reconstruction.ecoli.dataclasses.process.metabolism

SimulationData for metabolism process

TODO:

  • improved estimate of ILE/LEU abundance or some external data point

  • implement L1-norm minimization for AA concentrations

  • find concentration for PI[c]

  • add (d)NTP byproduct concentrations

class reconstruction.ecoli.dataclasses.process.metabolism.ConcentrationUpdates(concDict, relative_changes, equilibriumReactions, exchange_data_dict, all_metabolite_ids, linked_metabolites)[source]

Bases: object

_add_molecule_amounts(equilibriumReactions, concDict)[source]
_exchange_flux_present(exchange_data)[source]

Caches the presence of exchanges in each media condition based on exchange_data to set concentrations in concentrations_based_on_nutrients().

Parameters:

exchange_data (dict[str, Any]) –

dictionary of exchange data for all media conditions with keys:

{importUnconstrainedExchangeMolecules (dict[str, set[str]]):
        exchange molecules (with location tag) for each media key
        that do not have an upper bound on their flux,
importConstrainedExchangeMolecules (dict[str,
        dict[str, float with mol/mass/time units]]): constrained
        molecules (with location tag) for each media key with upper
        bound flux constraints}

Returns:

Sets of molecules IDs (with location tags) that can be imported for each media ID

Return type:

dict[str, set[str]]

concentrations_based_on_nutrients(media_id=None, imports=None, conversion_units=None)[source]
reconstruction.ecoli.dataclasses.process.metabolism.DRY_MASS_UNITS = 1 [fg]

Units for dry mass

exception reconstruction.ecoli.dataclasses.process.metabolism.InvalidReactionDirectionError[source]

Bases: Exception

reconstruction.ecoli.dataclasses.process.metabolism.K_CAT_UNITS = 1.0 [1/s]

Units for kcatvalues

reconstruction.ecoli.dataclasses.process.metabolism.METABOLITE_CONCENTRATION_UNITS = 1.0 [mol/L]

Units for metabolite concentrations

class reconstruction.ecoli.dataclasses.process.metabolism.Metabolism(raw_data, sim_data)[source]

Bases: object

Parameters:
solver

solver ID, should match a value in modular_fba.py, set by _set_solver_values()

Type:

str

kinetic_objective_weight

weighting for the kinetic objective, 1-weighting for the homeostatic objective, set by _set_solver_values()

Type:

float

kinetic_objective_weight_in_range

weighting for deviations from the kinetic target within min and max ranges, set by _set_solver_values()

Type:

float

secretion_penalty_coeff

penalty on secretion fluxes, set by _set_solver_values()

Type:

float

metabolite_charge

mapping of metabolite IDs to charge, set by _add_metabolite_charge()

Type:

dict[str, int]

concentration_updates
conc_dict
Type:

dict

nutrients_to_internal_conc
Type:

dict[str, dict[str, Unum]]

kinetic_constraint_reactions
kinetic_constraint_enzymes
kinetic_constraint_substrates
_kcats
_saturations
_enzymes
constraint_is_kcat_only
_compiled_enzymes
_compiled_saturation
reaction_stoich
maintenance_reaction
reaction_catalysts
catalyst_ids
reactions_with_catalyst
catalysis_matrix_I
catalysis_matrix_J
catalysis_matrix_V
use_all_constraints
constraints_to_disable
base_reaction_ids
reaction_id_to_base_reaction_id
amino_acid_export_kms
transport_reactions

transport reaction IDs in the metabolic network (includes reverse reactions and reactions with kinetic constraints), set by _build_transport_reactions()

Type:

list[str]

aa_synthesis_pathways

data for allosteric inhibition of amino acid pathways indexed by amino acid ID with location tag and nested dictionary with the following keys:

