ModelSEEDpy

https://modelseedpy.readthedocs.io/en/latest/
Admin
ModelSEEDpy is a comprehensive suite of packages that bootstrap metabolic modeling with the...
Tokens:177,176
Snippets:1,671
Trust Score:9.6
Update:4 months ago
Show doc for...
Tokens:
Raw
### MetaboFBAPkg Parameters Example

Source: https://modelseedpy.readthedocs.io/en/latest/fbapkg/metabofba_api.html

Example of a parameter dictionary for build_package. It includes an option to set the objective and a required 'peaks' key for metabolite identification.

```json
{
           "set_objective":true,
}
```

--------------------------------

### General Simulation Settings Example

Source: https://modelseedpy.readthedocs.io/en/latest/fbapkg/tfa_api.html

Example of general simulation settings for FullThermoPkg, including default concentration ranges, error tolerances, and temperature.

```json
{
           "default_max_conc":0.02,
           "default_min_conc":0.000001,
           "default_max_error":5,
           "custom_concentrations":{},
           "custom_deltaG_error":{},
           "compartment_potential":{},
           "temperature":298,
           "filter":null,
           "infeasible_model": false,
           "dgbin":false
}
```

--------------------------------

### MetaboFBA Parameters Example

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/fbapkg/metabofba_api.rst.txt

Example of parameters for the build_package method, including setting the objective and a list of peak data for metabolite identification.

```json
{
            "set_objective":true,
 }
```

--------------------------------

### Build Package Parameters Example

Source: https://modelseedpy.readthedocs.io/en/latest/fbapkg/kbasemedia_api.html

An example dictionary structure for parameters used in the build_package method, specifying default uptake, excretion, and media.

```json
{
               "default_uptake": 0,
               "default_excretion": 100,
               "media": null
}
```

--------------------------------

### KBaseMediaPkg Build Parameters

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/fbapkg/kbasemedia_api.rst.txt

Example JSON structure for specifying default uptake, default excretion, and media for the build_package function.

```json
{
                "default_uptake": 0,
                "default_excretion": 100,
                "media": null
 }
```

--------------------------------

### Default Simulation Parameters Example

Source: https://modelseedpy.readthedocs.io/en/latest/fbapkg/tfa_api.html

Example of default simulation parameters for FullThermoPkg, including concentrations, compartment potentials, and other simulation settings.

```json
{
           "cpd00067_c0":[0.0000001,0.0000001],
           "cpd00007_c0":[1E-07,8.2E-06],
           "cpd00011_c0":[1E-08,0.0014],
           "cpd00067_e0":[3.16228E-07,3.16228E-07],
           "cpd00009_e0":[0.056,0.056],
           "cpd00048_e0":[0.0030,0.0030],
           "cpd00013_e0":[0.019,0.019],
           "cpd00971_e0":[0.16,0.16],
           "cpd00205_e0":[0.022,0.022],
           "cpd10515_e0":[0.062,0.062],
           "cpd00011_e0":[0.00010,0.00010],
           "cpd00007_e0":[8.2E-06,8.2E-06],
           "cpd00027_e0":[0.020,0.020]
}
```

--------------------------------

### Example Minimal Media Dictionary Structure

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/community/mssmetana_api.rst.txt

Provides an example structure for the media_dict parameter, which can be used to pre-define minimal media for community members to optimize computation time.

```json
{
 "members": {
 "Bacillus_sp._bc15.RAST.fbamodel": {
 "media": {
 "EX_cpd00051_e0": 0.024950298185467054, "EX_cpd00644_e0": 0.0005612318319214348,
 "EX_cpd00654_e0": 0.0011184174481049735, "EX_cpd00393_e0": 0.0008418477478827718,
 "EX_cpd00149_e0": 0.00028061591596092344, "EX_cpd00030_e0": 0.0002806159159609234,
 "EX_cpd00182_e0": 0.012823174793819692, "EX_cpd00367_e0": 0.019860385045891646,
 "EX_cpd00063_e0": 0.0002806159159609234, "EX_cpd00066_e0": 0.015451949003231678,
 "EX_cpd00099_e0": 0.00028061591596092344, "EX_cpd15603_e0": 0.040392130687977465,
 "EX_cpd00254_e0": 0.00028061591596092344, "EX_cpd00264_e0": 0.00028061591596092344,
 "EX_cpd00209_e0": 0.29280989138763885, "EX_cpd00007_e0": 0.0007015397899023084,
 "EX_cpd00104_e0": 0.00028061591596092344, "EX_cpd00158_e0": 0.8572001890264485,
 "EX_cpd00065_e0": 0.004720191914454185, "EX_cpd10515_e0": 0.0008418477478827702,
 "EX_cpd00069_e0": 0.012067660460609869, "EX_cpd00048_e0": 0.00028061591596092344,
 "EX_cpd00793_e0": 0.0002806159159609235, "EX_cpd00058_e0": 0.00028061591596092344,
 "EX_cpd00107_e0": 0.037538884754252475, "EX_cpd00119_e0": 0.00792636000742222,
 "EX_cpd00156_e0": 0.035223318909387485, "EX_cpd00034_e0": 0.00028061591596092344,
 "EX_cpd00179_e0": 4.162129853322901e-13, "EX_cpd00017_e0": 0.0019643114116915986,
 "EX_cpd00205_e0": 0.00028061591596092344, "EX_cpd00277_e0": 0.014659099568563774,
 "EX_cpd00355_e0": 0.08097665993895736, "EX_cpd00220_e0": 0.0008418477478827705,
 "EX_cpd00322_e0": 0.024179851033877948, "EX_cpd00028_e0": 0.00028061591596092344
 }
 },
 "Sphingobium_AP49_pacbio_v2.RAST.fbamodel": {
 "media": {
 "EX_cpd00039_e0": 0.028543802049017904, "EX_cpd00118_e0": 0.000280615915959131,
 "EX_cpd01017_e0": 0.008456496968388943, "EX_cpd00051_e0": 0.024669682270134097,
 "EX_cpd00136_e0": 0.00028061591595913087, "EX_cpd00161_e0": 0.03565680232766155,
 "EX_cpd00009_e0": 0.08097472586643507, "EX_cpd00644_e0": 0.0005612318318649744,
 "EX_cpd00654_e0": 0.0011501166238305303, "EX_cpd00393_e0": 0.0008418477478774078,
 "EX_cpd00149_e0": 0.000280615915959131, "EX_cpd00030_e0": 0.000280615915959131,
 "EX_cpd00132_e0": 0.0200830806928348, "EX_cpd00060_e0": 0.011096446763321909,
 "EX_cpd00063_e0": 0.000280615915959131, "EX_cpd00066_e0": 0.015451949003134501
 }
 }
}
```

