3. Thermodynamic Databases¶

3.1. Converting a Matlab thermodynamic database¶

If you have a Matlab thermodynamic database, you can easily convert it to a Python database thanks to the script thermoDBconverter.py:

python thermoDBconverter.py database.mat converted_database.thermodb

3.2. Loading a thermodynamic database¶

Thermodynamic databases are compressed through zlib and binary-encoded with pickle. In order to load them, you need first to uncompress them with zlib.decompress then load the result into memory with pickle.loads:

import pickle
import zlib
with open('thermoDatabases/DB_AlbertyUpdate.thermodb', 'rb') as file:
    ReactionDB = pickle.loads(zlib.decompress(file.read()))

Warning

Since the file is compressed, you MUST load it as a binary file by calling open with the b flag, otherwise Python will try to decode it as unicode and raise an exception !

3.3. Structure of a thermodynamic database¶

A thermodynamic database is a dict with the following fields:

name : string The name of the database

units : string The unit of the energies in the database. Can be kcal/mol or kJ/mol.

metabolites : dict A dictionnary containing the metabolites’ thermodynamic data. See Metabolites for more information.

cues : dict A dictionnary containing the cues’ thermodynamic data. See Cues for more information.

3.3.1. Metabolites¶

This is a dictionnary storing various thermodynamic data about metabolites. It is stored as a dict where each key is a SeedID. The values are others dict with the following keys.

id	`string` SeedID of the metabolite
charge_std	`float` Charge of the metabolite (mV) in standard conditions
deltaGf_std	`float` Transformed Gibbs energy of formation of the metabolite, in standard conditions.
deltaGf_err	`float` Error on the transformed Gibbs energy of formation of the metabolite, in standard conditions
mass_std	`float` Mass of the metabolite (g.mol-1)
nH_std	`int` Number of protons of the metabolite, in standard conditions
error	`string` Error on the metabolite’s thermodynamic data. Thermodynamic values will be computed only if this equals to ‘Nil’.
formula	`string` Formula of the metabolite.
nH_std	`int` Number of protons in the metabolite’s formula
name	`string` Name of the metabolite
other_names	`list` (`string`) Other names of the metabolite
pKa	`list` (`float`) pKas of the metabolite
struct_cues	`dict` (`int`) cues of the metabolite The keys of the array are the names of the cues, and the values the number of cues of this type that are part of the structure.

Here is an example:

ReactionDB['metabolites']['cpd00001'] = {
  'charge_std': 0,
  'deltaGf_err': 0.5,
  'deltaGf_std': -56.686999999999998,
  'error': 'Nil',
  'formula': 'H2O',
  'id': 'cpd00001',
  'mass_std': 18.0,
  'nH_std': 2,
  'name': 'H2O',
  'other_names': ['H2O', 'Water', 'HO-', 'OH-', 'h2o'],
  'pKa': [15.7],
  'struct_cues': {'H2O': 1}
}

3.3.2. Cues¶

This is a dictionnary storing various thermodynamic data about cues. It is stored as a dict where each key is the cue ID, as referrenced in the struct_cues attribute of Metabolites. The values are others dict with the following keys.

id	`string` ID of the cue
charge	`float` The charge (mV) of the cue in standard conditions
datfile	`string` The dat file from which the data was imported. Optional
energy	`float` Transformed Gibbs energy of formation of the cue, in standard conditions.
error	`float` The error on the transformed Gibbs energy of formation of the cue, in standard conditions.
formula	`string` Formula of the cue
names	`list` (`string`) Other names of the cue
small	`bool` Whether this is a small cue or not

Here is an example:

ReactionDB['cues']['H2O'] = {
  'charge': 0,
  'datfile': 'H2O.gds',
  'energy': -56.686999999999998,
  'error': 0.5,
  'formula': 'H2O',
  'id': 'H2O',
  'names': ['H2O', 'OH-', 'HO-'],
  'small': True
}