Rate forcing factors
Forcing factors \(0 \geq F_{i} \leq 1\) parameterise the offset from optimality a biological rate (e.g., metabolic rate or growth rate) experiences based on environmental conditions and organism specific constraints. In NutMEG they are computed using ForcingFactor objects and several are stored in the NutMEG.models subpackage. They can be thought of as the fractional reduction in rate versus the maximum, for example:
Several functions can be proposed as a biological forcing factor, depending on the nature of the limitation. NutMEG allows any arbitrary child class of ForcingFactor to be used, provided it has a compute() function. Users can thus define their own functions or make use of the numerous built in options.
The snippet below creates four ForcingFactors that each model the effect of a change in concentration of substrate/inhibitor H2S, which can inhibit when the concentration is too large, but for organbisms using it as a substrate, it also has a lower viability limit.
# all values chosen arbitrarily. For illustration only.
import NutMEG as nm
import NutMEG.core as nmc
import NutMEG.models as nmm
R = nmc.Reactor()
nmc.Reagent('H2S', R, amount=(0.001, 'molal'), thermo=False)
BP = nmm.forcing_factors.BiologicalPerformance('H2S', 0.075, 0.01, 0.081, 2.)
M = nmm.forcing_factors.Monod('H2S', 0.05)
LL = nmm.forcing_factors.LowerLimit('H2S', 0.01)
UL = nmm.forcing_factors.UpperLimit('H2S', 0.081)
Below is how the forcing factors manifest as a function of H2S concentration:
