Markov Models¶

The module myokit.lib.markov contains functions for working with Markov models of ion channel currents.

The class LinearModel can be used to extract a Markov model from a Myokit: model.
By fixing the voltage and all other states that aren’t part of the: Markov model a linear system is created.
Fast simulations can then be performed using the: AnalyticalSimulation class, which is particularly useful when trying to estimate a Markov model’s parameters from a piecewise-linear voltage protocol (e.g. a normal step protocol).
Discrete, stochastic simulations can be performed using the: DiscreteSimulation class.

Analytical and discrete simulation¶

class myokit.lib.markov.LinearModel(model, states, parameters=None, current=None, vm=None)¶

Represents a linear Markov model of an ion channel extracted from a myokit.Model.

The class assumes the markov model can be written as:

dot(x) = A(V,p) * x
I = B(V,p) * x

where V is the membrane potential, p is a set of parameters and A and B are the matrices that relate the state x to its derivative dot(x) and a current I.

A and B can contain non-linear functions, but should be simple scalar matrices when evaluated for a fixed V and p. For example, the current equation I = g * O * (V - E) would have p = [g, E], so that for fixed p and V this would resolve to I = (g * (V - E)) * O, such that g * (V - E) is a constant that can be included in B.

The model variables to treat as parameter are specified by the user when the model is created. Any other variables, for example state variables such as intercellular calcium or constants such as temperature, are fixed when the markov model is created and can no longer be changed. Initial values written as expressions are evaluated when the model is made.

To create a Markov, pass in a myokit.Model and select a list of states. All other states will be fixed at their current value and an attempt will be made to write the remaining state equations as linear combinations of the states. If this is not possible, a ValueError is raised. The membrane potential must be indicated using the label membrane_potential or by passing it in as vm.

The current variable is optional, if no current is specified by the user the relation I = B * x is dropped and no B is calculated.

Example:

import myokit
import myokit.lib.markov as markov

# Load a model from disk
model = myokit.load_model('some-model.mmt')

# Select the relevant states and parameters
states = [
    'ina.C1',
    'ina.C2',
    'ina.O',
    ...
    ]
parameters = [
    'ina.p1',
    'ina.p2',
    ...
    ]
current = 'ina.INa'

# Extract a markov model
mm = markov.LinearModel(model, states, parameters, current)

# Get the matrices A and B such that dot(x) = A * x and I = B * x
# where ``x`` is the state vector and ``I`` is the current.
A, B = mm.matrices(membrane_potential=-40)
print(A)

Alternatively, a LinearModel can be constructed from a single component using the method from_component():

import myokit
import myokit.lib.markov as markov

# Load a model from disk
model = myokit.load_model('some-model.mmt')

# Extract a markov model
mm = markov.LinearModel.from_component(model.get('ina'))

Arguments:

model: The model to work with.
states: An ordered list of state variables (or state variable names) from model. All remaining state variables will be frozen in place. Each state’s derivative must be a linear combination of the other states.
parameters: A list of parameters to maintain in their symbolic form.
current: The markov model’s current variable. The current must be a linear combination of the states (for example g * (V - E) * (O1 + O2)) where O1 and O2 are states. If no current variable is specified None can be used instead.
vm: The variable indicating membrane potential. If set to None (default) the method will search for a variable with the label membrane_potential.

current()¶: Returns the name of the current variable used by this model, or None if no current variable was specified.

default_membrane_potential()¶: Returns this markov model’s default membrane potential value.

default_parameters()¶: Returns this markov model’s default parameter values

default_state()¶: Returns this markov model’s default state values.

static from_component(component, states=None, parameters=None, current=None, vm=None)¶

Creates a Markov model from a component, using the following rules:

Every state in the component is a state in the Markov model

Every unnested constant in the component is a parameter

The component should contain exactly one unnested intermediary variable whose value depends on the model states, this will be used as the current variable.

The model contains a variable labeled “membrane_potential”.

Any of the automatically set variables can be overridden using the keyword arguments states, parameters, current and vm.

The parameters, if determined automatically, will be specified in alphabetical order (using a natural sort).

matrices(membrane_potential=None, parameters=None)¶

For a given value of the membrane_potential and a list of values for the parameters, this method calculates and returns the matrices A and B such that:

dot(x) = A * x
I = B * x

where x is the state vector and I is the current.