{'enzymes' (str): limiting/regulated enzyme ID in synthesis
        pathway with location tag,
'kcat_data' (units.Unum): kcat associated with enzyme
        reaction with units of 1/time,
'ki' (Tuple[units.Unum, units.Unum]]): lower and upper
        limits of KI associated with enzyme reaction with units
        of mol/volume}

Set by _build_amino_acid_pathways()

Type:

dict[str, dict]

KI_aa_synthesis

KI for each AA for synthesis portion of supply (in units of METABOLITE_CONCENTRATION_UNITS), set by set_phenomological_supply_constants()

Type:

numpy.ndarray[float]

KM_aa_export

KM for each AA for export portion of supply (in units of METABOLITE_CONCENTRATION_UNITS), set by set_phenomological_supply_constants()

Type:

numpy.ndarray[float]

fraction_supply_rate

fraction of AA supply that comes from a base synthesis rate, set by set_phenomological_supply_constants()

Type:

float

fraction_import_rate

fraction of AA supply that comes from AA import if nutrients are present, set by set_phenomological_supply_constants()

Type:

numpy.ndarray[float]

ppgpp_synthesis_reaction

reaction ID for ppGpp synthesis (catalyzed by RelA and SpoT), set by _build_ppgpp_reactions()

Type:

str

ppgpp_degradation_reaction

reaction ID for ppGpp degradation (catalyzed by SpoT), set by _build_ppgpp_reactions()

Type:

str

ppgpp_reaction_names

names of reaction involved in ppGpp, set by _build_ppgpp_reactions()

Type:

list[str]

ppgpp_reaction_metabolites

names of metabolites in ppGpp reactions, set by _build_ppgpp_reactions()

Type:

list[str]

ppgpp_reaction_stoich

2D array with metabolites on rows and reactions on columns containing the stoichiometric coefficient, set by _build_ppgpp_reactions()

Type:

numpy.ndarray[int]

aa_to_exporters

dictonary that maps aa to transporters involved in export reactions. Set by set_mechanistic_export_constants().

Type:

dict[str, list]

aa_to_exporters_matrix

correlation matrix. Columns correspond to exporting enzymes and rows to amino acids. Set by set_mechanistic_export_constants().

Type:

numpy.ndarray[int]

aa_exporter_names

names of all exporters. Set by set_mechanistic_export_constants().

Type:

numpy.ndarray[str]

aa_export_kms

kms corresponding to generic transport/enzyme reactions for each AA in concentration units. Set by set_mechanistic_export_constants().

Type:

numpy.ndarray[float]

export_kcats_per_aa

kcats corresponding to generic export reactions for each AA. Units in counts/second. Set by set_mechanistic_export_constants() and set_mechanistic_export_constants().

Type:

numpy.ndarray[float]

aa_to_importers

dictonary that maps aa to transporters involved in import reactions. Set by set_mechanistic_uptake_constants().

Type:

dict[str, list]

aa_to_importers_matrix

correlation matrix. Columns correspond to importing enzymes and rows to amino acids. Set by set_mechanistic_export_constants().

Type:

numpy.ndarray[int]

aa_importer_names

names of all importers. Set by set_mechanistic_export_constants().

Type:

numpy.ndarray[str]

import_kcats_per_aa

kcats corresponding to generic import reactions for each AA. Units in counts/second. Set by set_mechanistic_export_constants().

Type:

numpy.ndarray[float]

aa_enzymes

enzyme ID with location tag for each enzyme that can catalyze an amino acid pathway with enzyme_to_amino_acid mapping these to each amino acid. Set by set_mechanistic_supply_constants().

Type:

numpy.ndarray[str]

aa_kcats_fwd

forward kcat value for each synthesis pathway in units of K_CAT_UNITS, ordered by amino acid molecule group. Set by set_mechanistic_supply_constants().

Type:

numpy.ndarray[float]

aa_kcats_rev

reverse kcat value for each synthesis pathway in units of K_CAT_UNITS, ordered by amino acid molecule group. Set by set_mechanistic_supply_constants().

