Configuration Data

This chapter provides information regarding the configuration tables utilized by the f2xba modeling framework. It is recommended that the user proceeds through the tutorials, in which these configuration files are established and employed. Tables are stored in ‘.xlsx’ spreadsheets files with sheet names corresponding to the table names.

Table columns with default or “None” values are not required to be included in the tables. Additional columns are permissible, provided they do not conflict with the specified column headings. A beneficial column could be a “notes” column, which would allow for the annotation of data for future reference.

XBA configuration

The XBA configuration file, used in configuration of the XbaModel instance, contains several tables, which are described in the following.

Table general

The table labeled general contains the primary parameters for extended model configuration. The default turnover numbers can be configured for metabolic and transport reactions. Furthermore, references to online databases, such as the UniProt protein database (organism_id), the NCBI genome database (chromosome2accids), and the Biocyc organism database (biocyc_org_prefix), can be defined as per requirement. Furthermore, the provision of file names to configure reaction specific turnover numbers (kcats_fname) and enzyme compositions (enzyme_comp_fname) is permitted, with the objective of replacing default values. Setting the cofactor_flag to “True” enables the incorporation of cofactors extracted from UniProt entries into enzyme compositions. Finally, a bulk mapping configuration table (bulk_mappings_fname) can be used for large-scale model reconfigurations, e.g., for remapping of identifiers used in the model.

general: general configuration data

Column	Contents	Example
parameter	parameter name	thermo_data_fname
value	value	data/thermo_data.thermodb

Following parameters can be configured with this table:

Parameter	Description	Example
default_metabolic_kcat	default for metabolic reaction turnover numbers in s-1 (default: 12.5)	12.5
default_transporter_kcat	default for transporter reaction turnover number in s-1 (default: 50.0)	100.0
organism_dir	directory with organism specific data files	data
organism_id	taxonomic identifier	83333
chromosome2accids	NCBI genome accession identifiers	chromosome=U00096.3
biocyc_org_prefix	BioCyc organism identifier	ecoli
kcats_fname	reaction specific turnover numbers	data/iML1515_predicted_fit_GECKO_kcats.xlsx
enzyme_comp_fname	enzyme composition	data/iML1515_enzyme_composition_updated.xlsx
bulk_mappings_fname	bulk mapping configuration	data_configs/iJN678_bulk_mappings.xlsx
cofactor_flag	cofactor use in RBA enzymes (default: False)	True

Notes: RBA models require “chromosome2accids” to reference all chromosomes and plasmids that contain genes used in the model, e.g. “Chr_I=BK006935.2, Chr_II=BK006936.2, Chr_III=BK006937.2, …”.

Table modify_attributes

The table designated modify_attributes allows the modification of model component attributes, e.g. modifying flux bounds on reaction components.

modify_attributes: modify model component attributes

Column	Contents	Example
id	component identifier	R_ALAt2pp
component	component type or None	reaction
attribute	attribute name	fbc_lower_bound
value	new value	cobra_0_bound

The “attribute” identifier and the type of “value” depends on the “component” type. Attributes of following components can be modified: “modelAttrs”, “compartment”, “species”, “reaction”, “gp”, “protein”, “enzyme”, “ncbi” and “uniprot”. Some examples are provided in below table:

examples for table entries

id	component	attribute	value
R_ALAt2pp	reaction	fbc_lower_bound”	cobra_0_bound
R_ASPCT	reaction	gene_product_assoc	(G_b4244 and G_b4245)
R_BIOMASS_Ec_iJO1366_core_53p95M	reaction	reactant	M_kdo2lipid4_e=0
R_DNAP	reaction	kind	biomass
enz_b4086_b4087_b4088	enzyme	composition	gene=b4087, stoic=1.0; gene=b4086, stoic=2.0; gene=b4088, stoic=50
P0A6D5	uniprot	locus	b1692
None	modelAttrs	miriamAnnotation	bqbiol:hasTaxon, taxonomy/2144189; bqmodel:isDescribedBy, pubmed/30657448

Table remove_gps

The table designated remove_gps contains a single column listing the gene products to be removed from the model, e.g. dummy proteins.

remove_gps: removal of gene products

Column	Contents	Example
id	gene product identifier	G_s0001

Table add_gps

The table designated add_gps facilitates adding gene products to the model, e.g. tRNAs to a RBA model.

add_gps: extend the model by adding gene products

Column	Contents	Example
id	gene product identifier	G_b0026
label	gene locus	b0026
compartment	compartment identifier (default: None)	c

Note: SBML parameters “miriamAnnotation”, “xmlAnnotation”, “metaid”, “sboterm” and “notes” can be configured as well.

