def microbe_enzproduction_rate(self, EnzGenes, EnzAttrib):
"""
Derive the taxon-specific fraction of 'available C' as enzymes.
This fraction only varies with taxon, which is independent of gene within a taxon
Parameters:
EnzGenes: taxon-specific available genes for enzyme;dataframe (taxon * genes); from the above microbe_enzyme_gene() method
EnzAttrib: dataframe [enzyme * 4 (C,N,P,maintenence cost)]; basic enzyme stoichiometry
Constit_Prod_min: min of constitutive enzyme production efficiency
Constit_Prod_max: max of constitutive enzyme production efficiency
Enz_Prod_min: min of inducible enzyme production efficiency
Enz_Prod_max: max of inducible enzyme production efficiency
NormalizeProd: scalar; normalize enzyme production for the number of enzyme genes (0: No, 1: Yes)
Returns:
Tax_EnzProdConsti: series; taxon-specific fraction of available C as enzyme for each taxon
Tax_EnzProdInduce: series; taxon-specific fraction of available C as enzyme for each taxon
Tax_Induce_Enzyme_C: dataframe; taxon-specific fraction of available C as enzyme for each enzyme
Tax_Consti_Enzyme_C: dataframe; taxon-specific fraction of available C as enzyme for each enzyme
"""
# LHS sampling of constitutive and inducible enzyme production efficiency for each taxon
Tax_EnzProdConsti_array = LHS(self.n_taxa, self.Constit_Prod_min,
self.Constit_Prod_max, 'uniform')
Tax_EnzProdInduce_array = LHS(self.n_taxa, self.Enz_Prod_min,
self.Enz_Prod_max, 'uniform')
Tax_EnzProdConsti = pd.Series(data=Tax_EnzProdConsti_array,
index=self.index,
dtype='float32')
Tax_EnzProdInduce = pd.Series(data=Tax_EnzProdInduce_array,
index=self.index,
dtype='float32')
# Derive the production rate of every single gene of each taxon
EnzProdConsti = EnzGenes.mul(Tax_EnzProdConsti, axis=0)
EnzProdInduce = EnzGenes.mul(Tax_EnzProdInduce, axis=0)
# Normalize constituitive and inducible enzyme production
if self.NormalizeProd == 1:
Normalize = EnzGenes.sum(axis=1) / self.Enz_per_taxon_max
EnzProdConsti = EnzProdConsti.divide(Normalize, axis=0)
EnzProdInduce = EnzProdInduce.divide(Normalize, axis=0)
EnzProdConsti[Normalize == 0] = 0
EnzProdInduce[Normalize == 0] = 0
# Relative enzyme carbon cost for each enzyme gene of each taxon
Tax_Consti_Enzyme_C = EnzProdConsti.mul(
EnzAttrib["C_cost"], axis=1) #C_cost = 1; so it doesn't matter
Tax_Induce_Enzyme_C = EnzProdInduce.mul(EnzAttrib["C_cost"], axis=1)
return Tax_EnzProdConsti, Tax_EnzProdInduce, Tax_Consti_Enzyme_C, Tax_Induce_Enzyme_C
def microbe_uptake_cost(self, UptakeGenes):
"""
Derive the taxon-specific cost of every single gene of uptake transporter.
Note this cost (in terms of Fraction of Biomass C) is same among different genes from the same taxon
Parameters:
UptakeGenes: dataframe; taxon-specific transporter genes; from the above method, microbe_uptake_gene()
Uptake_C_cost_min: 0.01 transporter mg-1 biomass C
Uptake_C_cost_max: 0.1 transporter mg-1 biomass C
NormalizeUptake: normalize uptake investment for the number of uptake genes (0: No, 1: Yes)
Returns:
UptakeProd_series: series; taxon-specific transporter cost
UptakeGenes_Cost: dataframe; taxon- and gene-specific transporter cost
"""
# LHS sampling of transporter production efficiency for each taxon
UptakeProd_array = LHS(self.n_taxa, self.Uptake_C_cost_min,
self.Uptake_C_cost_max, 'uniform')
UptakeProd_series = pd.Series(data=UptakeProd_array,
index=self.index,
dtype='float32')
# If true (== 1), then the uptake potential is normalized to the total number of uptake genes (n_uptake)
if self.NormalizeUptake == 1:
Normalize = UptakeGenes.sum(axis=1) / self.n_uptake
UptakeGenes = UptakeGenes.divide(Normalize, axis=0)
# Derive gene-specific production efficiency of each taxon
UptakeGenes_Cost = UptakeGenes.mul(UptakeProd_series, axis=0)
return UptakeProd_series, UptakeGenes_Cost
def microbe_osmoproduction_rate(self, OsmoGenes):
"""
Distribution of osmolyte production rate (i.e.,proportion of available C as osmolytes).