--------------------------------

### Install ModelSEEDpy

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/index.rst.txt

Install the ModelSEEDpy package using pip.

```bash
pip install modelseedpy
```

--------------------------------

### Install pyeda wheel on Windows

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/index.rst.txt

For Windows users, install the pyeda module by downloading the appropriate wheel and installing it via pip.

```bash
cd path/to/pyeda/wheel
pip install pyeda_wheel_name.whl
```

--------------------------------

### Kinetics Data Structure Example

Source: https://modelseedpy.readthedocs.io/en/latest/community/dfbapkg_api.html

Example JSON structure for providing parameterized kinetics data to the dFBAPkg.simulate() method. It includes rate laws, initial concentrations, and metabolite IDs for specific reactions.

```json
{
  "R_3OAS140": {
      "source_1": {
          "substituted_rate_law": "(68.0*A*B)/(50.0*0.34*C+360.0*B+0.34*A+A*B*C)",
          "initial_concentrations_M": {
              "A": 0.0200,
              "C": 0.022,
              "B": 0.0014
          },
          "met_id": {
              "A": "cpd11468",
              "B": "cpd00067",
              "C": "cpd11492"
          }
      }
  },
  "rxn2": {
      "source_1": {
          "substituted_rate_law": "(A*B)/(50.0*0.34*C+3*B+0.34*A+C)",
          "initial_concentrations_M": {
              "A": 0.0200,
              "C": 0.022,
              "B": 0.0012
          },
          "met_id": {
              "A": "cpd11468",
              "B": "cpd00011",
              "C": "cpd11492"
          }
      }
  }
}
```

--------------------------------

### Example Simulation Parameters (Concentrations)

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/fbapkg/tfa_api.rst.txt

Specifies default and custom concentrations for metabolites in different compartments. Required for building the simulation package.

```json
{
            "cpd00067_c0":[0.0000001,0.0000001],
            "cpd00007_c0":[1E-07,8.2E-06],
            "cpd00011_c0":[1E-08,0.0014],
            "cpd00067_e0":[3.16228E-07,3.16228E-07],
            "cpd00009_e0":[0.056,0.056],
            "cpd00048_e0":[0.0030,0.0030],
            "cpd00013_e0":[0.019,0.019],
            "cpd00971_e0":[0.16,0.16],
            "cpd00205_e0":[0.022,0.022],
            "cpd10515_e0":[0.062,0.062],
            "cpd00011_e0":[0.00010,0.00010],
            "cpd00007_e0":[8.2E-06,8.2E-06],
            "cpd00027_e0":[0.020,0.020]
 }
```

--------------------------------

### Get List of Exchange Reactions

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/msmodelutils_api.rst.txt

Retrieve all COBRA reaction objects that represent exchange reactions (ID starts with 'EX_').

```python
exchange_reactions = msutil.exchange_list()
```

--------------------------------

### Initialize KBaseMediaPkg

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/fbapkg/kbasemedia_api.rst.txt

Instantiate the KBaseMediaPkg class with a CobraKBase model.

```python
from modelseedpy.fbapkg import KBaseMediaPkg
kbmedia = KBaseMediaPkg(model)
```

--------------------------------

### Get Genome Classifier Model

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/model_reconstruction/Classifier.ipynb.txt

This snippet retrieves the 'knn_filter' genome classifier model from modelseedpy.helpers. Ensure the modelseedpy library is installed.

```python
from modelseedpy.helpers import get_classifier
```

```python
classifier = get_classifier('knn_filter')
```

--------------------------------

### Syntax Error Example

Source: https://modelseedpy.readthedocs.io/en/latest/flux_analysis/MetaboFBAExample.html

This snippet highlights a syntax error encountered during the execution of the MetaboFBA package setup, specifically related to the format of the 'peaks' data.

```python
File "<ipython-input-1-9c96e9aed238>", line 18
    mfp.build_package({"peaks":[{"id":"peak.1","inchi":,"set_objective":1})
                                                       ^
SyntaxError: invalid syntax
```

--------------------------------

### Load model, template, and media, then gapfill

Source: https://modelseedpy.readthedocs.io/en/latest/model_reconstruction/Gapfilling.html

