HPLC	High Pressure Liquid Chromatography
CHO	Chinese Hamster Ovary
MC	Monte Carlo
FA	Functional Analysis
Gln	Glutamine
Lac	Lactate
$\mu$	biomass-specific growth rate
$q_p$	biomass-specific product formation rate
$q_S$	biomass-specific substrate uptake rate
$q_i$	biomass-specific rate of component i
$c_i$	concentration of component i
X	biomass
S	substrate
P	product
D	dilution rate
$c_{S,feed}$	substrate concentration in the feed
$F_S$	feed rate
$t_k$	t of sample k
$c_{S,t,meas}$	measured concentration at t
$\overline{X}$	true input
$\overline{x}$	reconstructed input by regression
$\overline{Y}$	measured output
$\overline{y}$	predicted regression output
${\mathbf{W}}_{\mathbf{y}}$	weighting matrix of predictions
${\mathbf{W}}_{\mathbf{x}}$	weighting matrix of input variables
S	weighted sum of squared error
$\sigma$	standard deviation of measured output or true input
N	number of Monte Carlo iterations
f	arbitrary function converting input $\overline{x}$ to output $\overline{y}$
${\overline{x}}_{\mathrm{sampled}}$	sampled input from Gaussian-distributed error
$\overline{y_k}$	calculation result of sampled input
${\overline{y}}_{i,\mathrm{sampled}}$	sampled input from output i
${\overline{\sigma }}_i$	standard deviation after calculation step i
$P_{k(1:N)}$	regression parameter for N Monte Carlo evaluations
${\mathit{cov}}_{\mathit{P}}$	parameter covariance
${\overline{\sigma }}_{\mathit{P}}$	parameter standard deviation
${\overline{\sigma }}_{\widehat{y}}$	standard deviation of regression output