Table add_species

The table designated add_species facilitated the addition of species to the model, e.g. cofactors or tRNA metabolites. RBA models distinguish between tRNA metabolites used in reactions, e.g. charging and elongation reactions, and tRNA macromolecules, which need to be synthesized and get diluted by growth and have configured concentration targets.

add_species: extend model by adding species

Column	Contents	Example
id	species identifier	M_pqq_p
name	descriptive name	pyrroloquinoline quinone(3−)
compartment	compartment identifier	p
miriamAnnotation	MIRIAM annotation string (default: None)	bqbiol:is, chebi/CHEBI:58442
fbcCharge	electrical charge (default: None)	-3
fbcChemicalFormula	chemical formula (default: None)	C14H3N2O8

Notes: Additional SBML parameters can be configured with this table, which include “xmlAnnotation”, “metaid”, “notes”, “sboterm”, “substanceUnits”, and bool values for “constant”, “boundaryCondition” and “hasOnlySubstanceUnits”.

Table add_reactions

The table designated add_reactions facilitates the addition of reactions to the model, e.g. tRNA charging reactions used in RBA models. The detailed configuration options can be explored when exporting a FBA or XBA model in tabular format, using the converter sbmlxdf or the method XbaModel.export(‘mymodel.xlsx’)

add_reactions: extend model by adding reactions

Column	Contents	Example
id	reaction identifier	R_GLYTRS
name	descriptive name	Glycyl-tRNA synthetase
fbcGeneProdAssoc	gene product association (default: None)	assoc=(G_b3559 and G_b3560)
reactionString	reaction string (default: None)	M_gly_c + M_trnagly_c + M_atp_c => M_glytrna_c + M_amp_c + M_ppi_c
fbcLb	lower flux bound (default: None)	0
fbcUb	upper flux bound (default: None)	1000

Note: : Additional SBML parameters can be configured with this table. As an alternative to the provision of a “reactionString”, the parameters “reactants”, “products” and “reversible” can be configured. In lieu of configuring numerical values for “fbcLb” and “fbcUb”, model parameter identifiers can be utilized in “fbcLowerFluxBound” and “fbcUpperFluxBound.” Additionally, “miriamAnnotation”, “xmlAnnotation”, “metaid”, “sboterm” and “notes” can be configured.

Table add_parameters

The table designated add_parameters facilitates the addition of SBML parameters to the model, e.g. specific variable bounds.

add_parameters: add SBML parameters

Column	Contents	Example
id	parameter identifier	R_ATPM_upper_bound
name	descriptive name	ATPM upper - bound
value	numerical value	13.5
units	reference to a SBML units definition (default: ‘dimensionless’)	mmol_per_gDW_per_hr
constant	SBML constant flag (default: True)	None

Table chebi2sid

Enzymes utilized in RBA models may encompass cofactors (refer to the “cofactor_flag” parameter in the “general” table). These cofactors are retrieved from UniProt records as ChEBI identifiers and subsequently mapped to the identifiers of the model species. Cofactors that cannot be mapped will not be included in the model. An automatic mapping is facilitated based on the MIRIAM annotation configured on model species. In instances where this mapping proves unsuccessful, ChEBI identifiers can be mapped using the table chebi2sid.”