Type:

numpy.ndarray[float]

aa_kis

KI value for each synthesis pathway in units of METABOLITE_CONCENTRATION_UNITS, ordered by amino acid molecule group. Will be inf if there is no inhibitory control. Set by set_mechanistic_supply_constants().

Type:

numpy.ndarray[float]

aa_upstream_kms

KM value associated with the amino acid that feeds into each synthesis pathway in units of METABOLITE_CONCENTRATION_UNITS, ordered by amino acid molecule group. Will be 0 if there is no upstream amino acid considered. Set by set_mechanistic_supply_constants().

Type:

list[list[float]]

aa_reverse_kms

KM value associated with the amino acid in each synthesis pathway in units of METABOLITE_CONCENTRATION_UNITS, ordered by amino acid molecule group. Will be inf if the synthesis pathway is not reversible. Set by set_mechanistic_supply_constants().

Type:

numpy.ndarray[float]

aa_upstream_mapping

index of the upstream amino acid that feeds into each synthesis pathway, ordered by amino acid molecule group. Set by set_mechanistic_supply_constants().

Type:

numpy.ndarray[int]

enzyme_to_amino_acid

relationship mapping from aa_enzymes to amino acids (n enzymes, m amino acids). Will contain a 1 if the enzyme associated with the row can catalyze the pathway for the amino acid associated with the column. Set by set_mechanistic_supply_constants().

Type:

numpy.ndarray[float]

aa_forward_stoich

relationship mapping from upstream amino acids to downstream amino acids (n upstream, m downstream). Will contain a -1 if the amino acid associated with the row is required for synthesis of the amino acid associated with the column. Set by set_mechanistic_supply_constants().

Type:

numpy.ndarray[float]

aa_reverse_stoich

relationship mapping from upstream amino acids to downstream amino acids (n downstream, m upstream). Will contain a -1 if the amino acid associated with the row is produced through a reverse reaction from the amino acid associated with the column. Set by set_mechanistic_supply_constants().

Type:

numpy.ndarray[float]

aa_import_kis

inhibition constants for amino acid import based on the internal amino acid concentration

Type:

numpy.ndarray[float]

specific_import_rates

import rates expected in rich media conditions for each amino acid normalized by dry cell mass in units of K_CAT_UNITS / DRY_MASS_UNITS, ordered by amino acid molecule group. Set by set_mechanistic_supply_constants().

Type:

numpy.ndarray[float]

max_specific_import_rates

max import rates for each amino acid without including internal concentration inhibition normalized by dry cell mass in units of K_CAT_UNITS / DRY_MASS_UNITS, ordered by amino acid molecule group. Set by set_mechanistic_supply_constants().

Type:

numpy.ndarray[float]

_add_metabolite_charge(raw_data)[source]

Compiles all metabolite charges.

Parameters:

raw_data (KnowledgeBaseEcoli) – Raw data object

Attributes set:
_build_amino_acid_pathways(raw_data, sim_data)[source]

Creates mapping between enzymes and amino acid pathways with allosteric inhibition feedback from the amino acid.

Parameters:
Attributes set:
_build_biomass(raw_data, sim_data)[source]

Calculates metabolite concentration targets.

Parameters:
Attributes set:
_build_linked_metabolites(raw_data, conc_dict)[source]

Calculates ratio between linked metabolites to keep it constant throughout a simulation.

Parameters:
Returns:

Mapping from a linked metabolite to its lead metabolite and concentration ratio to be maintained:

{'lead' (str): metabolite to link the concentration to,
'ratio' (float): ratio to multiply the lead concentration by}

Return type:

dict[str, dict[str, Any]]

_build_metabolism(raw_data, sim_data)[source]

Build the matrices/vectors for metabolism (FBA) Reads in and stores reaction and kinetic constraint information

Parameters:
Attributes set:
_build_ppgpp_reactions(raw_data, sim_data)[source]

Creates structures for ppGpp reactions for use in polypeptide_elongation.