Loads a model, template, and media from KBase, then uses MSBuilder to gapfill the model. It also builds a KBaseMediaPkg and optimizes the model.

```python
kbase_api = cobrakbase.KBaseAPI()
model = kbase_api.get_from_ws("test_model",18528)
#If you have CPLEX, uncomment this
#model.solver = 'optlang-cplex'
template = kbase_api.get_from_ws("GramNegModelTemplateV3","NewKBaseModelTemplates")
media = kbase_api.get_from_ws("Carbon-D-Glucose","KBaseMedia")
model = MSBuilder.gapfill_model(model,"bio1",template,media)

kmp = KBaseMediaPkg(model)
kmp.build_package(media)
sol=model.optimize()
model.summary()
```

--------------------------------

### Example Model Pair Specification

Source: https://modelseedpy.readthedocs.io/en/latest/community/mssmetana_api.html

Illustrates the dictionary format for specifying model pairs for analysis. Keys are model identifiers, and values are lists of models to be paired with the key.

```python
{
       model1: [model2, model3, model4, model5],
   model2: [model4, model6],
   model4: [model5, model7]
}
```

--------------------------------

### Annotation results example

Source: https://modelseedpy.readthedocs.io/en/latest/model_reconstruction/Genomes.html

Example of the structured data returned by the RAST annotation service, detailing the tools and parameters used.

```json
[{'execution_time': 1622756127.36331,
  'tool_name': 'kmer_search',
  'hostname': 'pear',
  'parameters': ['-a',
   '-g',
   200,
   '-m',
   5,
   '-d',
   '/opt/patric-common/data/kmer_metadata_v2',
   '-u',
   'http://pear.mcs.anl.gov:6100/query'],
  'id': '9CCA6D20-C4B3-11EB-A893-36A8BEF382BD'},
 {'parameters': ['annotate_hypothetical_only=1',
   'dataset_name=Release70',
   'kmer_size=8'],
  'hostname': 'pear',
  'tool_name': 'KmerAnnotationByFigfam',
  'id': '9CE3769E-C4B3-11EB-A893-36A8BEF382BD',
  'execution_time': 1622756127.52738},
 {'execute_time': 1622756127.88296,
  'hostname': 'pear',
  'parameters': [],
  'tool_name': 'annotate_proteins_similarity',
  'id': '9D19B7EA-C4B3-11EB-9714-71B3BDF382BD'}]
```

--------------------------------

### KBaseMediaPkg()

Source: https://modelseedpy.readthedocs.io/en/latest/fbapkg/kbasemedia_api.html

Initializes the KBaseMediaPkg class with a CobraKBase model.

```APIDOC
## KBaseMediaPkg()

### Description
Initializes the KBaseMediaPkg class with a CobraKBase model. This class constrains exchange reactions and media compounds.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
* **model** (`cobra.core.model.Model`): The CobraKBase model that will be edited. The conversion from standard COBRA models to CobraKBase models is facilitated by the cobrakbase package.
```

--------------------------------

### Compartment Potentials Example

Source: https://modelseedpy.readthedocs.io/en/latest/fbapkg/tfa_api.html

Example of compartment potentials for FullThermoPkg simulation. The extracellular environment ('e0') is set to 0 by default.

```json
{
           "e0":0,
           "c0":-160
}
```

--------------------------------

### Build and Solve Full Thermodynamics Package

Source: https://modelseedpy.readthedocs.io/en/latest/flux_analysis/FullThermodynamicsExample.html

Initializes the FullThermodynamicsPkg, builds the package with ModelSEED data, writes the LP file, and solves the model. Ensure the ModelSEED database path is correct.

```python
ftp = FullThermoPkg(model)
modelseed_db_path = os.path.join('..', '..', '..', 'ModelSEEDDatabase')  # /Users/chenry/code/ModelSEEDDatabase/
ftp.build_package({"modelseed_path":modelseed_db_path})
with open('FullThermo.lp', 'w') as out:
    out.write(str(model.solver))
sol=model.optimize()
#Next minimize active reactions
model.summary()
```

--------------------------------

### Example Simulation Parameters (General Settings)

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/fbapkg/tfa_api.rst.txt

General simulation settings including default concentration bounds, error tolerances, temperature, and filtering options. The 'modelseed_db_path' is a required key.

```json
{
            "default_max_conc":0.02,
            "default_min_conc":0.000001,
            "default_max_error":5,
            "custom_concentrations":{},
            "custom_deltaG_error":{},
            "compartment_potential":{},
            "temperature":298,
            "filter":null,
            "infeasible_model": false,
            "dgbin":false
 }
```

--------------------------------

### Initialize Full Thermodynamics Package

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/flux_analysis/FullThermodynamicsExample.ipynb.txt

Initializes the FullThermoPkg with a given model. This is a prerequisite for performing thermodynamic calculations.

```python
ftp = FullThermoPkg(model)
```

--------------------------------

### Initialize Gapfilling Package

Source: https://modelseedpy.readthedocs.io/en/latest/model_reconstruction/Gapfilling.html

Initializes the GapfillingPkg with a core model and a template for gapfilling.

```python
from modelseedpy import FBAHelper
from modelseedpy import GapfillingPkg
pkg_gf = GapfillingPkg(model_core)
pkg_gf.build(template_gramneg)
```

--------------------------------

### Initialize MSCommPhitting

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/community/mscommphitting_api.rst.txt