The table designated chebi2sid maps cofactor ChEBI identifiers to model species.

chebi2sid: cofactor mapping to model species

Column	Contents	Example
chebi	number part of ChEBI identifier	61717
sid	species identifier	M_pheme_c

ECM configuration

The ECM configuration file, used in configuration of the EcModel instance, contains a single table, designated general.

general: general configuration data

Column	Contents	Example
parameter	parameter name	ecm_type
value	value	GECKO

Following parameters can be configured with this table:

Parameter	Description	Example
ecm_type	model type to create: ‘GECKO’, ‘ccFBA’, ‘MOMENT’ or ‘MOMENTmr’ (default: GECKO)	GECKO
arm_flag	flag to create ‘arm’ reactions (default: False)	None
avg_enz_sat	average enzyme saturation value (default: 0.5)	0.53
p_total	mass fraction of total protein in cellular dry mass (default: 0.5)	0.57
pm2totpm_val_or_paxdb	mass fraction of modeled protein to total protein, a numerical value or a PaxDB compliant file	data/511145-WHOLE_ORGANISM-integrated.txt

‘arm’ reactions can be created for reactions catalyzed by multiple enzymes to constrain, through adequate flux bounds, the summary flux post-splitting.

RBA configuration

The RBA configuration file, used in configuration of the RbaModel instance, contains several tables, with configuration data based on RbaPy.

Table general

The table general is mandatory and contains a single parameter.

general: general configuration data

Column	Contents	Example
parameter	parameter name	avg_enz_sat
value	value	0.41

Following parameters can be configured with this table:

Parameter	Description	Example
avg_enz_sat	average enzyme saturation value	0.41

Table trna2locus

The table designated trna2locus is mandatory and contains configuration data for tRNA macromolecules.

trna2locus: configuration data for tRNA macromolecules

Column	Contents	Example
rna_id	arbitrary tRNA identifier	trnaala
label	gene locus	b0203
compartment	compartment identifier	c
biomass_aa	corresponding amino acid metabolite identifier in biomass reaction (default: None)	M_ala__L_c

Note: A single gene locus should be selected for a given tRNA type. The mass distribution of tRNA macromolecules can be determined automatically when the corresponding amino acid metabolite identifier is assigned to “biomass_aa”.

Table functions

The table designated functions contains constant value and function definitions that are used in the RBA model. Either a constant value or function definition must be assigned. Once a function has been defined, its name can be used in aggregates.

functions: definitions of constant values and RBA functions

Column	Contents	Example
function_name	identifier	frac_protein_c
constant	numerical values (default: None)	None
function	RBA function definition (default: None)	LINEAR_CONSTANT=0.7279, LINEAR_COEF=0.04472, X_MIN=0.2, X_MAX=1.9

Function definitions are based on RbaPy function definitions and support following types:

functions definitions

Type	Parameters	Example
constant	‘CONSTANT’	CONSTANT=4.981
linear	‘LINEAR_CONSTANT’, ‘LINEAR_COEF’, ‘X_MIN’, ‘X_MAX’, ‘Y_MIN’, ‘Y_MAX’	LINEAR_CONSTANT=0.7279, LINEAR_COEF=0.04472, X_MIN=0.2, X_MAX=1.9
michaelisMenten	‘kmax’, ‘Km’, ‘Y_MIN’	kmax=86400.0, Km=0.5, Y_MIN=32400.0
exponential	‘RATE’	variable=growth_rate, RATE=-0.083333
indicator	‘X_MIN’, ‘X_MAX’	X_MIN=0.4, X_MAX=0.6

Notes: the optional parameter variable can specify a model variable (default: ‘growth_rate’). ‘X_MIN’ and ‘Y_MIN’, if not provided, are set to ‘-inf’, ‘X_MAX’ and ‘Y_MAX’ to ‘inf’.

