Description Usage Arguments Details Value See Also Examples
These functions simulate a biochemical reacton system
with spatial effects parameterized as a Petri Net.
sGillespieOptimDirect
and sGillespieDirectCR
performs pure
stochastic simulations, sRungeKuttaDormandPrince45
a pure
deterministic integration, sHaseltineRawlings
a hybrid of the
above. Multiple runs can be performed at once.
See init
for a way of defining the model that is close
to the way reactions are written.
1 2 3 4 5 6 7 8 9 | ## Exact stochastic simulation:
sGillespieOptimDirect(model, timep, delta=1, runs=1)
sGillespieDirectCR(model, timep, delta=1, runs=1)
## Pure deterministic:
sRungeKuttaDormandPrince45(model, timep, delta=1, ect = 1e-09)
## Hybrid stochastic/deterministic:
sHaseltineRawlings(model, timep, delta=1, runs=1, ect = 1e-09)
|
model |
list containing named elements: |
timep |
It can be either a numeric, indicating for how long (in the same time units as the propensity constants) the process will run, or a functions (R or C), in which case can be used to change the protocol at time intervals. See details. |
delta |
Interval time at which the state will be saved. |
runs |
How many runs will be performed. |
ect |
Precision for the fast reactions. |
model is a list containing the following elements:
model$pre: pre matrix, with as many rows as transitions (reactions), and columns as places (reactants). It has the stoichiometrics of the left sides of the reactions.
model$post: post matrix, with as many rows as transitions, and columns as places (products). It has the stoichiometrics of the right sides of the reactions.
model$h: list of propensity constants or functions returning the propensity (with as many elements as transitions).
model$slow: vector of zeros for slow transitions and ones
for fast transitions. Only needed for
HaseltineRawlings
. Ignored otherwise.
model$M: initial marking (state) of the system.
model$place: vector with names of the places.
model$transition: vector with names of the transitions.
The functions return a list with the following elements:
place |
vector with the names of the places if supplied. If not, the function creates names as follows: P1, P2, ... |
transition |
vector with the names of the transitions if supplied. If not, the function creates names as follows: T1, T2, ... |
dt |
vector containing the discretized times at which the state is saved (according to delta) |
run |
list with as many elements as runs. We will describe the first element, run[[1]], as the rest have exactly the same structure. It is also a list, with the following elements: |
run[[1]]$M |
list with as many elements as places, each of them containing the state of the system sampled according to delta. |
run[[1]]$transitions |
vector with as many elements as transitions, with the total of time each slow reaction fired. |
run[[1]]$tot.transitions |
numeric with the summ of run[[1]]$transitions. |
1 2 | ## sbioPN has been tested only on 64 bits machines.
## It may fail in 32 bits architecture.
|
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