Instantiate the MSCommPhitting class with the path to the ModelSEED Database and optional community member data, experimental data, or pre-processed files. The solver can also be specified.

```python
from modelseedpy.community import MSCommPhitting
mscommfit = MSCommPhitting(msdb_path, community_members: dict=None, fluxes_df=None, 
                            growth_df=None, carbon_conc=None, media_conc=None, 
                            experimental_metadata=None, base_media=None, solver: str = "glpk", 
                            all_phenotypes=True, data_paths: dict = None, 
                            species_abundances: str = None, carbon_conc_series: dict = None,
                            ignore_trials: Union[dict, list] = None, ignore_timesteps: list = None, 
                            species_identities_rows=None, significant_deviation: float = 2, 
                            extract_zip_path: str = None)
```

--------------------------------

### Commented Reaction Examples

Source: https://modelseedpy.readthedocs.io/en/latest/model_reconstruction/Biochem.html

Provides examples of reaction stoichiometry with different compartment notations ('_0', '_k', '_c'). These are commented-out lines, likely for reference or testing.

```python
#2 cpd00001_0 + 4.0 cpd00067_1 + 2 cpd07274_0 + cpd11632_0      --> 1 cpd00007_0 + 4.0 cpd00067_0 + 2 cpd16503_0
#2 cpd00001_k + 4.0 cpd00067_c + 2 cpd07274_k + 4.0 hvphoton2_c --> 1 cpd00007_k + 4.0 cpd00067_k + 2 cpd16503_k
```

--------------------------------

### Initialize GapfillingHelper

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/gapfillinghelper_api.rst.txt

Instantiate the GapfillingHelper class. Use 'blacklist' to exclude reactions from gapfilling and 'auto_sink' to specify biomass growth reactions.

```python
gfhelper = GapfillingHelper(blacklist = [], auto_sink = ["cpd02701_c", "cpd11416_c0", "cpd15302_c"])
```

--------------------------------

### Example Output of Reaction Fluxes

Source: https://modelseedpy.readthedocs.io/en/latest/flux_analysis/FlexibleBiomass.html

This is an example output showing the calculated fluxes for various metabolites and biomass components from the Flexible Biomass Package. The values represent the primal values of the reaction variables.

```text
FLEX_cpd00201_c0:-3.450255666989782e-05
FLEX_cpd24604_c0:-4.022719611736966e-06
FLEX_cpd03445_c0:-3.450255666989782e-05
FLEX_cpd24682_c0:-4.022719611736965e-05
FLEX_cpd00035_c0:0.0794779544089827
FLEX_cpd00017_c0:-3.450255666989782e-05
FLEX_cpd00051_c0:-0.04576493382288079
FLEX_cpd00132_c0:0.03729602600027901
FLEX_cpd00041_c0:0.03729602600027901
FLEX_cpd00104_c0:-3.0943997013361277e-07
FLEX_cpd00063_c0:0.0008053175222727272
FLEX_cpd00099_c0:0.0008053175222727272
FLEX_cpd00010_c0:-8.911871139848047e-05
FLEX_cpd00149_c0:3.86799962667016e-06
FLEX_cpd00052_c0:0.020656355766299185
FLEX_cpd00058_c0:0.00010969646941236572
FLEX_cpd00084_c0:-0.014169256232418128
FLEX_cpd00115_c0:-0.0040484031292580555
FLEX_cpd00356_c0:0.004180069836549908
FLEX_cpd00241_c0:-0.004180069836549908
FLEX_cpd00015_c0:-3.450255666989782e-05
FLEX_cpd10515_c0:0.0010389446997236048
FLEX_cpd10516_c0:0.0012080536434016242
FLEX_cpd00053_c0:0.040716111270180766
FLEX_cpd00023_c0:0.040716111270180766
FLEX_cpd00033_c0:-0.09478734221135812
FLEX_cpd00038_c0:-0.023642791862056332
FLEX_cpd00119_c0:-0.014657861945259103
FLEX_cpd00322_c0:-0.044950642541474184
FLEX_cpd00205_c0:0.030200258045145142
FLEX_cpd03586_e0:0.003010232029459785
FLEX_cpd00107_c0:0.06970614959213335
FLEX_cpd00039_c0:0.05309386479551034
FLEX_cpd00060_c0:-0.023778295624977203
FLEX_cpd00254_c0:0.0013421958704545453
FLEX_cpd00125_c0:-3.450255666989782e-05
FLEX_cpd00030_c0:0.00010691150968116322
FLEX_cpd11574_c0:1.0830398954676446e-06
FLEX_cpd15514_p0:-0.0021496794725182075
FLEX_cpd00003_c0:-0.00028329229265732246
FLEX_cpd00006_c0:-6.915983332486246e-05
FLEX_cpd00013_c0:0.0020133711656743518
FLEX_cpd00244_c0:4.997455517657846e-05
FLEX_cpd15531_p0:0.009873301127053181
FLEX_cpd15532_p0:0.011637108956814775
FLEX_cpd00066_c0:-0.028664198033401884
FLEX_cpd00028_c0:-3.450255666989782e-05
FLEX_cpd00129_c0:-0.03420162629894288
FLEX_cpd00016_c0:3.450255666989782e-05
FLEX_cpd00220_c0:-3.450255666989782e-05
FLEX_cpd00054_c0:0.03338733501753628
FLEX_cpd00557_c0:-3.450255666989782e-05
FLEX_cpd00048_c0:0.000671175295219806
FLEX_cpd00078_c0:-1.5162558536547025e-05
FLEX_cpd00087_c0:-3.450255666989782e-05
FLEX_cpd00056_c0:-3.450255666989782e-05
FLEX_cpd00161_c0:-0.018206546111615842
FLEX_cpd00065_c0:-0.008794748111152474
FLEX_cpd00069_c0:-0.02133526706077233
FLEX_cpd25775_c0:-8.509599178674351e-06
FLEX_cpd00034_c0:5.275951490778098e-05
rna_flex:-0.15471998506680637
dna_flex:0.010314665671120425
protein_flex:0.02475519761068902
energy_flex:-0.0412586626844817
```

