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Table 2 Application of the network aging model in yeast natural isolates

From: Estimating network changes from lifespan measurements using a parsimonious gene network model of cellular aging

StrainsAverage RLSNetwork ModelGomperz ModelAIC Comparison
  Rt0nGompertz RGompertz GWeibull AICGompertz AICNetwork AIC
101S31.46±0.8150.0025±9e−0436±5.56.7±0.6970.0012±0.000630.14±0.024582.72±16.95589.2±20.1608.56±12.37
M1-227.9±1.290.0034±0.00140.5±5.37.1±0.760.0026±0.001170.13±0.017393.19±10.51389.76±10.11397.27±6.96
M1326.6±1.0640.0034±9e−0440.8±4.27.4±0.640.003±0.00110.12±0.012520.85±18.13504±10.2510.83±8.61
M1436.32±1.6210.0035±0.00155.1±6.66.6±0.6370.0021±0.000930.09±0.011476.9±8.55470.91±8.86480.81±6.33
M2-824.77±0.7330.0034±6e−0442.4±48±0.1090.0043±0.001040.12±0.011738.35±14.92748.01±13.64746.64±13.4
M2232.07±1.3590.0033±0.00146.3±11.86.9±1.5080.002±0.000730.11±0.011449.76±9.87447.31±8.14456.56±6.9
M3228.15±0.9730.0027±0.001134.3±2.67±0.7140.0016±0.000460.15±0.011402.89±10.32411.68±8.67419.48±9.48
M3427.02±0.9970.0028±7e−0431.4±3.56.7±0.6160.0013±7e−040.16±0.018408.27±14.11397.81±10.93411.9±7.43
M536.85±0.9420.0034±6e−0474±7.27.8±0.3810.004±0.000850.07±0.0071321.34±14.321343.21±17.231339.61±14.58
M834.79±0.9690.0018±3e−0430.7±2.96.1±0.2074e−04±0.000180.16±0.016401.29±10.71404.45±10.6431.77±5.99
RM112N44.13±1.7510.0025±6e−0455.4±66.2±0.3750.0011±0.000470.09±0.01470.48±11.35466.79±10.27481.52±6.92
S288c26.36±1.5010.0051±0.001756.8±12.97.9±1.3410.0062±0.002020.09±0.016309.61±9.31310.8±8.3312.08±8.13
SGU5723.9±1.3190.0065±0.002158±19.77.9±1.5050.0077±0.002340.09±0.011439.51±8.84435.81±7.11438.37±7.38
YPS12835.08±1.1250.0026±8e−0441.9±3.96.5±0.4860.0011±0.000450.12±0.011507.62±12.55506.89±11.38522.34±8.71
YPS16334.41±0.6990.0023±5e−0437.3±2.66.4±0.4288e−04±0.000230.13±0.01923.64±14.97922.83±15.07957.15±10.43
  1. Replicative lifespans of each strain were resampled 100 times using the bootstrap with replacement method. Each resampled lifespan data set was fitted with the proposed network aging model using the maximal likelihood method. The maximal likelihood estimations from fitting to 100 bootstraps were averaged