2.1.1.2. wc_model_gen.prokaryote package

2.1.1.2.1. Submodules

2.1.1.2.2. wc_model_gen.prokaryote.core module

Generator for models based on KBs for random in silico organisms

Author

Jonathan Karr <karr@mssm.edu>

Date

2018-06-11

Copyright

2018, Karr Lab

License

MIT

class wc_model_gen.prokaryote.core.ProkaryoteModelGenerator(knowledge_base, component_generators=None, options=None)[source]

Bases: wc_model_gen.core.ModelGenerator

Generator for models based on KBs for random in silico organisms

DEFAULT_COMPONENT_GENERATORS = (<class 'wc_model_gen.prokaryote.initalize_model.InitalizeModel'>, <class 'wc_model_gen.prokaryote.transcription.TranscriptionSubmodelGenerator'>, <class 'wc_model_gen.prokaryote.rna_degradation.RnaDegradationSubmodelGenerator'>, <class 'wc_model_gen.prokaryote.translation.TranslationSubmodelGenerator'>, <class 'wc_model_gen.prokaryote.protein_degradation.ProteinDegradationSubmodelGenerator'>, <class 'wc_model_gen.prokaryote.metabolism.MetabolismSubmodelGenerator'>)[source]
clean_and_validate_options()[source]

Apply default options and validate options

2.1.1.2.3. wc_model_gen.prokaryote.initalize_model module

Initalize the construction of wc_lang-encoded models from wc_kb-encoded knowledge base.

Author

Balazs Szigeti <balazs.szigeti@mssm.edu>

Date

2018-01-21

Copyright

2018, Karr Lab

License

MIT

TODO: - read cytosol volume from DB; currently there is only fractional volume?!

class wc_model_gen.prokaryote.initalize_model.InitalizeModel(knowledge_base, model, options=None)[source]

Bases: wc_model_gen.core.ModelComponentGenerator

Generate compartments

static calc_avg_half_life(kb_species_types)[source]
clean_and_validate_options()[source]

Apply default options and validate options

gen_compartments()[source]
gen_complexes()[source]

Generate complexes in model from knowledge base

gen_distribution_init_concentrations()[source]

Generate concentrations in model from knowledge base

gen_kb_rate_laws()[source]

Generate rate laws for reactions encoded in KB

gen_kb_reactions()[source]

Generate reactions encoded within KB TODO: rxn.submodel attribute is removed from KB, so submodel assignement needs to be taken care of here.

Since all rxns explicitly encoded in KB are metabolic ones, it is manually set to be metablism atm, but not more sophisticated approach

gen_metabolites()[source]

Generate all metabolic species in the cytosol

gen_observables()[source]

Generate observables in model from knowledge base

gen_parameters()[source]
gen_protein()[source]

Generate proteins in model from knowledge base

gen_rna()[source]

Generate RNAs in model from knowledge base

gen_species_type(kb_species_type, extra_compartment_ids=None)[source]

Generate a model species type

Parameters

  • kb_species_type (wc_kb.SpeciesType) – knowledge base species type

  • extra_compartment_ids (list of str, optional) – compartment ids of additional species that should be created beyond those represented in the KB

Returns

model species type

Return type

  • wc_lang.SpeciesType

run()[source]

Generate model components

2.1.1.2.4. wc_model_gen.prokaryote.metabolism module

Generator for metabolism submodels based on KBs for random in silico organisms

Author

Balazs Szigeti <balazs.szigeti@mssm.edu>

Author

Jonathan Karr <karr@mssm.edu>

Author

Ashwin Srinivasan <ashwins@mit.edu>

Author

Arthur Goldberg <Arthur.Goldberg@mssm.edu>

Date

2018-06-11

Copyright

2018, Karr Lab

License

MIT

TODO: improve terminology to better distinguish this and the metabolism species generation

class wc_model_gen.prokaryote.metabolism.MetabolismSubmodelGenerator(knowledge_base, model, options=None)[source]

Bases: wc_model_gen.core.SubmodelGenerator

Generator for metabolism submodel

calc_aa_per_prot()[source]

Calculates the average amino acid content of proteins

calc_aa_transfer_rate()[source]

Calculates the rate of monophosphate transfer from the extracellular space

calc_gtp_corr_rate()[source]

Calculates the extra amount of GTP needed if model has translation subunit

calc_gtp_per_translate()[source]

Calculates the average GTP needed for a translation reaction

calc_h_per_transcript()[source]

Calculates the average H needed for a transcription reaction

calc_h_transfer_rate()[source]

Calculates the rate of H transfer from the extracellular space

calc_mpp_conversion_rate()[source]

Calculates the rate of conversion from mono- to triphosphate molecules

calc_mpp_transfer_rate()[source]

Calculates the rate of monophosphate transfer from the extracellular space

calc_prot_copy_num()[source]

Calculates the # of RNA molecules at t=0

calc_prot_degrad_rxns()[source]

Calculates the expected # of RNA degradation reactions over the CC

calc_rna_copy_num()[source]