Table compartments

The table designated compartments is mandatory and contains RBA compartment specific data, including mapping of reaction cids, compartment roles, synthesis targets for dummy proteins (not explicitly modeled proteins) and compartment density constraints.

compartments: RBA compartment specific configuration

Column	Contents	Example
id	RBA compartment identifier	om
name	descriptive name	outer_membrane
reaction_cids	reaction-cids mapped	e-p
keyword	assigned role: ‘cytoplasm’, ‘medium’, ‘uptake’ or None	uptake
translation_target_constant	dummy protein target, numerical value (default: None)	None
translation_target_function	dummy protein target, RBA function (default: None)	None
translation_target_aggregate	dummy protein target, RBA aggregate (default: None)	aa_conc, inv_avg_protein_len, frac_protein_om, frac_dummy_protein_om
density_contraint_value_type	density constraint type: ‘value’, ‘upperBound’, ‘lowerBound’ or None	upperBound
density_constraint_constant	density, numerical value (default: None)	None
density_constraint_function	density, RBA function (default: None)	None
density_constraint_aggregate	density, RBA aggregate (default: None)	aa_conc, frac_protein_om

Notes: Assign value to either “xxx_constant“, “xxx_functionv or “xxx_aggregate“. “xxx_function“ must comply with the function definitions, see Table functions. “xxx_aggregate“ must reference function names defined in table ‘functions’.

Table targets

The table targets is mandatory and contains the target concentrations in mmol/gDW for metabolites and macromolecules to implement dilution by growth. It contains reaction flux targets in mmol/gDWh for metabolic reactions, e.g. non-growth associated maintenance, and production/degradation flux targets from macromolecules. The targets can be grouped by ‘target_group’.

targets: RBA model target concentrations and fluxes

Column	Contents	Example
target_group	arbitrary group name	mrna_degradation
target_type	target type: ‘concentrations’, ‘reactionFluxes’, ‘productionFluxes’ or ‘degradationFluxes’	degradationFluxes
target	metabolite id, macromolecule id, reaction id or a filter	mrna
target_value_type	value type: ‘value’, ‘lowerBound’, ‘upperBound’	value
target_constant	numerical value (default: None)	None
target_function	RBA function (default: None)	LINEAR_CONSTANT=0.2732, LINEAR_COEF=-0.0376, X_MIN=0.2
target_aggregate	RBA aggregate (default: None)	None

Notes: Assign value to either “xxx_constant“, “xxx_function“ or “xxx_aggregate“. “xxx_function“ must comply with the function definitions, see Table functions. “xxx_aggregate“ must reference function names defined in table ‘functions’. “target” accepts a filter consisting of a reaction identifier and the keyword ‘metabolites’, ‘amino_acids’ or ‘dna’, to automatically generate individual targets. E.g. ‘R_BIOMASS_Ec_iML1515_core_75p37M, metabolites’ creates individual metabolite targets with “target_constant” scaled by the stoichiometric coefficients in the biomass reaction. With keyword ‘amino_acids’ the molar distribution of amino acids is used as scaling factor.

Table processes

The table processes is mandatory and contains the configuration of the process machines that process macromolecules. The composition of the process machine referenced by process must be configured in the table ‘machineries’. The macromolecules referenced in set and the processing maps used in processing_map must be defined in the table ‘processing_map’.

processes: definition of process machines

Column	Contents	Example
process	process name	pm_translation
name	descriptive name	protein synthesis
type	process type: ‘production’ or ‘degradation’	production
capacity_constant	process capacity, numerical value (default: None)	None
capacity_function	RBA function (default: None)	None
capacity_aggregate	RBA aggregate (default: None)	ribosome_efficiency_MM, fraction_active_ribosomes
processing_map	processing map	translation
set	macromolecule set: ‘dna’, ‘rna’, ‘protein’	protein
input_filter	filter for specific macromolecules (default: None)	None

Notes: The “input_filter” can be utilized to select specific macromolecules as inputs for the process machine. The set ‘rna’, accepts a comma-separated list of regular expression patterns to select RNA macromolecules by identifier matching, e.g. ‘trna, rRNA’. ‘proteins’ accept the keyword ‘signal_peptide’ to select proteins with a signal peptide. As an alternative option, a list of RBA compartment identifiers separated by commas can be utilized, or a list of gene loci. Assign value to either “xxx_constant“, “xxx_functionv or “xxx_aggregate“. “xxx_function“ must comply with the function definitions, see Table functions. “xxx_aggregate“ must reference function names defined in table ‘functions’.