Arguments:

membrane_potential: The value to use for the membrane potential, or None to use the value from the original myokit.Model.
parameters: The values to use for the parameters, given in the order they were originally specified in (if the model was created using from_component(), this will be alphabetical order).

membrane_potential()¶: Returns the name of the membrane potential variable used by this model.

parameters()¶: Returns the names of the parameter variables used by this model.

rates(membrane_potential=None, parameters=None)¶

For a given value of the membrane_potential and a list of values for the parameters, this method calculates and returns an ordered list of tuples (i, j, rij) such that rij is a non-zero transition rate from the i-th state to the j-th state.

Arguments:

membrane_potential: The value to use for the membrane potential, or None to use the value from the original myokit.Model.
parameters: The values to use for the parameters, given in the order they were originally specified in (if the model was created using from_component(), this will be alphabetical order).

states()¶: Returns the names of the state variables used by this model.

steady_state(membrane_potential=None, parameters=None)¶

Analytically determines a steady state solution for this Markov model.

membrane_potential: The value to use for the membrane potential, or None to use the value from the original myokit.Model.
parameters: The values to use for the parameters, given in the order they were originally specified in (if the model was created using from_component(), this will be alphabetical order).

class myokit.lib.markov.LinearModelError(message)¶: Raised for issues with constructing or using a LinearModel.

class myokit.lib.markov.AnalyticalSimulation(model, protocol=None)¶

Analytically evaluates a LinearModel’s state over a given set of points in time.

Solutions are calculated for the “law of large numbers” case, i.e. without stochastic behavior. The solution algorithm is based on eigenvalue decomposition.

Each simulation object maintains an internal state consisting of

The current simulation time
The current state
The default state

When a simulation is created, the simulation time is set to zero and both the current and default state are initialized using the LinearModel. After each call to run() the time and current state are updated, so that each successive call to run continues where the previous simulation left off.

A protocol can be used to set the membrane potential during the simulation, or the membrane potential can be adjusted manually between runs.

Example:

import myokit
import myokit.lib.markov as markov

# Create a linear markov model
m = myokit.load_model('clancy-1999.mmt')
m = markov.LinearModel.from_component(m.get('ina'))

# Create an analytical simulation object
s = markov.AnalyticalSimulation(m)

# Run a simulation
s.set_membrane_potential(-30)
d = s.run(10)

# Show the results
import matplotlib.pyplot as plt
plt.figure()
plt.subplot(211)
for state in m.states():
    plt.plot(d.time(), d[state], label=state)
plt.legend(loc='center right')
plt.subplot(212)
plt.plot(d.time(), d[m.current()])
plt.show()

current(state)¶: Calculates the current for a given state.

default_state()¶: Returns the default state used by this simulation.

membrane_potential()¶: Returns the currently set membrane potential.

parameters()¶: Returns the currently set parameter values.

pre(duration)¶

Performs an unlogged simulation for duration time units and uses the final state as the new default state.

After the simulation:

The simulation time is not affected
The current state and the default state are updated to the final state reached in the simulation.

Calls to reset() after using pre() will set the current state to this new default state.

reset()¶

Resets the simulation:

The time variable is set to zero.
The state is set to the default state.

run(duration, log=None, log_interval=0.01, log_times=None)¶

Runs a simulation for duration time units.

After the simulation:

The simulation time will be increased by duration time units.
The simulation state will be updated to the last reached state.

Arguments:

duration: The number of time units to simulate.
log: A log from a previous run can be passed in, in which case the results will be appended to this one.
log_interval: The time between logged points.
log_times: A pre-defined sequence of times to log at. If set, log_interval will be ignored.

Returns a myokit.DataLog with the simulation results.

set_constant(variable, value)¶: Updates a single parameter to a new value.

set_default_state(state)¶: Changes this simulation’s default state.

set_membrane_potential(v)¶: Changes the membrane potential used in this simulation.

set_parameters(parameters)¶: Changes the parameter values used in this simulation.

set_state(state)¶: Changes the initial state used by in this simulation.

solve(times)¶

Evaluates and returns the states at the given times.

In contrast to run(), this method simply evaluates the states (and current) at the given times, using the last known settings for the state and membrane potential. It does not use a protocol and does not take into account the simulation time. After running this method, the state and simulation time are not updated.

Arguments:

times: A series of times, where each time must be some t >= 0.

For models with a current variable, this method returns a tuple (state, current) where state is a matrix of shape (len(states), len(times)) and current is a vector of length len(times).

