Kinetic nonlinear mixed-effects model fit by maximum likelihood

Structural model:
d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
           time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
           * parent
d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)
           * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
           exp(-k2 * time))) * parent - k_m1 * m1

Data:
507 observations of 2 variable(s) grouped in 15 datasets

Log-likelihood: -1326

Fixed effects:
 list(parent_0 ~ 1, log_k_m1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1,      log_k2 ~ 1, g_qlogis ~ 1) 
       parent_0        log_k_m1 f_parent_qlogis          log_k1          log_k2 
          100.7            -5.4            -0.1            -2.8            -4.5 
       g_qlogis 
           -0.1 

Random effects:
 Formula: list(parent_0 ~ 1, log_k_m1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1,      log_k2 ~ 1, g_qlogis ~ 1)
 Level: ds
 Structure: Diagonal
        parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 g_qlogis Residual
StdDev:        1     0.03             0.3    0.3    0.2      0.3        3