Table machineries

The mandatory table machineries contains the composition of the process machines.

machineries: process machinery composition

Column	Contents	Example
process	process name	pm_translation
id	component identifier	b0023
set	macromolecule set: ‘rna’, ‘protein’ or None for metabolites	protein
label	gene locus or species identifier	b0023
stoic	stoichiometry, negative value for consumption	-1.0
compartment	RBA compartment	c
gpid	gene product identifier or None for metabolites	“G_b0023”

Table processing_maps

The table processing_maps is mandatory and contains two distinct configurations. The first configuration pertains to the composition of macromolecule sets, while the second configuration details the processing of these components.

processingMap: macromolecule components and their processing requirements

Column	Contents	Example
processingMap	identifier	translation
set	macromolecule set: ‘dna’, ‘rna’, ‘protein’ or None	protein
component	one-letter identifier in sequence data or keyword	A
name	component name or None	Alanine
weight	weight with respect to average amino acid weight or None	1
machinery_cost	processing cost for this component or None	1
reaction_string	detailed processing reaction	M_alatrna_c + 2.0 M_gtp_c + 2.0 M_h2o_c => M_trnaala_c + 2.0 M_gdp_c + 2.0 M_pi_c + 3.0 M_h_c

Notes: In the context of a macromolecule “set“, a specific “component”, denoting the one-letter code utilized in the respective sequence data, may be represented multiple times in the table. Values for the parameters “set“, “name“ and “weight“ have to be assigned only once. The parameter “component” also accepts keywords. The utilization of the keyword ‘constantProcessing’ facilitates the definition of an initial setup reaction for a process machine by assigning a value to the parameter “reaction_string.” The keyword ‘cofactor’ enables the allocation of values to “weight,” “machinery_cost,” and “reaction_string,” thereby facilitating the specification of cofactor treatment. Finally, the keyword ‘amino_acids’ facilitates the allocation of values to the “machinery_cost” and “reaction_string” categories, a process that is applicable to all amino acid components present in proteins.

TFA configuration

The TFA configuration file, used in configuration of the TfaModel instance, contains several tables, which are described in the following.

Table general

The table general is mandatory as the file name of the TD database must be specified in the thermo_data_fname parameter. The corresponding file must have the same structure as the file ‘thermo_data.thermodb’ used in the pyTFA package.

general: general configuration data

Column	Contents	Example
parameter	parameter name	thermo_data_fname
value	value	data/thermo_data.thermodb

Following parameters can be configured with this table:

Parameter	Description	Example
thermo_data_fname	parameter name	data/thermo_data.thermodb
mid_regex_pattern	regular expression pattern to extract metabolite identifier from species identifier	^M_(w+)_w+$

Table td_compartments

The table designated td_compartments is mandatory and contains the thermodynamics data related to the compartments defined in the model. The number of columns is determined by the number of compartments, with membrane potential columns <cid>_mV added for each compartment.

td_compartments: compartment specific thermodynamics configuration

Column	Contents	Example
cid	compartment identifier	c
ph	compartmental pH	7.5
ionic_strength_M	ionic strength in mol/L	0.25
c_min_M	default minimal metabolite concentration in mol/L	1.0e-8
c_max_M	default maximal metabolite concentration in mol/L	0.05
<cid>_mV	membrane potentials in mV (<cid> el. potential - own el. potential)	150.0

Table modify_td_sids

The optional table modify_td_sids facilitates the hard linking of selected metabolites to specific TD data records, thereby overruling the automated matching procedure. Use of this table requires the parameter “mid_regex_pattern” in the table “general” to be configured.

modify_td_sids: link metabolite identifiers to TD database records

Column	Contents	Example
mid	metabolite identifier, without compartment prefix	h2o
td_sid	TD database record identifier	cpd00001

Table modify_thermo_data

The table entitled modify_thermo_data is optional and can be utilized to modify data in the TD database, which has been found to be inconsistent. Attributes employed by f2xba include ‘charge_std’, ‘formula’, ‘deltaGf_std’ and ‘name’.