Calculates the # of RNA molecules at t=0

calc_rna_degrad_rxns()[source]

Calculates the expected # of RNA degradation reactions over the CC

calc_tpp_per_rna()[source]

Calculates the average triphosphate content of RNAs

gen_rate_laws()[source]

Generate rate laws associated with min metabolism model

:raises ValueError: if any phosphate species are missing from the model: :raises Exception: if there is a reaction with unexpected ID:

gen_reactions()[source]

Generate reactions assocated with min model

:raises ValueError: if any phosphate species are missing from the model:

reaction_scale = 100[source]

2.1.1.2.5. wc_model_gen.prokaryote.protein_degradation module

Generator for protein degradation submodels based on KBs for random in silico organisms

Author

Bilal Shaikh <bilal.shaikh@columbia.edu>

Author

Ashwin Srinivasan <ashwins@mit.edu>

Author

Jonathan Karr <karr@mssm.edu>

Author

Yin Hoon Chew <yinhoon.chew@mssm.edu>

Date

2018-07-05

Copyright

2018, Karr Lab

License

MIT

class wc_model_gen.prokaryote.protein_degradation.ProteinDegradationSubmodelGenerator(knowledge_base, model, options=None)[source]

Bases: wc_model_gen.core.SubmodelGenerator

Generator for protein degradation model

Options: * beta (float, optional): ratio of Michaelis-Menten constant to substrate

concentration (Km/[S]) for use when estimating Km values, the default value is 1

calibrate_submodel()[source]

Calibrate the submodel using data in the KB

clean_and_validate_options()[source]

Apply default options and validate options

gen_rate_laws()[source]

Generate rate laws for the reactions in the submodel

gen_reactions()[source]

Generate reactions associated with submodel

2.1.1.2.6. wc_model_gen.prokaryote.rna_degradation module

Generator for RNA degradation submodels based on KBs for random in silico organisms

Author

Jonathan Karr <karr@mssm.edu>

Author

Ashwin Srinivasan <ashwins@mit.edu>

Author

Yin Hoon Chew <yinhoon.chew@mssm.edu>

Date

2018-06-11

Copyright

2018, Karr Lab

License

MIT

class wc_model_gen.prokaryote.rna_degradation.RnaDegradationSubmodelGenerator(knowledge_base, model, options=None)[source]

Bases: wc_model_gen.core.SubmodelGenerator

Generator for RNA degradation submodel

Options: * beta (float, optional): ratio of Michaelis-Menten constant to substrate

concentration (Km/[S]) for use when estimating Km values, the default value is 1

calibrate_submodel()[source]

Calibrate the submodel using data in the KB

clean_and_validate_options()[source]

Apply default options and validate options

gen_rate_laws()[source]

Generate rate laws for the reactions in the submodel

gen_reactions()[source]

Generate reactions associated with submodel

2.1.1.2.7. wc_model_gen.prokaryote.transcription module

Generator for transcription submodels based on KBs

Author

Jonathan Karr <karr@mssm.edu>

Author

Ashwin Srinivasan <ashwins@mit.edu>

Author

Yin Hoon Chew <yinhoon.chew@mssm.edu>

Date

2018-06-11

Copyright

2018, Karr Lab

License

MIT

class wc_model_gen.prokaryote.transcription.TranscriptionSubmodelGenerator(knowledge_base, model, options=None)[source]

Bases: wc_model_gen.core.SubmodelGenerator

Generator for transcription submodel

Options: * beta (float, optional): ratio of Michaelis-Menten constant to substrate

concentration (Km/[S]) for use when estimating Km values, the default value is 1

calibrate_submodel()[source]

Calibrate the submodel using data in the KB

clean_and_validate_options()[source]

Apply default options and validate options

gen_rate_laws()[source]

Generate rate laws for the reactions in the submodel

gen_reactions()[source]

Generate reactions associated with submodel

2.1.1.2.8. wc_model_gen.prokaryote.translation module

Generating wc_lang formatted models from knowledge base. :Author: Balazs Szigeti <balazs.szigeti@mssm.edu> :Author: Ashwin Srinivasan <ashwins@mit.edu> :Author: Yin Hoon Chew <yinhoon.chew@mssm.edu> :Date: 2018-01-21 :Copyright: 2018, Karr Lab :License: MIT

class wc_model_gen.prokaryote.translation.TranslationSubmodelGenerator(knowledge_base, model, options=None)[source]

Bases: wc_model_gen.core.SubmodelGenerator

Generate translation submodel

Options: * beta (float, optional): ratio of Michaelis-Menten constant to substrate

concentration (Km/[S]) for use when estimating Km values, the default value is 1

calibrate_submodel()[source]

Calibrate the submodel using data in the KB

clean_and_validate_options()[source]

Apply default options and validate options

gen_rate_laws()[source]

Generate rate laws for the reactions in the submodel

gen_reactions()[source]

Generate a lumped reaction that covers initiation, elongation and termination for each protein translated

2.1.1.2.9. Module contents