biocrnpyler.core.ProportionalHillNegative
- class biocrnpyler.core.ProportionalHillNegative(k: float, s1: Species, K: float, n: float, d: Species)[source]
Bases:
HillNegativeProportional negative Hill function propensity.
Implements a repressive Hill function with rate proportional to a species concentration. Commonly used for regulated production where a repressor inhibits production from a template/enzyme.
- Parameters:
k (
floatorParameterEntry) – Maximum rate constant per unit of d (when s1=0). Must be positive.s1 (
Species) – Input species that represses the reaction (e.g., repressor).K (
floatorParameterEntry) – Half-saturation constant for repression by s1. Must be positive.n (
floatorParameterEntry) – Hill coefficient. Must be positive.d (
Species) – Proportional species (e.g., DNA template, enzyme). Rate scales linearly with this species concentration.
- Attributes:
name (
str) – Set to ‘proportionalhillnegative’ for this propensity type.
See also
HillNegativeNon-proportional negative Hill.
ProportionalHillPositiveProportional positive Hill.
Notes
The following mathematical formula: is used:
\[p(s_1, d; k, K, n) = \frac{k d}{1 + (s_1/K)^n} \]This is commonly used for repressed transcription where
d = DNA template concentration
s1 = repressor concentration
Rate is proportional to template but repressed by s1
and resulting in the following behaviors:
When d = 0: rate = 0 (no template/enzyme)
When s1 = 0: rate = k*d (fully derepressed)
When s1 \(\gg\) K: rate \(\rightarrow\) 0 (fully repressed)
Examples
Model repressed transcription:
>>> repressor = bcp.Species('repressor') >>> DNA = bcp.Species('DNA') >>> prop = bcp.ProportionalHillNegative( ... k=0.1, s1=repressor, K=50.0, n=2.0, d=DNA)
Model enzyme with allosteric inhibitor:
>>> inhibitor = bcp.Species('inhibitor') >>> enzyme = bcp.Species('enzyme') >>> kcat = bcp.ParameterEntry('kcat', 10.0) >>> Ki = bcp.ParameterEntry('Ki', 100.0) >>> prop = bcp.ProportionalHillNegative( ... k=kcat, s1=inhibitor, K=Ki, n=2.0, d=enzyme)
Methods
Create SBML kinetic law for Hill propensity.
Create a propensity from a dictionary.
Get all available propensity subclasses.
Check if an object is a valid Propensity subclass instance.
Generate human-readable string representation of propensity.
Generate formatted string of all propensity parameters.
Generate human-readable rate formula string.
- property K
Half-saturation (dissociation) constant value.
- Type:
float
- __eq__(other)[source]
Test equality between propensities.
- Parameters:
other (
Propensity) – Other propensity to compare with.- Returns:
True if propensities have the same class and propensity_dict.
- Return type:
bool
- create_kinetic_law(model, sbml_reaction, stochastic, **kwargs)[source]
Create SBML kinetic law for Hill propensity.
This method is shared by all Hill subclasses.
- Parameters:
model (
libsbml.Model) – SBML model object.sbml_reaction (
libsbml.Reaction) – SBML reaction to add kinetic law to.stochastic (
bool) – If True, uses stochastic formulation (same as deterministic for Hill functions).**kwargs – Additional arguments. ‘reverse_reaction’ is not supported.
- Returns:
Created SBML kinetic law object.
- Return type:
libsbml.KineticLaw- Raises:
ValueError – If reverse_reaction=True (Hill propensities cannot be reversible) or if rate formula is invalid.
- classmethod from_dict(propensity_dict)[source]
Create a propensity from a dictionary.
- Parameters:
propensity_dict (
dict) – Dictionary with ‘parameters’ and ‘species’ keys containing parameter and species values.- Returns:
New instance of the propensity class.
- Return type:
- static get_available_propensities() Set[source]
Get all available propensity subclasses.
- Returns:
Set of all Propensity subclass types available in BioCRNpyler.
- Return type:
set
Examples
>>> propensities = bcp.Propensity.get_available_propensities() >>> bcp.MassAction in propensities True
- property is_reversible
Whether the propensity represents a reversible reaction.
Default is False. Subclasses override this for reversible kinetics.
- Type:
bool
- static is_valid_propensity(propensity_type) bool[source]
Check if an object is a valid Propensity subclass instance.
Recursively checks all subclasses of Propensity to determine if the given object is a valid propensity type.
- Parameters:
propensity_type (
object) – Object to check for Propensity validity.- Returns:
True if
propensity_typeis an instance of Propensity or any of its subclasses, False otherwise.- Return type:
bool
Examples
>>> prop = bcp.MassAction(k_forward=100.0) >>> bcp.Propensity.is_valid_propensity(prop) True >>> bcp.Propensity.is_valid_propensity("not a propensity") False
- property k
Maximum rate constant value.
- Type:
float
- property k_forward
Forward rate constant.
- Raises:
NotImplementedError – Must be implemented by subclasses that use rate constants.
- Type:
Float
- property k_reverse
Reverse rate constant for reversible reactions.
- Raises:
NotImplementedError – Must be implemented by subclasses that use rate constants.
- Type:
Float or None
- property n
Hill coefficient (cooperativity) value.
- Type:
float
- pretty_print(show_parameters=True, **kwargs)[source]
Generate human-readable string representation of propensity.
- Parameters:
show_parameters (
bool, defaultTrue) – If True, includes parameter values in output.**kwargs – Additional keyword arguments passed to formatting methods.
- Returns:
Formatted string showing rate formula and optionally parameters.
- Return type:
str
- pretty_print_parameters(show_keys=True, **kwargs)[source]
Generate formatted string of all propensity parameters.
- Parameters:
show_keys (
bool, defaultTrue) – If True, shows search and found keys for ModelParameter objects (useful for debugging parameter lookup).**kwargs – Additional formatting keyword arguments.
- Returns:
Formatted string listing all parameters and their values.
- Return type:
str