reconstruction.ecoli.dataclasses.process.two_component_system

Two component systems.

Note: Ligand binding to histidine kinases is modeled by equilibrium.

TODOs: moleculesToNextTimeStep()

Consider relocating (since it’s useful for both the parca and simulation)

class reconstruction.ecoli.dataclasses.process.two_component_system.TwoComponentSystem(raw_data, sim_data)

Bases: object

_buildComplexToMonomer(sim_data, modifiedFormsMonomers, tcsMolecules)

Maps each complex to a dictionary that maps each subunit of the complex to its stoichiometry.

This function also handles correcting compartment tags for subunits where the compartment tag listed in the raw_data.modified_proteins table is inconsistent with the molecule’s tag saved in the bulk molecule data.

_make_dependency_matrix()

Builds matrix mapping linearly independent molecules (ATP, histidine kinases, response regulators, and ligand-bound histidine kinases for positively oriented networks) to their dependents.

_make_derivative()

Creates symbolic representation of the ordinary differential equations and the Jacobian. Used during simulations.

_make_derivative_parca()

Creates symbolic representation of the ordinary differential equations and the Jacobian assuming ATP, ADP, Pi, water and protons are at steady state. Used in the parca.

_make_y_dy()

_populate_derivative_and_jacobian()

Compile callable functions for computing the derivative and the Jacobian.

derivatives(t, y)

Calculate derivatives from stoichiometry and rates with argument order for solve_ivp.

derivatives_jacobian(t, y)

Calculate the jacobian of derivatives from stoichiometry and rates with argument order for solve_ivp.

derivatives_jacobian_jit(t, y)

Calculate the jacobian of derivatives from stoichiometry and rates with argument order for solve_ivp.

derivatives_jit(t, y)

Calculate derivatives from stoichiometry and rates with argument order for solve_ivp.

get_monomers(cplxId)

Returns subunits for a complex (or any ID passed). If the ID passed is already a monomer returns the monomer ID again with a stoichiometric coefficient of one.

get_reaction_name(templateName, systemMolecules)

Returns reaction name for a particular system.

make_modified_molecule_list()

Since the raw modified_proteins table contains proteins that are not in the original molecule pool provided in raw_data.two_component_systems (PHOQ-MONOMER, PHOR-MONOMER, ARCB-MONOMER, and NARX-MONOMER), the list of molecules that the stoich_matrix_monomers() function pulls from needs to be manually expanded to include these proteins.

This function generates a modified molecule list that includes the original molecules from the TCS molecule pool (built in __init__()) and the new proteins from the raw_data.modified_proteins file that were not included in the original list.

mass_balance()

Sum along the columns of the massBalance matrix to check for reaction mass balance

mass_matrix()

Builds stoichiometry mass matrix Rows: molecules Columns: reactions Values: molecular mass

molecules_to_next_time_step(moleculeCounts, cellVolume, nAvogadro, timeStepSec, random_state, method='LSODA', min_time_step=None, jit=True, methods_tried=None)

Calculates the changes in the counts of molecules in the next timestep by solving an initial value ODE problem.

Parameters:

• moleculeCounts (1d ndarray, ints) – current counts of molecules involved in the ODE

• cellVolume (float) – current volume of cell

• nAvogadro (float) – Avogadro’s number

• timeStepSec (float) – current length of timestep in seconds

• random_state (RandomState object) – process random state

• method (str) – name of the ODE method to use

• min_time_step (int) – if not None, timeStepSec will be scaled down until it is below min_time_step if negative counts are encountered

• jit (bool) – if True, use the jit compiled version of derivatives functions

• methods_tried (Optional[Set[str]]) – methods for the solver that have already been tried

Returns:

counts of molecules that need

to be consumed

allMoleculesChanges (1d ndarray, ints): expected changes in

molecule counts after timestep

Return type:

moleculesNeeded (1d ndarray, ints)

molecules_to_ss(moleculeCounts, cellVolume, nAvogadro, timeStepSec=1e+20)

Calculates the changes in the counts of molecules as the system reaches steady state

Parameters:

• moleculeCounts – current counts of molecules involved in the ODE

• cellVolume – current volume of cell

• nAvogadro – Avogadro’s number

• timeStepSec – current length of timestep (set to large number)

Returns:

counts of molecules that need to be consumed allMoleculesChanges: expected changes in molecule counts after timestep

Return type:

moleculesNeeded

stoich_matrix()

Builds stoichiometry matrix Rows: molecules Columns: reactions Values: reaction stoichiometry

stoich_matrix_monomers()

Builds stoichiometry matrix for mapping complexes to their base monomers subunits.

Rows: modified molecules (complexes and monomers, including base monomer subunits that were not included in the original molecule list extracted from raw_data.two_component_systems, but were included in the raw_data.modified_proteins table). Columns: complexes Values: base monomer stoichiometry (not including non-monomer subunits, like metabolites, ATP, etc.)