Parameters:
Attributes set:
_build_transport_reactions(raw_data, sim_data)[source]

Creates list of transport reactions that are included in the reaction network.

Parameters:
Attributes set:
static _construct_default_saturation_equation(mets, kms, kis, known_mets)[source]
Parameters:

  • mets (list[str]) – metabolite IDs with location tag for KM and KI parameters ordered to match order of kms then kis

  • kms (list[float]) – KM parameters associated with mets

  • kis (list[float]) – KI parameters associated with mets

  • known_mets (Iterable[str]) – metabolite IDs with location tag with known concentrations

Returns:

Saturation equation with metabolites to replace delimited by double quote (e.g. “metabolite”)

Return type:

str

static _extract_custom_constraint(constraint, reactant_tags, product_tags, known_mets)[source]
Parameters:

  • constraint (dict[str, Any]) –

    values defining a kinetic constraint:

    {'customRateEquation' (str): mathematical representation of
        rate (must contain 'kcat*E'),
'customParameterVariables' (dict[str, str]): mapping of
        variable names in the rate equation to metabolite IDs
        without location tags (must contain 'E'),
'customParameterConstants' (list[str]): constant strings
        in the rate equation that correspond to values (must
        contain 'kcat'),
'customParameterConstantValues' (list[float]): values for
        each of the constant strings,
'Temp' (float or ''): temperature of measurement}

  • reactant_tags (dict[str, str]) – mapping of molecule IDs without a location tag to molecule IDs with a location tag for all reactants

  • product_tags (dict[str, str]) – mapping of molecule IDs without a location tag to molecule IDs with a location tag for all products

  • known_mets (set[str]) – molecule IDs with a location tag for molecules with known concentrations

Returns:

2-element tuple containing

  • kcats: temperature adjusted kcat value, in units of 1/s

  • saturation: saturation equation with metabolites to replace delimited by double quote (eg. “metabolite”)

Return type:

tuple[ndarray | None, list[str]]

_is_transport_rxn(stoich)[source]

Determines if the metabolic reaction with a given stoichiometry is a transport reactions that transports metabolites between different compartments. A metabolic reaction is considered to be a transport reaction if the substrate set and the product share the same metabolite tagged into different compartments.

Parameters:

stoich (dict[str, int]) –

Stoichiometry of the metabolic reaction:

{
metabolite ID (str): stoichiometric coefficient (int)
}

Returns:

True if given stoichiometry is a transport reaction

Return type:

bool

static _lambdify_constraints(constraints)[source]

Creates str representations of kinetic terms to be used to create kinetic constraints that are returned with getKineticConstraints().

Parameters:

constraints (dict[str, Any]) –

valid kinetic constraints for each reaction:

{reaction ID: {
        'enzyme': enzyme catalyst (str),
        'kcat': kcat values (list[float]),
        'saturation': saturation equations (list[str])
}}

Returns:

7-element tuple containing

  • rxns: sorted reaction IDs for reactions with a kinetic constraint

  • enzymes: sorted enzyme IDs for enzymes that catalyze a kinetic reaction

  • substrates: sorted substrate IDs for substrates that are needed for kinetic saturation terms

  • all_kcats: (n rxns, 3) min, mean and max kcat value for each reaction

  • all_saturations: sympy str representation of a list of saturation terms (eg. ‘[s[0] / (1 + s[0]), 2 / (2 + s[1])]’)

  • all_enzymes: sympy str representation of enzymes for each reaction (e.g. ‘[e[0], e[2], e[1]]’)

  • constraint_is_kcat_only: True if reaction only has kcat values and no saturation terms

Return type:

tuple[list[str], list[str], list[str], ndarray, str, str, ndarray]

static _replace_enzyme_reactions(constraints, stoich, rxn_catalysts, reversible_rxns, rxn_id_to_compiled_id)[source]

Modifies reaction IDs in data structures to duplicate reactions with kinetic constraints and multiple enzymes.