modify_thermo_data: modify metabolite records in TD database

Column	Contents	Example
td_sid	TD database record identifier	cpd00637
attribute	record attribute	charge_std
value	value	1

Table modify_drg0_bounds

The table entitled modify_drg0_bounds is optional. Typically, this table is generated automatically as a byproduct of parameter relaxation, and it encompasses adjustments to the lower and upper bounds of the standard transformed Gibbs energy of reaction variables.

modify_drg0_bounds: modify lower and upper bounds of variables

Column	Contents	Example
id	variable identifier	V_DRG0_MECDPS
component	set to ‘reaction’	reaction
attribute	either ‘fbc_lower_bound’ or ‘fbc_upper_bound’	fbc_lower_bound
value	numerical value	83.3918

Bulk configuration

This bulk mappings configuration file is used for large scale XbaModel modifications, which includes adding or updating MIRIAM annotations to species, reaction and gene product components. The bulk mappings configuration file can be referenced in the XBA configuration file (table “general”, parameter “bulk_mappings_fname”). The configuration data could be generated based on supplementary information of poorly annotated metabolic models or based on database research.

Table species

The species table is utilized for the purpose of bulk updating species components within the model. For instance, this facilitates model annotation.

species: bulk update of species

Column	Contents	Example
id	species identifier	M_f6p_c
MA:bigg.metabolite	add BiGG identifier (default:None)	f6p
MA:kegg.compound	add KEGG compound annotation, used for turnover number prediction (default: None)	C00085
MA:seed.compound	add SEED compound annotation, used in TD modelling (default: None)	cpd00072
MA:chebi	add ChEBI annotation, used for RBA enzyme cofactors (default: None)	CHEBI:10375

Note: SBML parameters “compartment”, “fbcCharge”, “fbcChemicalFormula”, “substanceUnits”, “constant”, “boundaryCondition”, “hasOnlySubstanceUnits”, “miriamAnnotation”, “xmlAnnotation”, “metaid”, “sboterm” and “notes” can be configured as well.

Table reactions

The table entitled reactions .

The reactions table is utilized for the purpose of bulk updating reaction components within the model. For instance, this facilitates model annotation.

reactions:

Column	Contents	Example
id	variable identifier	R_PGI
name	set reaction name (default: None)	Glucose-6-phosphate isomerase
MA:ec-code	add EC code annotation(default: None)	5.3.1.9

Notes: SBML parameters “reactants”, “products”, “reversible”, “fbcGeneProdAssoc”, “fbcLowerFluxBound”, “fbcUpperFluxBound”, “miriamAnnotation”, “xmlAnnotation”, “metaid”, “sboterm” and “notes” can be configured as well.

Table fbcGeneProducts

The fbcGeneProducts table is employed for the purpose of bulk updating gene product components within the model. Gene locus identifiers provided in the label column are used to generate new or substitute existing gene products.

fbcGeneProducts: bulk modification of gene products

Column	Contents	Example
id	gene product identifier	G_MMSYN1_0445
label	gene locus identifier (default: None)	JCVISYN3A_0445
MA:uniprot	update/create uniprot identifier in MIRIAM annotation (default: None)	AVX54806.1

Note: SBML parameters “miriamAnnotation”, “xmlAnnotation”, “metaid”, “sboterm” and “notes” can be configured as well.

Table groups

The groups table contains configuration data that is used to replace the SBML groups component (SBML Level 3 groups package) in the model. In the event that the SBML groups component is not present, it is added to the model.

groups:

Column	Contents	Example
id	group identifier	g1
name	descriptive name	Central metabolism
kind	nature of the group :’partonomy’, ‘classification’ or ‘collection’ (default: ‘partonomy’)	None
members	list of reaction identifiers	idRef=R_PGI; idRef=R_PFK; idRef=R_FBA; …

