Source code for pints.toy._stochastic_logistic_model
#
# Stochastic logistic model.
#
# This file is part of PINTS (https://github.com/pints-team/pints/) which is
# released under the BSD 3-clause license. See accompanying LICENSE.md for
# copyright notice and full license details.
#
import numpy as np
from scipy.interpolate import interp1d
import pints
from . import ToyModel
[docs]class StochasticLogisticModel(pints.ForwardModel, ToyModel):
r"""
This model describes the growth of a population of individuals, where the
birth rate per capita, initially :math:`b_0`, decreases to :math:`0` as the
population size, :math:`\mathcal{C}(t)`, starting from an initial
population size, :math:`n_0`, approaches a carrying capacity, :math:`k`.
This process follows a rate according to [1]_
.. math::
A \xrightarrow{b_0(1-\frac{\mathcal{C}(t)}{k})} 2A.
The model is simulated using the Gillespie stochastic simulation algorithm
[2]_, [3]_.
*Extends:* :class:`pints.ForwardModel`, :class:`pints.toy.ToyModel`.
Parameters
----------
initial_molecule_count : float
Sets the initial population size :math:`n_0`.
References
----------
.. [1] Simpson, M. et al. 2019. Process noise distinguishes between
indistinguishable population dynamics. bioRxiv.
https://doi.org/10.1101/533182
.. [2] Gillespie, D. 1976. A General Method for Numerically Simulating the
Stochastic Time Evolution of Coupled Chemical Reactions.
Journal of Computational Physics. 22 (4): 403-434.
https://doi.org/10.1016/0021-9991(76)90041-3
.. [3] Erban R. et al. 2007. A practical guide to stochastic simulations
of reaction-diffusion processes. arXiv.
https://arxiv.org/abs/0704.1908v2
"""
def __init__(self, initial_molecule_count=50):
super(StochasticLogisticModel, self).__init__()
self._n0 = float(initial_molecule_count)
if self._n0 < 0:
raise ValueError('Initial molecule count cannot be negative.')
[docs] def n_parameters(self):
""" See :meth:`pints.ForwardModel.n_parameters()`. """
return 2
def _simulate_raw(self, parameters):
"""
Returns tuple (raw times, population sizes) when reactions occur.
"""
parameters = np.asarray(parameters)
if len(parameters) != self.n_parameters():
raise ValueError('This model should have only 2 parameters.')
b = parameters[0]
k = parameters[1]
if b <= 0:
raise ValueError('Rate constant must be positive.')
# Initial time and count
t = 0
a = self._n0
# Run stochastic logistic birth-only algorithm, calculating time until
# next reaction and increasing population count by 1 at that time
mol_count = [a]
time = [t]
while a < k:
r = np.random.uniform(0, 1)
t += np.log(1 / r) / (a * b * (1 - a / k))
a = a + 1
time.append(t)
mol_count.append(a)
return time, mol_count
def _interpolate_values(self, time, pop_size, output_times, parameters):
"""
Takes raw times and population size values as inputs and outputs
interpolated values at output_times.
"""
# Interpolate as step function, increasing pop_size by 1 at each
# event time point
interp_func = interp1d(time, pop_size, kind='previous')
# Compute population size values at given time points using f1
# at any time beyond the last event, pop_size = k
values = interp_func(output_times[np.where(output_times <= time[-1])])
zero_vector = np.full(
len(output_times[np.where(output_times > time[-1])]),
parameters[1])
values = np.concatenate((values, zero_vector))
return values
[docs] def simulate(self, parameters, times):
""" See :meth:`pints.ForwardModel.simulate()`. """
times = np.asarray(times)
if np.any(times < 0):
raise ValueError('Negative times are not allowed.')
if self._n0 == 0:
return np.zeros(times.shape)
# run Gillespie
time, pop_size = self._simulate_raw(parameters)
# interpolate
values = self._interpolate_values(time, pop_size, times, parameters)
return values
[docs] def mean(self, parameters, times):
r"""
Computes the deterministic mean of infinitely many stochastic
simulations with times :math:`t` and parameters (:math:`b`, :math:`k`),
which follows:
:math:`\frac{kC(0)}{C(0) + (k - C(0)) \exp(-bt)}`.
Returns an array with the same length as `times`.
"""
parameters = np.asarray(parameters)
if len(parameters) != self.n_parameters():
raise ValueError('This model should have only 2 parameters.')
b = parameters[0]
if b <= 0:
raise ValueError('Rate constant must be positive.')
k = parameters[1]
if k <= 0:
raise ValueError("Carrying capacity must be positive")
times = np.asarray(times)
if np.any(times < 0):
raise ValueError('Negative times are not allowed.')
c0 = self._n0
return (c0 * k) / (c0 + np.exp(-b * times) * (k - c0))
[docs] def variance(self, parameters, times):
r"""
Returns the deterministic variance of infinitely many stochastic
simulations.
"""
raise NotImplementedError
[docs] def suggested_parameters(self):
""" See :meth:`pints.toy.ToyModel.suggested_parameters()`. """
return np.array([0.1, 500])
[docs] def suggested_times(self):
""" See :meth:`pints.toy.ToyModel.suggested_times()`."""
return np.linspace(0, 100, 101)