For models without a current variable, only state is returned.

state()¶: Returns the initial state used by this simulation.

class myokit.lib.markov.DiscreteSimulation(model, protocol=None, nchannels=100)¶

Performs stochastic simulations of a LinearModel’s behavior for a finite number of channels.

Simulations are run using the “Direct method” proposed by Gillespie [1].

Each simulation object maintains an internal state consisting of

The current simulation time
The current state
The default state

A protocol can be used to set the membrane potential during the simulation, or the membrane potential can be adjusted manually between runs.

Example:

import myokit
import myokit.lib.markov as markov

# Create linear markov model
m = myokit.load_model('clancy-1999.mmt')
m = markov.LinearModel.from_component(m.get('ina'))

# Run discrete simulation
s = markov.DiscreteSimulation(m, nchannels=1000)
s.set_membrane_potential(-30)
d = s.run(10)

import matplotlib.pyplot as plt
plt.figure()
for state in m.states():
    plt.step(d.time(), d[state], label=state)
plt.legend()
plt.show()

References

[1] Gillespie (1976) A General Method for Numerically Simulating the: stochastic time evolution of coupled chemical reactions The Journal of Computational Physics, 22, 403-434.

Arguments:

model: A LinearModel.
nchannels: The number of channels to simulate.

default_state()¶: Returns the default simulation state.

discretize_state(x)¶

Converts a list of fractional state occupancies to a list of channel counts.

Arguments:

x: A fractional state where sum(x) == 1.

Returns a discretized state y where sum(y) = nchannels.

membrane_potential()¶: Returns the current membrane potential.

number_of_channels()¶: Returns the number of channels used in this simulation.

parameters()¶: Returns the current parameter values.

pre(duration)¶

Performs an unlogged simulation for duration time units and uses the final state as the new default state.

After the simulation:

The simulation time is not affected
The current state and the default state are updated to the final state reached in the simulation.

Calls to reset() after using pre() will set the current state to this new default state.

reset()¶

Resets the simulation:

The time variable is set to zero.
The state is set to the default state.

run(duration, log=None)¶

Runs a simulation for duration time units.

After the simulation:

The simulation time will be increased by duration time units.
The simulation state will be updated to the last reached state.

Arguments:

duration: The number of time units to simulate.
log: A log from a previous run can be passed in, in which case the results will be appended to this one.

Returns a myokit.DataLog with the simulation results.

set_constant(variable, value)¶: Updates a single parameter to a new value.

set_default_state(state)¶: Changes the default state used in the simulation.

set_membrane_potential(v)¶: Changes the membrane potential used in this simulation.

set_parameters(parameters)¶: Changes the parameter values used in this simulation.

set_state(state)¶: Changes the current state used in the simulation (i.e. the number of channels in every markov model state).

state()¶: Returns the current simulation state.

Finding Markov models¶

myokit.lib.markov.convert_markov_models_to_compact_form(model)¶

Scans a myokit.Model for Markov models, and ensures they contain one state that’s not evaluated as an ODE, but as 1 - sum(x[i]), where the sum is over all other states x[i].

Arguments:

model: The myokit.Model to scan.

Returns an updated myokit.Model.

myokit.lib.markov.convert_markov_models_to_full_ode_form(model)¶

Scans a myokit.Model for Markov models, and ensures they are written in a form where every Markov state is evaluated as an ODE.

Arguments:

model: The myokit.Model to scan.

Returns an updated myokit.Model.

myokit.lib.markov.find_markov_models(model)¶

Searches a myokit.Model for groups of states that constitute a Markov model.

Returns a list of lists, where the inner lists are groups of variables that form a Markov model together.

Note that this method performs a shallow check of the equation shapes, and does not perform any simplification or rewriting to see if the expressions can be made to fit a Markov form.

Arguments:

model: The myokit.Model to search.

Deprecated¶

The following class was used in previous versions of Myokit (before 1.22.0). It now exists only as an interface to the newer classes. The MarkovModel class will be removed in future versions.

class myokit.lib.markov.MarkovModel(model, states, parameters=None, current=None, vm=None)¶

Deprecated: This class has been replaced by the classes LinearModel and AnalyticalSimulation. Please update your code to use these classes instead. This class will be removed in future versions of Myokit.

static from_component(component, states=None, parameters=None, current=None, vm=None)¶: Creates and returns an AnalyticalSimulation using a LinearModel based on a Myokit model component.

Myokit :: API and User Guide

Search

Markov Models¶

Analytical and discrete simulation¶

Finding Markov models¶

Deprecated¶