Turnover numbers

A template file containing default turnover numbers for each enzyme-catalyzed reaction can be extracted from the XbaModel instance by calling the function “xba_model.export_kcats()”. The turnover number configuration file, which can be referenced in the XBA configuration file (table “general”, parameter “kcats_fname”), contains a single table designated kcats with the following data:

kcats: configuration of turnover numbers

Column	Contents	Example
key	reaction identifier	R_FLVR_iso1
rid	net reaction identifier (= FBA reaction id)	R_FLVR
dirxn	reaction direction (1: forward, -1: reverse)	1
enzyme	enzyme identifier	enz_b2763_b2764
kcat_per_s	turnover number in s-1 (per active site)	10.27

Enzyme composition

A template file containing default enzyme compositions can be extracted by calling the function “xba_model.export_enz_composition()”. The enzyme composition file, which can be referenced in the XBA configuration file (table “general”, parameter “enzyme_comp_fname”), contains a single table designated enzymes with the following data:

enzymes: configuration of enzyme composition

Column	Contents	Example
eid	enzyme identifier	enz_b2763_b2764
name	enzyme name	assimilatory sulfite reductase (NADPH)
composition	proteins (designated by gene label) and stoichiometry	gene=b2763, stoic=4.0; gene=b2764, stoic=8.0
active_sites	number of active sites	4

Proteomics

Following the optimization of the model across a singular or multiple conditions, the data in the proteomics table can be utilized to create correlation reports between predicted and measured protein levels, for protein correlation plots, and to add information to results related to proteins. Proteins are designated by their gene locus in the first column of the table. Columns <condition>, with condition names used during optimization, contain the values in units of mg measured protein to g total protein (mpmf; milli protein mass fraction).

The locus field

proteomics: measured protein concentrations in mg/gP

Column	Contents	Example
locus	gene locus identifier	b2763
uniprot	UniProt identifier	P17846
description	protein name	Sulfite reductase [NADPH] hemoprotein beta-component
gene_name	gene name	cysI
mw_Da	protein molecular weight in g/mol	63939.93
avg_mpmf	mean value of mpmf across conditions	0.9764
rank	rank after sorting avg_mpmf	204
<condition>	mg measured protein per gram total protein (mpmf)	1.3147

Thermodynamics Database

The thermodynamics database is required to generate models with thermodynamics constraints. The format of this database corresponds to the TD database used by pyTFA package. The file contents is a pickled Python dictionary compressed by zlib. This Python dictionary contains 4 entries:

thermodynamics database

Key	Contents	Example
name	database name	DB_AlbertyUpdate
units	units (‘kcal/mol’ or ‘kJ/mol’)	kcal/mol
metabolites	thermodynamics data related to metabolites	Python dictionary, see below
cues	thermodynamics data related to cues	Python dictionary, see below

Metabolites data

Metabolites data is stored under the key metabolites. It is a dictionary with keys set to the metabolite identifier (SEED compound id) and values stored in dictionaries as given below.

metabolites: thermodynamics data related to metabolites

Key	Contents	Example
id	TD data record identifier	cpd00002
name	compound name	ATP
other_names	list of alternative names	[‘ATP’, ‘Adenosine 5-triphosphate’, ‘atp’]
nH_std	number of hydrogen atoms in standard condition	13
mass_std	molecular mass (g/mol) of compound in standard condition	504.0
formula	chemical formula in standard condition	C10H13N5O13P3
deltaGf_std	Gibbs energy of formation in standard condition	-673.85
error	error flag	Nil
charge_std	electrical charge in standard condition	-3
deltaGf_err	estimated error	3.0431
struct_cues	groups	{‘RWWNW’: 1, ‘RWCdblWW’: 1, ‘WNH2’: 1, ‘HeteroAromatic’: 2, …}
pKa	list of pKa values	[0.84, 1.83, 4.68, 7.6, 13.03]

Cues data

Cues data is stored under the key cues. It is a dictionary with keys set to the cue identifier containing the data in a dictionary as given below

cues: thermodynamics data related to cues

Key	Contents	Example
id	cues record identifier	RWWNW
names	list of names	[‘RWWNW’]
formula	electrical formula, if applicable	N1
charge	electrical charge	0
energy	energy contribution	22.1
error	estimated error	0.617
datfile	thermodynamics data file, if applicable	RWWNW.gds
small	boolean flag	False