--------------------------------

### Initialize MSMedia

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/msmedia_api.rst.txt

Instantiate an MSMedia object to represent a complete media composition for investigation.

```python
from modelseedpy.core import MSMedia
msmedia = MSMedia(media_id)
```

--------------------------------

### Initialize dFBAPkg

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/community/dfbapkg_api.rst.txt

Instantiate the dFBAPkg class with a COBRA model and ModelSEED database path. Configure simulation parameters like solver and verbosity.

```python
from modelseedpy.community import dFBAPkg
dfba = dFBAPkg(model, modelseed_db_path, solver = 'glpk', warnings = True, verbose = False, printing = False, jupyter = False)
```

--------------------------------

### Get Compartment

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/mstemplate_api.rst.txt

Retrieve the compartment information for a reaction.

```python
comptment = rxn_template.compartment()
```

--------------------------------

### Importing Libraries and Setting Environment

Source: https://modelseedpy.readthedocs.io/en/latest/flux_analysis/FullThermodynamicsExample.html

Imports necessary libraries and sets up the KBase environment. Ensure you have the correct path to cobrakbase and ModelSEEDpy.

```python
# define the environment path
import os
local_cobrakbase_path = os.path.join('C:', 'Users', 'Andrew Freiburger','Documents','Argonne','cobrakbase')
os.environ["HOME"] = local_cobrakbase_path

# import the models
import cobrakbase
token = 'WE6CHYRDTJSGOHFIDGPE7WYFT6PRPXJL'
kbase_api = cobrakbase.KBaseAPI(token)

#Put the path to ModelSEEDpy on your machine here
# import sys
# sys.path.append("/Users/chenry/code/ModelSEEDpy")
#import modelseedpy.fbapkg
from modelseedpy import KBaseMediaPkg, FullThermoPkg
from modelseedpy import FBAHelper
```

--------------------------------

### Get Stoichiometry

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/mstemplate_api.rst.txt

Retrieve the stoichiometric coefficients for each metabolite in the reaction.

```python
rxn_stoichiometry = rxn_template.cstoichiometry()
```

--------------------------------

### Build Simulation Package with Parameters

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/fbapkg/tfa_api.rst.txt

Builds the simulation package using provided parameters. Use verbose mode for detailed output during troubleshooting.

```python
tfa.build_package(parameters, verbose = True)
```

--------------------------------

### Get Number of Reactions

Source: https://modelseedpy.readthedocs.io/en/latest/model_reconstruction/Gapfilling.html

Returns the total number of reactions in the model.

```python
len(model_core.reactions)
```

--------------------------------

### GapfillingHelper Initialization

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/gapfillinghelper_api.rst.txt

Initializes the GapfillingHelper with a blacklist of reactions to ignore and a list of reactions to automatically consider as sinks (e.g., for biomass growth).

```APIDOC
## GapfillingHelper()

### Description
Initializes the GapfillingHelper with a blacklist of reactions to ignore and a list of reactions to automatically consider as sinks (e.g., for biomass growth).

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
- **blacklist** (list) - The collection of IDs for reactions that will not be examined during gapfilling.
- **auto_sink** (list) - The collection of IDs for reactions that represent biomass growth.
```

--------------------------------

### Graphing Configurations for MSCommPhitting

Source: https://modelseedpy.readthedocs.io/en/latest/community/mscommphitting_api.html

This example demonstrates how to configure the 'graphs' argument to specify different types of plots, including biomass, concentration, and total biomass, with options for filtering by trial, phenotype, and experimental data.

```json
[
   {
       "trial":"G48",
       "phenotype": "*",
       "content": "biomass",
       "experimental_data": false
   },
   {
       "trial":"G48",
       "content": "conc"
   },
   {
       "trial":"G48",
       "phenotype": "*",
       "content": "biomass",
       "parsed": true
   },
   {
       "trial":"G48",
       "content": "total_biomass",
       "experimental_data": true
   }
]

```

--------------------------------

### Get Gene Reaction Rule

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/mstemplate_api.rst.txt

Retrieve the gene complexes associated with a reaction.

```python
gene_rules = rxn_template.gene_reaction_rule()
```

--------------------------------

### Initialize ElementUptakePkg

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/fbapkg/elementuptakepkg_api.rst.txt

Instantiate the ElementUptakePkg class with a CobraKBase model.

```python
from modelseedpy.community import ElementUptakePkg
eleup = ElementUptakePkg(model)
```

--------------------------------

### Get Reaction Template Data

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/mstemplate_api.rst.txt

Retrieve the complete data dictionary for the MSTemplateReaction object.

```python
rxn_template_dict = rxn_template.get_data()
```

--------------------------------

### Initialize MSSmetana Class

Source: https://modelseedpy.readthedocs.io/en/latest/community/mssmetana_api.html