Parameters:

  • constraints (dict[tuple[str, str], dict[str, list[Any]]]) –

    valid kinetic constraints for each reaction/enzyme pair:

    {(reaction ID, enzyme with location tag): {
        'kcat': kcat values (list[float]),
        'saturation': saturation equations (list[str])
}}

  • stoich (dict[str, dict[str, int]]) –

    stoichiometry of metabolites for each reaction (if None, data is loaded from raw_data and sim_data):

    {reaction ID: {metabolite ID with location tag: stoichiometry}}

  • rxn_catalysts (dict[str, list[str]]) –

    enzyme catalysts for each reaction with known catalysts, likely a subset of reactions in stoich (if None, data is loaded from raw_data and sim_data):

    {reaction ID: enzyme IDs with location tag}

  • reversible_rxns (list[str]) – reaction IDs for reactions that have a reverse complement, does not have reverse tag

  • rxn_id_to_compiled_id (dict[str, str]) – mapping from reaction IDs to the IDs of the original reactions they were derived from

Returns:

5-element tuple containing

  • new_constraints: valid kinetic constraints for each reaction:

    {reaction ID: {
        'enzyme': enzyme catalyst (str),
        'kcat': kcat values (list[float]),
        'saturation': saturation equations (list[str])
}}

  • stoich: stoichiometry of metabolites for each reaction with updated reactions for enzyme catalyzed kinetic reactions:

    {reaction ID: {metabolite ID with location tag: stoichiometry}}

  • rxn_catalysts: enzyme catalysts for each reaction with known catalysts, likely a subset of reactions in stoich with updated reactions for enzyme catalyzed kinetic reactions:

    {reaction ID: enzyme IDs with location tag}

  • reversible_rxns: reaction IDs for reactions that have a reverse complement with updated reactions for enzyme catalyzed kinetic reactions, does not have reverse tag

  • rxn_id_to_compiled_id: mapping from reaction IDs to the IDs of the original reactions they were derived from, with updated reactions for enzyme catalyzed kinetic reactions

Return type:

tuple[dict[str, Any], dict[str, dict[str, int]], dict[str, list[str]], list[str], dict[str, str]]

_set_solver_values(constants)[source]

Sets values to be used in the FBA solver.

Attributes set:
Parameters:

constants (Constants) –

aa_supply_scaling(aa_conc, aa_present)[source]

Called during polypeptide_elongation process Determine amino acid supply rate scaling based on current amino acid concentrations.

Parameters:

  • aa_conc (Unum) – internal concentration for each amino acid (ndarray[float])

  • aa_present (Unum) – whether each amino acid is in the external environment or not (ndarray[bool])

Returns:

Scaling for the supply of each amino acid with higher supply rate if >1, lower supply rate if <1

Return type:

ndarray[float]

amino_acid_export(aa_transporters_counts, aa_conc, mechanistic_uptake)[source]

Calculate the rate of amino acid export.

Parameters:

  • aa_transporters_counts (ndarray) – counts of each transporter

  • aa_conc (Unum | ndarray) – concentrations of each amino acid with mol/volume units

  • mechanistic_uptake (bool) – if true, the uptake is calculated based on transporters

Returns:

Rate of export for each amino acid (unitless but represents counts of amino acid per second)

Return type:

ndarray[float]

amino_acid_import(aa_in_media, dry_mass, internal_aa_conc, aa_transporters_counts, mechanisitic_uptake)[source]

Calculate the rate of amino acid uptake.

