Abstract | The theory is generally applicable and amenable to predictions if the dose-response curve for gene repression is noncooperative with a unit Hill coefficient , which is observed for GR-regulated repression of AP1LUC reporter induction by phorbol myristate acetate. |
Discussion | This is supported by the observation of noncooperative dose-response curves with unit Hill coefficient in both GR-mediated repression (Fig. |
Discussion | The binding of preformed GR dimers would yield a dose-response curve with greater than unity Hill coefficient . |
Introduction | The theory is based on the fact that the dose-response curve for gene induction is noncooperative With a Hill coefficient of one [20]. |
Non-cooperative dose-response | Experimentally, the dose-response of gene activity A in steroid-regulated repression has been found to be noncooperative With a Hill coefficient of one (see Fig. |
Theory of non-cooperative gene induction | The goal is to calculate this function and determine conditions for when it is noncooperative with unit Hill coefficient . |
Theory of non-cooperative gene induction | In general, the dose-response for this system Will not have unit Hill coefficient [20]. |
Discussion | 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. |
E-glc-atp | 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). |
E-glc-atp | 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. |
E-glc-atp | In order to compare our results, we also show experimentally measured Hill coefficients reported by Izui et al. |
Predictive power of the recovery time for injection-based protocols | 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]. |
Predictive power of the recovery time for injection-based protocols | 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). |
Supporting Information | The effect of the Hill coefficient on the predictive powers of recovery time. |
Supporting Information | (A,C) Recovery time depends less on antibiotic concentration if the Hill coefficient (H) is high enough. |
Supporting Information | 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. |
Sensitivity analysis of gene expression model | 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). |
Supporting Information | ( Hill coefficient 11H 2 100.) |
Supporting Information | ( Hill coefficient 11 H = 100). |
Supporting Information | 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. |
The control targets transcend the logical modeling framework | 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. |
The control targets transcend the logical modeling framework | 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). |