Instantiate the MSSmetana class with community member models and simulation parameters. Use this for comprehensive community metric calculations.

```python
from modelseedpy.community import MSSmetana
mssmet = MSSmetana(self, member_models: Iterable, min_growth=0.1, n_solutions=100,
                   environment=None, abstol=1e-3, media_dict=None,
                   printing=True, raw_content=False, antismash_json_path:str=None,
                   antismash_zip_path:str=None, minimal_media_method="minFlux")
```

--------------------------------

### Get Roles per Complex

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/mstemplate_api.rst.txt

Create a dictionary mapping each complex to its associated roles.

```python
roles = rxn_template.get_complex_roles()
```

--------------------------------

### Initialize ReactionUsePkg

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/fbapkg/reactionuse_api.rst.txt

Instantiate the ReactionUsePkg class with a CobraKBase model.

```python
from modelseedpy.fbapkg import ReactionUsePkg
rxnuse = ReactionUsePkg(model)
```

--------------------------------

### Get Roles from Complexes

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/mstemplate_api.rst.txt

Retrieve all unique roles present across all complexes in the reaction.

```python
roles = rxn_template.get_roles()
```

--------------------------------

### Get Exchange Reaction Hash

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/msmodelutils_api.rst.txt

Create a dictionary mapping metabolites to their corresponding exchange reactions.

```python
exchange_reactions = msutil.exchange_hash()
```

--------------------------------

### Load Model and Perform Thermodynamic Analysis

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/flux_analysis/FullThermodynamicsExample.ipynb.txt

Loads a model from the KBase workspace, sets the solver, builds a KBaseMediaPkg, defines the objective function, optimizes the model, and generates a summary. This is the primary workflow for thermodynamic analysis.

```python
model = kbase_api.get_from_ws("E_iAH991V2",40576)
model.solver = 'glpk'
kmp = KBaseMediaPkg(model)
kmp.build_package(None)
FBAHelper.set_objective_from_target_reaction(model,"bio1")
sol=model.optimize()
model.summary()
```

--------------------------------

### Get MSModel Template and Genome

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/msmodel_api.rst.txt

Retrieves the template and genome objects associated with an MSModel instance.

```python
template, genome = msmodel.template(), msmodel.genome()
```

--------------------------------

### Initialize KBase API Client

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/kbase/Workspace.ipynb.txt

Initialize the KBaseAPI client. You can either use default authentication or provide a specific login token.

```python
kbase = cobrakbase.KBaseAPI() # or kbase = cobrakbase.KBaseAPI(YOUR_LOGIN_TOKEN)
```

--------------------------------

### Load models and build packages

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/flux_analysis/MetaboFBAExample.ipynb.txt

Loads a model, template, media, and metabolomics data from KBase. It then constructs gapfilling and media packages before initializing the MetaboFBAPkg.

```python
kbase_api = cobrakbase.KBaseAPI()
model = kbase_api.get_from_ws("test_model",18528)
pickaxe_model = kbase_api.get_from_ws("MMSyn3Expansion",29280)
model.solver = 'optlang-cplex'
template = kbase_api.get_from_ws("GramNegModelTemplateV3","NewKBaseModelTemplates")
media = kbase_api.get_from_ws("Carbon-D-Glucose","KBaseMedia")
metabolome = kbase_api.get_from_ws("JCVI_Syn3_metabolomics",29280)
FBAHelper.set_objective_from_target_reaction(model,"bio1")
copy_model = cobra.io.json.from_json(cobra.io.json.to_json(model))
gfp = GapfillingPkg(copy_model)
gfp.build_package({
    "default_gapfill_models":[pickaxe_model]
    "default_gapfill_templates":[template],
    "gapfill_all_indecies_with_default_templates":1,
    "minimum_obj":0.01,
    "set_objective":0
})
kmp = KBaseMediaPkg(copy_model)
kmp.build_package(media)
mfp = MetaboFBAPkg(copy_model)
mfp.build_package({"peaks":metabolome.build_peaks_data(),"set_objective":1})
sol=model.optimize()
with open('MetaboFBA.lp', 'w') as out:
    out.write(str(model.solver))
#Next minimize active reactions
model.summary()

cobra.io.save_json_model(model,filename)
```

--------------------------------

### Get Compartment of Greatest Interest

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/msmodel_api.rst.txt

Filters a list of compartments to determine the compartment of greatest interest.

```python
compartment_index = get_cmp_token(compartments)
```

--------------------------------

### Initialize GapfillingPkg

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/fbapkg/gapfillingpkg_api.rst.txt

Instantiate the GapfillingPkg with a CobraKBase model.

```python
from modelseedpy.fbapkg import ReactionUsePkg
gapfill = GapfillingPkg(model)
```

--------------------------------

### Exchange Reaction Definitions

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/model_reconstruction/Biochem.ipynb.txt

Examples of exchange reactions, which represent the transport of metabolites into or out of the system. These are typically reversible.

```text
EX_cpd00067_e0: cpd00067_e0 <=>  