Parameters:

  • aa_in_media (ndarray) – bool for each amino acid being present in current media

  • dry_mass (Unum) – current dry mass of the cell, with mass units

  • internal_aa_conc (Unum | ndarray) – internal concentrations of amino acids

  • aa_transporters_counts (ndarray) – counts of each transporter

  • mechanisitic_uptake (bool) – if true, the uptake is calculated based on transporters

Returns:

Rate of uptake for each amino acid (unitless but represents counts of amino acid per second)

Return type:

ndarray[float]

amino_acid_synthesis(counts_per_aa_fwd, counts_per_aa_rev, aa_conc)[source]

Calculate the net rate of synthesis for amino acid pathways (can be negative with reverse reactions).

Parameters:

  • counts_per_aa_fwd (ndarray) – counts for enzymes in forward reactions for each amino acid

  • counts_per_aa_rev (ndarray) – counts for enzymes in loss reactions for each amino acid

  • aa_conc (Unum | ndarray) – concentrations of each amino acid with mol/volume units

Returns:

3-element tuple containing

  • synthesis: net rate of synthesis for each amino acid pathway. array is unitless but represents counts of amino acid per second

  • forward_fraction: saturated fraction for forward reactions

  • loss_fraction: saturated fraction for loss reactions

Return type:

tuple[ndarray[float], ndarray[float], ndarray[float]]

Note

Currently does not match saturation terms used in calc_kcats since it assumes only a reverse or degradation KM exists for simpler calculations

exchange_constraints(exchangeIDs, coefficient, targetUnits, media_id, unconstrained, constrained, concModificationsBasedOnCondition=None)[source]

Called during Metabolism process Returns the homeostatic objective concentrations based on the current nutrients Returns levels for external molecules available to exchange based on the current nutrients

static extract_kinetic_constraints(raw_data, sim_data, stoich=None, catalysts=None, known_metabolites=None)[source]

Load and parse kinetic constraint information from raw_data

Parameters:

  • raw_data (KnowledgeBaseEcoli) – knowledge base data

  • sim_data (SimulationDataEcoli) – simulation data

  • stoich (dict[str, dict[str, int]] | None) –

    stoichiometry of metabolites for each reaction (if None, data is loaded from raw_data and sim_data):

    {reaction ID: {metabolite ID with location tag: stoichiometry}}

  • catalysts (dict[str, list[str]] | None) –

    enzyme catalysts for each reaction with known catalysts, likely a subset of reactions in stoich (if None, data is loaded from raw_data and sim_data:

    {reaction ID: enzyme IDs with location tag}

  • known_metabolites (Set[str] | None) – metabolites with known concentrations

Returns:

Valid kinetic constraints for each reaction/enzyme pair:

{(reaction ID, enzyme with location tag): {
        'kcat': kcat values (list[float]),
        'saturation': saturation equations (list[str])
}}

Return type:

dict[tuple[str, str], dict[str, list[Any]]]

static extract_reactions(raw_data, sim_data)[source]

Extracts reaction data from raw_data to build metabolism reaction network with stoichiometry, reversibility and enzyme catalysts.

Parameters:
Returns:

5-element tuple containing

  • base_rxn_ids: list of base reaction IDs from which reaction IDs were derived from

  • reaction_stoich: stoichiometry of metabolites for each reaction:

    {reaction ID: {metabolite ID with location tag: stoichiometry}}

  • reversible_reactions: reaction IDs for reactions that have a reverse complement, does not have reverse tag

  • reaction_catalysts: enzyme catalysts for each reaction with known catalysts, likely a subset of reactions in stoich:

    {reaction ID: enzyme IDs with location tag}

  • rxn_id_to_base_rxn_id: mapping from reaction IDs to the IDs of the base reactions they were derived from:

    {reaction ID: base ID}

Return type:

tuple[list[str], dict[str, dict[str, int]], list[str], dict[str, list[str]], dict[str, str]]

get_aa_to_transporters_mapping_data(sim_data, export=False)[source]

Creates a dictionary that maps amino acids with their transporters. Based on this dictionary, it creates a correlation matrix with rows as AA and columns as transporters.