Parameters:
OsmoGenes: dataframe(taxon*genes); generated by the above microbe_osmolyte_gene() method
Osmo_Constit_Prod_min: min of constitutive osmolyte production efficiency
Osmo_Constit_Prod_max: max of constitutive osmolyte production efficiency
Osmo_Induci_Prod_min: min of inducible osmolyte production efficiency
Osmo_Induci_Prod_max: max of inducible osmolyte production efficiency
NormalizeProd: 0:No; 1:Yes
Returns:
Tax_OsmoProd_Consti_series:
Tax_OsmoProd_Induci_series:
Tax_Consti_Osmo_C: taxon-specific fraction of available C as osmolytes for each gene
Tax_Induce_Osmo_C: taxon-specific fraction of available C as osmolytes for each gene
"""
# LHS sampling of osmolyte production efficiency for every taxon
Tax_OsmoProd_Consti = LHS(self.n_taxa, self.Osmo_Consti_Prod_min,
self.Osmo_Consti_Prod_max, 'uniform')
Tax_OsmoProd_Induci = LHS(self.n_taxa, self.Osmo_Induci_Prod_min,
self.Osmo_Induci_Prod_max, 'uniform')
Tax_OsmoProd_Consti_series = pd.Series(data=Tax_OsmoProd_Consti,
index=self.index,
dtype='float32')
Tax_OsmoProd_Induci_series = pd.Series(data=Tax_OsmoProd_Induci,
index=self.index,
dtype='float32')
# Derive the production rate of every single gene of each taxon
Tax_Consti_Osmo_C = OsmoGenes.mul(Tax_OsmoProd_Consti_series, axis=0)
Tax_Induci_Osmo_C = OsmoGenes.mul(Tax_OsmoProd_Induci_series, axis=0)
# Normalize constituitive and inducible osmolyte production
if self.NormalizeProd == 1:
Normalize = OsmoGenes.sum(axis=1) / self.Osmo_per_taxon_max
Tax_Consti_Osmo_C = Tax_Consti_Osmo_C.divide(Normalize, axis=0)
Tax_Induci_Osmo_C = Tax_Induci_Osmo_C.divide(Normalize, axis=0)
Tax_Consti_Osmo_C[Normalize == 0] = 0
Tax_Induci_Osmo_C[Normalize == 0] = 0
return Tax_OsmoProd_Consti_series, Tax_OsmoProd_Induci_series, Tax_Consti_Osmo_C, Tax_Induci_Osmo_C
def enzyme_Vmax(self, ReqEnz):
"""
Pre-exponential constants for enzymes.
Parameters:
ReqEnz: substrate required enzymes, from the substrate module
Vmax0_min: 5 (mg substrate mg-1 enzyme day-1); Minimum Vmax for enzyme
Vmax0_max: 50 (mg substrate mg-1 enzyme day-1); Maximum Vmax for enzyme
Specif_factor: default 1; Efficiency-specificity
Returns:
Vmax0: dataframe(substrates * enzymes); feed the the method, enzyme_Km(), below
Vmax0.T: dataframe(enzyme*substrate); will feed the expand()
"""
Vmax0_array = LHS(self.n_substrates * self.n_enzymes, self.Vmax0_min,
self.Vmax0_max, 'uniform')
Vmax0_array = Vmax0_array.reshape(self.n_substrates, self.n_enzymes)
columns = ['Enz' + str(i) for i in range(1, self.n_enzymes + 1)]
Vmax0 = pd.DataFrame(data=Vmax0_array,
index=self.substrate_index,
columns=columns,
dtype='float32')
# Account for efficiency-specificity tradeoff by dividing Vmax_0 by the number of substrates (or monomers)
# targeted and multiplied by a specificity factor
RE1 = ReqEnz.loc['set1'].iloc[range(self.n_substrates), :]
RE2 = ReqEnz.loc['set2'].iloc[range(self.n_substrates), :]
RE2 = RE2.fillna(0)
# Total num. of substrates that each enzyme can target: series; indexed by enzyme
total_substrates = RE1.sum(axis=0) + RE2.sum(axis=0)
if self.Specif_factor == 0:
total_substrates[total_substrates > 1] = 1
else:
total_substrates[total_substrates > 1] = total_substrates[
total_substrates > 1] * self.Specif_factor
Vmax0 = Vmax0.divide(total_substrates, axis=1)
Vmax0.loc[:, total_substrates == 0] = 0 # get rid of NAs
Vmax0 = Vmax0.astype('float32')
return Vmax0, Vmax0.T
def enzyme_uptake_Vmax(self, Uptake_ReqEnz):
"""
Pre-exponential constants for uptake.
Parameters:
Uptake_ReqEnz: uptake required enzymes; monomers * enzymes; from the monomer module
Uptake_Vmax0_min: 1 (mg substrate mg-1 substrate day-1) Minimum uptake Vmax
Uptake_Vmax0_max: 10 (mg substrate mg-1 substrate day-1) Maximum uptake Vmax
Specif_factor: default 1; Efficiency-specificity
Return:
Uptake_Vmax0: dataframe; Rows are monomers; cols are uptake enzymes
"""
#Uptake_Vmax0_array = np.random.uniform(self.Uptake_Vmax0_min,self.Uptake_Vmax0_max,self.n_uptake*self.n_monomers)
Uptake_Vmax0_array = LHS(self.n_uptake * self.n_monomers,
self.Uptake_Vmax0_min, self.Uptake_Vmax0_max,
'uniform')
Uptake_Vmax0_array = Uptake_Vmax0_array.reshape(
self.n_monomers, self.n_uptake)
index = ['Mon' + str(i) for i in range(1, self.n_monomers + 1)]
columns = ['Upt' + str(i) for i in range(1, self.n_uptake + 1)]
Uptake_Vmax0 = pd.DataFrame(data=Uptake_Vmax0_array,
index=index,
columns=columns,
dtype='float32')
# implement the tradeoff with specificity
total_monomers = Uptake_ReqEnz.sum(axis=0)
if self.Specif_factor == 0:
total_monomers[total_monomers > 1] = 1
else:
total_monomers[total_monomers > 1] = total_monomers[
total_monomers > 1] * self.Specif_factor
Uptake_Vmax0 = Uptake_Vmax0.divide(total_monomers, axis=1)
Uptake_Vmax0.loc[:, total_monomers == 0] = 0
Uptake_Vmax0 = Uptake_Vmax0.astype('float32')
return Uptake_Vmax0