```

```text
EX_cpd00159_e0: cpd00159_e0 <=>  

```

```text
EX_cpd00971_e0: cpd00971_e0 <=>  

```

```text
EX_cpd00009_e0: cpd00009_e0 <=>  

```

```text
EX_cpd00209_e0: cpd00209_e0 <=>  

```

```text
EX_cpd00058_e0: cpd00058_e0 <=>  

```

```text
EX_cpd00075_e0: cpd00075_e0 <=>  

```

```text
EX_cpd00013_e0: cpd00013_e0 <=>  

```

```text
EX_cpd00011_e0: cpd00011_e0 <=>  

```

```text
EX_cpd00029_e0: cpd00029_e0 <=>  

```

```text
EX_cpd00007_e0: cpd00007_e0 <=>  

```

```text
EX_cpd00047_e0: cpd00047_e0 <=>  

```

```text
EX_cpd00001_e0: cpd00001_e0 <=>  

```

```text
EX_cpd00020_e0: cpd00020_e0 <=>  

```

```text
EX_cpd00363_e0: cpd00363_e0 <=>  

```

```text
EX_cpd11640_e0: cpd11640_e0 <=>  

```

```text
EX_cpd00221_e0: cpd00221_e0 <=>  

```

--------------------------------

### Get Complex Template Data

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/mstemplate_api.rst.txt

Retrieves the complex template information as a dictionary. This is useful for serialization or inspection.

```python
complex_data = complex_template.get_data()
```

--------------------------------

### Initialize KBaseAPI

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/model_reconstruction/Biochem.ipynb.txt

Initializes the KBaseAPI client for interacting with the KBase platform.

```python
import cobrakbase
kbase = cobrakbase.KBaseAPI()

```

--------------------------------

### Get MSTemplateMetabolite Data

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/mstemplate_api.rst.txt

Retrieves the data content of an MSTemplateMetabolite object as a dictionary. Useful for serialization or inspection.

```python
met_template_dict = met_template.get_data()
```

--------------------------------

### Initialize KBase API

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/kbase/FBAModel.ipynb.txt

Initializes the KBaseAPI client. This is the entry point for interacting with KBase services.

```python
kbase = cobrakbase.KBaseAPI()
```

--------------------------------

### Get Media ID

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/fbahelper_api.rst.txt

Retrieves the ID of a media. Defaults to 'Complete' if no specific media ID is provided.

```python
media_id = fbahelper.mediaName()
```

--------------------------------

### MSCommPhitting()

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/community/mscommphitting_api.rst.txt

Initializes the MSCommPhitting class. This class loads and parameterizes experimental data, defines a linear problem for the system, simulates the linear programming problem, and graphically presents the results. It can either parse community members and experimental data directly or load pre-processed experimental data files.

```APIDOC
## MSCommPhitting()

### Description
Initializes the MSCommPhitting class to load and parameterize experimental data, define a linear problem for the defined system with the parameterized data, simulate the LP, and graphically present the results. It can parse member models and experimental data directly or load processed files of experimental data.

### Parameters

- **msdb_path** (`str`): Required. The path to the ModelSEED Database GitHub repository.
- **community_members** (`dict`): Optional. Describes the member models and phenotypes in the simulated community. See source for example structure.
- **fluxes_df** (`Pandas DataFrame`): Optional. Metabolic flux profile for each phenotype. Each column is a phenotype, each row is an exchange reaction, and each element is the flux.
- **growth_df** (`Pandas DataFrame`): Optional. Parsed and organized experimental data. Indexed by ``short_codes`` with ``trial_IDs`` providing details on member abundances and compound concentrations.
- **carbon_conc** (`dict`): Optional. Concentrations of carbon sources as ModelSEED IDs, denoted by either ``columns`` or ``rows`` for the dimension in the experimental well-plate where the specified concentration varies. See source for example structure.
- **media_conc** (`dict`): Optional. The concentrations of media components.
- **experimental_metadata** (`dict`): Optional. Metadata related to the experimental data.
- **base_media** (`dict`): Optional. The base media composition.
- **solver** (`str`): Optional. The solver to use for the linear programming problem. Defaults to "glpk".
- **all_phenotypes** (`bool`): Optional. Whether to consider all phenotypes. Defaults to True.
- **data_paths** (`dict`): Optional. Paths to data files.
- **species_abundances** (`str`): Optional. Path to species abundances file.
- **carbon_conc_series** (`dict`): Optional. Dictionary of carbon concentration series.
- **ignore_trials** (`Union[dict, list]`): Optional. Trials to ignore.
- **ignore_timesteps** (`list`): Optional. Timesteps to ignore.
- **species_identities_rows** (`bool`): Optional. Whether species identities are in rows.
- **significant_deviation** (`float`): Optional. Threshold for significant deviation. Defaults to 2.
- **extract_zip_path** (`str`): Optional. Path to extract zip file.
```

--------------------------------

### Get Template Data

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/mstemplate_api.rst.txt

Retrieve all data associated with the template, organized into a dictionary structure. This is useful for inspection or serialization.

```python
template_data = template.get_data()
```

--------------------------------

### Add and Get Complexes

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/mstemplate_api.rst.txt

Manage complexes associated with a reaction. Use add_complexes to extend the list and get_complexes to retrieve them.

```python
rxn_template.add_complexes(complex_list)
complexes = rxn_template.get_complexes()
```

--------------------------------

### Initialize KBaseAPI

Source: https://modelseedpy.readthedocs.io/en/latest/others/MPA_poster.html

Instantiate the KBaseAPI object to interact with the KBase workspace. Ensure a KBase token is available, either through a login session or saved to ~/.kbase/token.

```python
from cobrakbase import KBaseAPI
kbase = KBaseAPI()
```