Parameters:

  • sim_data (SimulationDataEcoli) – simulation data

  • export (bool) – if True, the parameters calculated are for mechanistic export instead of uptake

Returns:

3-element tuple containing

  • aa_to_transporters: dictonary that maps aa to transporters involved in transport reactions

  • aa_to_transporters_matrix: correlation matrix. Columns correspond to transporter enzymes and rows to amino acids

  • aa_transporters_names: names of all transporters

Return type:

tuple[dict[str, list], ndarray[int], ndarray[str]]

get_amino_acid_conc_conversion(conc_units)[source]
get_kinetic_constraints(enzymes, substrates)[source]

Allows for dynamic code generation for kinetic constraint calculation for use in Metabolism process. Inputs should be unitless but the order of magnitude should match the kinetics parameters (umol/L/s).

If trying to pickle sim_data object after function has been called, _compiled_enzymes and _compiled_saturation might not be able to be pickled. See __getstate__(), __setstate__() comments on PR 111 to address.

Returns np.array of floats of the kinetic constraint target for each reaction with kinetic parameters

Parameters:

  • enzymes (Unum) – concentrations of enzymes associated with kinetic constraints (mol / volume units)

  • substrates (Unum) – concentrations of substrates associated with kinetic constraints (mol / volume units)

Returns:

Array of dimensions (n reactions, 3) where each row contains the min, mean and max kinetic constraints for each reaction with kinetic constraints (mol / volume / time units)

Return type:

Unum

get_pathway_enzyme_counts_per_aa(enzyme_counts)[source]

Get the counts of enzymes for forward and reverse reactions in the amino acid synthesis network based on all of the enzymes used in the network. Useful to get the counts to pass to amino_acid_synthesis() from counts based on self.aa_enzymes.

Parameters:

enzyme_counts (ndarray) – counts of all enzymes in the amino acid network

Returns:

2-element tuple containing

  • counts_per_aa_fwd: counts of enzymes for the forward reaction for each amino acid

  • counts_per_aa_rev: counts of enzymes for the reverse reaction for each amino acid

Return type:

Tuple[ndarray, ndarray]

static match_reaction(stoich, catalysts, rxn_to_match, enz, mets, direction=None)[source]

Matches a given reaction (rxn_to_match) to reactions that exist in stoich given that enz is known to catalyze the reaction and mets are reactants in the reaction. Can perform a fuzzy reaction match since rxn_to_match just needs to be part of the actual reaction name to match specific instances of a reaction. (eg. rxn_to_match=”ALCOHOL-DEHYDROG-GENERIC-RXN” can match “ALCOHOL-DEHYDROG-GENERIC-RXN-ETOH/NAD//ACETALD/NADH/PROTON.30.”).

Parameters:

  • stoich (dict[str, dict[str, int]]) –

    stoichiometry of metabolites for each reaction:

    {reaction ID: {metabolite ID with location tag: stoichiometry}}

  • catalysts (dict[str, list[str]]) –

    enzyme catalysts for each reaction with known catalysts, likely a subset of reactions in stoich:

    {reaction ID: enzyme IDs with location tag}

  • rxn_to_match (str) – reaction ID from kinetics to match to existing reactions

  • enz (str) – enzyme ID with location tag

  • mets (list[str]) – metabolite IDs with no location tag from kinetics

  • direction (str | None) – reaction directionality, 'forward' or 'reverse' or None

Returns:

Matched reaction IDs in stoich

Return type:

list[str]

set_mechanistic_export_constants(sim_data, cell_specs, basal_container)[source]

Calls get_aa_to_transporters_mapping_data() for AA export, which calculates the total amount of export transporter counts per AA. Kcats are calculated using the same exchange rates as for uptake and transporter counts. Missing KMs are calculated based on present KMs. This is done by calculating the average factor for KMs compared to estimated concentration (av_factor = sum(KM / concentration) / n_aa_with_kms). ** KM = av_factor * concentration

Parameters:

  • sim_data (SimulationDataEcoli) – simulation data

  • cell_specs (dict[str, dict]) – mapping from condition to calculated cell properties

  • basal_container (ndarray) – average initial bulk molecule counts in the basal condition (structured Numpy array, see Bulk Molecules)

Attributes set:
set_mechanistic_supply_constants(sim_data, cell_specs, basal_container, with_aa_container)[source]

Sets constants to determine amino acid supply during translation. Used with amino_acid_synthesis() and amino_acid_import() during simulations but supply can alternatively be determined phenomologically. This approach is more detailed and should better respond to environmental changes and perturbations but has more variability related to gene expression and regulation.

Parameters:

  • sim_data (SimulationDataEcoli) – simulation data

  • cell_specs (dict[str, dict]) – mapping from condition to calculated cell properties

  • basal_container (ndarray) – average initial bulk molecule counts in the basal condition (structured Numpy array, see Bulk Molecules)

  • with_aa_container (ndarray) – average initial bulk molecule counts in the with_aa condition

Sets class attributes:

Assumptions:

  • Only one reaction is limiting in an amino acid pathway (typically the first and one with KI) and the kcat for forward or reverse directions will apply to all enzymes that can catalyze that step

  • kcat for reverse and degradation reactions is the same (each amino acid only has reverse or degradation at this point but that could change with modifications to the amino_acid_pathways flat file)

set_mechanistic_uptake_constants(sim_data, cell_specs, with_aa_container)[source]

Based on the matrix calculated in get_aa_to_transporters_mapping_data(), we calculate the total amount of transporter counts per AA.

Parameters:

  • sim_data (SimulationDataEcoli) – simulation data

  • cell_specs (dict[str, dict]) – mapping from condition to calculated cell properties

  • with_aa_container (ndarray) – average initial bulk molecule counts in the with_aa condition (structured Numpy array, see Bulk Molecules)

Attributes set:
set_phenomological_supply_constants(sim_data)[source]

Sets constants to determine amino acid supply during translation. Used with aa_supply_scaling() during simulations but supply can alternatively be determined mechanistically. This approach may require manually adjusting constants (fraction_supply_inhibited and fraction_supply_exported) but has less variability related to gene expression and regulation.

Parameters:

sim_data (SimulationDataEcoli) – simulation data

Attributes set:
Assumptions:

  • Each internal amino acid concentration in ‘minimal_plus_amino_acids’ media is not lower than in ‘minimal’ media

TODO (Travis):

Better handling of concentration assumption

static temperature_adjusted_kcat(kcat, temp='')[source]
Parameters:

  • kcat (Unum) – enzyme turnover number(s) (1 / time)

  • temp (float | str) – temperature of measurement, defaults to 25 if ‘’

Returns:

Temperature adjusted kcat values, in units of 1/s

Return type:

ndarray[float]

reconstruction.ecoli.dataclasses.process.metabolism.amino_acid_export_jit(aa_transporters_counts, aa_conc, mechanistic_uptake, aa_to_exporters_matrix, export_kcats_per_aa, aa_export_kms)
reconstruction.ecoli.dataclasses.process.metabolism.amino_acid_import_jit(aa_in_media, dry_mass, internal_aa_conc, aa_transporters_counts, mechanisitic_uptake, aa_import_kis, aa_to_importers_matrix, import_kcats_per_aa, max_specific_import_rates)
reconstruction.ecoli.dataclasses.process.metabolism.amino_acid_synthesis_jit(counts_per_aa_fwd, counts_per_aa_rev, aa_conc, aa_upstream_kms, aa_kis, aa_reverse_kms, aa_degradation_kms, aa_forward_stoich, aa_kcats_fwd, aa_reverse_stoich, aa_kcats_rev)
reconstruction.ecoli.dataclasses.process.metabolism.np_apply_along_axis(func1d, axis, arr)
reconstruction.ecoli.dataclasses.process.metabolism.np_prod(array, axis)