that the effectiveness of the interventions is mostly unchanged by varying the Hill coefficients (S5 Table), varying the the timescale parameters T,- and thresholds (S6 Table), or fixing the intervened node variables close to but not exactly at the intervention-prescribed values (S7 Table).