Crate sosa 

The stochastic simulation algorithm (SSA) with a Monte-Carlo generating method.

Example

sosa allows using the SSA with agents that carry some individual proprieties evolving over time.

Consider for example human cells which reproduce asexually and are thought to acquire new point mutations in their genome upon cell division. We could be interested in tracking the evolution in the number of mutations over time, as cells reproduce. Moreover, cells can reproduce at different rates on average, e.g. cells carrying special mutations can reproduce faster compared to other cells. In this case, we can use sosa to perform SSA and at the same time track those mutations over time taking into account the different proliferation rates.

Note that if we are just interested in tracking the number of individuals over time, without taking into consideration the indiviual proprities of the agents, then rebop should be used instead of sosa.

Structs

CurrentState

The current state of a Markov process.

IterTime

NextReaction

The next reaction sampled by the SSA.

Options

ReactionRates

The rate of a reaction is the average number of occurrence of that reaction in a time-unit.

Enums

SimState

Whether to stop or continue the simulation.

StopReason

Traits

AdvanceStep

Perform an iteration of the SSA.

Functions

exprand

Generates a random waiting time using the exponential waiting time with parameter lambda of Poisson StochasticProcess.

simulate

The main loop running one realisation of a stochastic process with NB_REACTIONS possible REACTIONs.

write2file

Write vector of float into new file with a precision of 4 decimals. Write NAN if the slice to write to file is empty.

Type Aliases

NbIndividuals

Number of individuals present in the system.