Simple microbial competition

Below is an example code that will perform a simple time-integrated calculation of microbial growth for a methanogen and sulfate reducer population initialised in a similiar way as described on the last two pages.

import NutMEG.core as nmc
import NutMEG.models as nmm

R = nmc.Reactor(pH=7)

# introduce some reagents
CO2 = nmc.Reagent('CO2(aq)', R, phase='aq', amount=(0.001, 'mol'))
H2aq = nmc.Reagent('H2(aq)', R, phase='aq', amount=(0.001, 'mol'))
CH4aq = nmc.Reagent('Methane(aq)', R, phase='g', amount=(1e-7, 'mol'))
SO4 = nmc.Reagent('SO4-2', R, phase='aq', charge=-2, amount=(0.001, 'mol') )
HS = nmc.Reagent('HS-', R, phase='aq', charge=-1, amount=(1e-7, 'mol'))
H2O = nmc.Reagent('H2O(aq)', R, phase='l', activity=1.)


# the overall reaction is CO2 + 4H2 -> CH4 + 2H2O
thermalMG = nmc.Reaction(R, {'CO2(aq)':1, 'H2(aq)':4}, {'Methane(aq)':1, 'H2O(aq)':2})


base_MG = nmc.BaseOrganism('Methanogen', thermalMG)
# intialising a BaseOrganism like this is the bare minimum - and not enough for a
# growth simulation. At the very least we need to tell it anticipated maximum
# metabolic and growth rates.
base_MG.metabolism.set_max_rate(nmm.base_rate_models.FirstOrderChemical(1e-3))
base_MG.growth.set_max_rate(float('inf'))

# Defined like this, the organism will perform its metabolic reaction
# as if it had a first-order rate constant of 0.001, and grow as fast as
# the metabolic kinetics allows.
# Another concentration dependency can be added using a forcing factor.
# Let's add a Monod dependency on H2:
MG_Monod = nmm.forcing_factors.Monod('H2(aq)', 1e-5)
base_MG.metabolism.add_forcing_factor('F_D', MG_Monod)

# we can perform a quick habitability check to see if this organism is viable
print(base_MG.check_habitability(R))

# now let's make a community of methanogens numbering 1000
Pop1 = nmc.OrgPop(base_MG, 100)

# Next, create an organism that uses a different reaction - sulfate reducers
# This time we'll plan it's rate dependencies first and set them on initialisation.
SR_Monod = nmm.forcing_factors.Monod('H2(aq)', 1e-3) # more sensitive to H2 than methanogens
SR_MetRate = nmm.base_rate_models.FirstOrderChemical(5e-4)
SR_GroRate = nmm.base_rate_models.ConstantRate(float('inf'))

# the overall reaction is 4H2 + SO4(2-) + H(+) -> HS(-) + 4H2O
# but for this simple example we will omit the H+
thermalSR = nmc.Reaction(R, {'H2(aq)':4, 'SO4-2':1}, {'HS-':1, 'H2O(aq)':4})

# Finally, we can create the organism
base_SR = nmc.BaseOrganism('SulfateReducer',
  nmc.org.Metaboliser(thermalSR, base_rate=SR_MetRate, forcing_factors={'F_D': SR_Monod, 'F_B':nmm.forcing_factors.Bioenergetic()}),
  growth = nmc.org.Grower(base_rate=SR_GroRate))

# create an OrgPop of sulfate reducers
Pop2 = nmc.OrgPop(base_SR, 100)

# populate an ecosystem with the organisms and chemical environment.
ES = nmc.Ecosystem(R, [Pop1, Pop2])

# We will perform a time-integrated growth calculation using
# the nmm.GrowthSimulation class. We need to tell it exactly what
# outputs we are interested in monitoring through time, which we will do
# with the outparams dictionary below.
# try out adding output parameters!
outparams = {
    'mol H2' : R.composition['H2(aq)'].get_amount,
    'mol CO2' : R.composition['CO2(aq)'].get_amount,
    'mol HS' : R.composition['HS-'].get_amount,
    'mol SO4' : R.composition['SO4-2'].get_amount,
    'mol CH4' : R.composition['Methane(aq)'].get_amount,
    'mol H' : R.composition['H+'].get_amount,
    'DG_MG' : Pop1.base.metabolism.net_pathway.get_molar_gibbs,
    'DG_SR' : Pop2.base.metabolism.net_pathway.get_molar_gibbs,
    'mu_MG' :Pop1.base.get_growth_rate,
    'mu_SR' :Pop2.base.get_growth_rate,
    'k_MG' :Pop1.base.get_metabolic_rate,
    'k_SR' :Pop2.base.get_metabolic_rate,
}

GS = nmm.GrowthSimulation(ES, resultsdict=outparams)

# evolve the system for 4e5 seconds using a time-step of 100 seconds.,
# and return the requested parameters' variability.
popdict = GS.run(100., 4e5)

If you plot the outputs, it should look like this:

../../_images/competition.png