--------------------------------

### Get MSTemplateSpecies Compound Properties

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/mstemplate_api.rst.txt

Retrieves the compound object, name, or formula associated with an MSTemplateSpecies. These are property accessors.

```python
template_compound = met_template.compound()
compound_name = met_template.name()
compound_formula = met_template.formula()
```

--------------------------------

### Apply Flexible Biomass Constraints

Source: https://modelseedpy.readthedocs.io/en/latest/flux_analysis/FlexibleBiomass.html

This snippet demonstrates setting up a metabolic model with KBase API, applying element uptake limits, KBase media constraints, and then configuring the FlexibleBiomassPkg. It sets biomass reaction ID and defines constraints for RNA, DNA, protein, and energy classes. Finally, it sets the objective to maximize biomass and writes the LP file.

```python
kbase_api = cobrakbase.KBaseAPI(token = "5YAOMNFB7XOEKBKHRHJB4AIZSQ2EJGA5")

model = kbase_api.get_from_ws("C4_Calvin_Zmays_Model",25427)
#model = kbase_api.get_from_ws("E_iML1515.kb",40576)
model.solver = 'optlang-cplex'

#Applying uptake limit to total carbon uptake by model
eup = ElementUptakePkg(model)
eup.build_package({"C":60})
#Applying constraints based on KBase media
#media = kbase_api.get_from_ws("Btheta_Ecoli_minimal_media",40576)
kmp = KBaseMediaPkg(model)
#kmp.build_package(media)
kmp.build_package(None)
#Applying flexible biomass constraints
fbp = FlexibleBiomassPkg(model)
fbp.build_package({"bio_rxn_id":"bio1","use_rna_class":[-0.75,0.75],
            "use_dna_class":[-0.75,0.75],
            "use_protein_class":[-0.1052,-0.1052],
            "use_energy_class":[-0.1,0.1]})
#Setting the objective to biomass
biomass_objective = model.problem.Objective(
    1 * model.reactions.bio1.flux_expression,
    direction='max')
model.objective = biomass_objective
#Printing LP file
with open('FlexibleBiomass.lp', 'w') as out:
    out.write(str(model.solver))
#Solving
sol=model.optimize()
model.summary()
```

--------------------------------

### Get Elemental Mass Dictionary

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/fbahelper_api.rst.txt

Retrieves a dictionary containing the masses of all elements. This can be used for various chemical calculations.

```python
elementmasses = fbahelper.elemental_mass()
```

--------------------------------

### Initialize MSCommPhitting

Source: https://modelseedpy.readthedocs.io/en/latest/community/mscommphitting_api.html

Instantiate the MSCommPhitting class with various parameters to load and process experimental data for community modeling.

```python
from modelseedpy.community import MSCommPhitting
mscommfit = MSCommPhitting(msdb_path, community_members: dict=None, fluxes_df=None,
                           growth_df=None, carbon_conc=None, media_conc=None,
                           experimental_metadata=None, base_media=None, solver: str = "glpk",
                           all_phenotypes=True, data_paths: dict = None,
                           species_abundances: str = None, carbon_conc_series: dict = None,
                           ignore_trials: Union[dict, list] = None, ignore_timesteps: list = None,
                           species_identities_rows=None, significant_deviation: float = 2,
                           extract_zip_path: str = None)
```

--------------------------------

### Get Alias Hash from MSGenome

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/core/msgenome_api.rst.txt

Retrieve a dictionary mapping aliases to their corresponding genes for all features within the genome.

```python
alias_hash = msgen.alias_hash()
```

--------------------------------

### Import necessary libraries

Source: https://modelseedpy.readthedocs.io/en/latest/model_reconstruction/Gapfilling.html

Imports the required libraries for model reconstruction and gapfilling. Uncomment the CPLEX import if you have CPLEX installed.

```python
import cobra
#If you have CPLEX, uncomment this
#import cplex
import cobrakbase
#import modelseedpy.fbapkg
from modelseedpy import GapfillingPkg, KBaseMediaPkg
from modelseedpy import FBAHelper, MSBuilder
```

--------------------------------

### dFBAPkg() Constructor

Source: https://modelseedpy.readthedocs.io/en/latest/_sources/community/dfbapkg_api.rst.txt

Initializes the dFBAPkg class for dynamic FBA simulations. It takes a COBRA model and ModelSEED database path as primary arguments, with optional parameters for solver selection and output verbosity.

```APIDOC
## dFBAPkg()

### Description
Initializes the dFBAPkg class for dynamic FBA simulations. It takes a COBRA model and ModelSEED database path as primary arguments, with optional parameters for solver selection and output verbosity.

### Parameters
- **model** (cobra.core.model.Model) - The COBRA model to be simulated.
- **modelseed_db_path** (str) - Path to a local version of the ModelSEED Database.
- **solver** (str, optional) - Specifies the linear programming algorithm to use. Defaults to 'glpk'.
- **warnings** (bool, optional) - Specifies whether to print simulation warnings. Defaults to True.
- **verbose** (bool, optional) - Specifies whether to print simulation details. Defaults to False.
- **printing** (bool, optional) - Specifies whether to print simulation calculations or results. Defaults to False.
- **jupyter** (bool, optional) - Specifies whether the simulation is conducted in a Jupyter notebook. Defaults to False.

### Example
```python
from modelseedpy.community import dFBAPkg
dfba = dFBAPkg(model, modelseed_db_path, solver = 'glpk', warnings = True, verbose = False, printing = False, jupyter = False)
```
```
Tokens:
Raw