In this vignette, we describe details of **MGDrivE2** for advanced users, including details of the internal **MGDrivE2** simulation API. It is highly recommended to familiarize yourself with the content in the other vignettes before reading this.

The internal **MGDrivE2** code relies on “step functions” (not to be confused with the mathematical function \(\Theta(x)\)) which are responsible for taking an input state (marking, \(X_{0}\)) and updating over some time step \(\Delta t\) from \(t_{0}\) to \(t_{0}+\Delta t\). Internally, the wrapper function `sim_trajectory_R()`

calls `sim_trajectory_base_R()`

, which is an adapter for any valid step function. `sim_trajectory_base_R()`

is responsible for recording output at requested times by the user, as well as firing release events, and anything else exogenous to the internal dynamics of the system. The step function then, describes how to sample a trajectory from a valid Petri Net model. This flexibility, gained by decoupling the conceptual model, expressed as a Petri Net, from the numerical methods sampling trajectories, allows a variety of deterministic and stochastic methods to be used interchangeably in **MGDrivE2**.

It is our hope that future development and users will be interested in improving these numerical methods. Therefore, we provide a detailed example showing how to write a new step function. Because we are interested in describing how to interface with the internal **MGDrivE2** API, rather than describing a specific method, we use use a simple Euler method for our example.

The function (factory) is below. It takes two arguments as input. `pn`

is a named list with two elements, the stoichiometry matrix `S`

and the “hazard” vector `h`

, needed to update the state over the time step. Hazard is quoted because, when interpreting the model as describing a set of ODEs, these should be referred to as deterministic rate functions, rather than hazard functions, which have a specific stochastic interpretation. However, we will refer to these as hazard functions with apologies to the pedantic.

All of the work is done in the returned function object. `x`

is the state vector which will be updated and returned. `termt`

is the right endpoint of the time step, at \(t_{0}+\Delta t\). Until we reach the termination point, the state is updated as a standard Euler scheme, with each internal step of size `dt`

. When the step function returns, note that it returns a named list with the first element `x`

, and second named element `NULL`

. That is for compatibility with the internal simulation functions which allow users to track additional output besides the state vector.

```
function(pn, dt = 0.01) {
step_Euler <-
stopifnot(all(names(pn) %in% c("S","h")))
return(
function(x0, t0, deltat){
x0
x = t0
tNow = t0 + deltat
termt =repeat {
pn$h(x, tNow)
h =if(any(h > 1e6)){
stop("rates too large, terminating simulation.\n\ttry reducing dt")
} pn$S %*% (h*dt)
dx = x + as.vector(dx)
x =<0] <- 0 # "absorption" at 0
x[x tNow+dt
tNow =if(tNow > termt){
return(list("x"=x,"o"=NULL))
}
}
}
) }
```

The function `step_Euler()`

implements this first-order explicit Euler scheme just described. All step functions are required to take the three named arguments `x0`

, `t0`

, and `deltat`

, giving the state at the beginning of the time step, the initial time, and the size of the time step, and must return the updated state vector when that time step is over. To test this function, we will setup a simple one-node, lifecycle simulation, given in the “MGDrivE2: One Node Lifecycle Dynamics” vignette.

Because this vignette covers advanced topics it assumes familiarity with **MGDrivE2** and the setup for the one node simulation is given with few comments.

We start by loading the **MGDrivE2** package, as well as the **MGDrivE** package for access to inheritance cubes and **ggplot2** for graphical analysis. We will use the basic cube to simulate Mendelian inheritance for this example.

```
# simulation functions
library(MGDrivE2)
#> Loading MGDrivE2: Mosquito Gene Drive Explorer Version 2
# inheritance patterns
library(MGDrivE)
#> Loading MGDrivE: Mosquito Gene Drive Explorer
# plotting
library(ggplot2)
# basic inheritance pattern
MGDrivE::cubeMendelian() cube <-
```

These are the same parameters as in the “MGDrivE2: One Node Lifecycle Dynamics” vignette.

```
# adule female mosquitoes
500
NF <-
# lifecycle parameters
list(
theta <-qE = 1/4,
nE = 2,
qL = 1/3,
nL = 3,
qP = 1/6,
nP = 2,
muE = 0.05,
muL = 0.15,
muP = 0.05,
muF = 0.09,
muM = 0.09,
beta = 16,
nu = 1/(4/24)
)
# simulation parameters
75
tmax <- 1 dt <-
```

```
# Places and transitions
spn_P_lifecycle_node(params = theta, cube = cube)
SPN_P <- spn_T_lifecycle_node(spn_P = SPN_P, params = theta, cube = cube)
SPN_T <-
# Stoichiometry matrix
spn_S(spn_P = SPN_P, spn_T = SPN_T) S <-
```

```
# lifecycle equilibrium and initial conditions
equilibrium_lifeycle(params = theta, NF = NF, spn_P=SPN_P, cube = cube)
init <-
# approximate hazards for continous approximation
spn_hazards(spn_P = SPN_P, spn_T = SPN_T, cube = cube,
approx_hazards <-params = init$params, exact = FALSE, tol = 1e-8,
verbose = FALSE)
```

We will use a release scheme similar to the one-node vignette for both of our simulations, but with only 3 overall releases.

```
# releases
seq(from = 15, length.out = 3, by = 10)
r_times <- 50
r_size <- data.frame("var" = paste0("F_", cube$releaseType, "_", cube$wildType),
events <-"time" = r_times,
"value" = r_size,
"method" = "add",
stringsAsFactors = FALSE)
```

At this point the simulation is almost ready. We will use the internal **MGDrivE2** API to run our custom Euler step function. We will write a function `evaluate_haz()`

that evaluates all of the hazard functions at the given time and state using `vapply`

for speed. Because we are providing a non-standard step function, we have to call the base setup functions from the package, instead of the nice `sim_trajectory_R()`

wrapper.

```
# function to evaluate
function(M,t){vapply(X = approx_hazards$hazards,
evaluate_haz <-FUN = function(h){h(t=t, M=M)},
FUN.VALUE = numeric(1), USE.NAMES = FALSE) }
# step function for hazard evaluation
step_Euler(pn = list(S=S, h=evaluate_haz), dt = 0.1)
Euler_stepper <-
# checks for simulation time and events
MGDrivE2:::base_time(t0 = 0, tt = tmax, dt = dt)
sim_times <- MGDrivE2:::base_events(x0 = init$M0, events = events, dt = dt)
events <-
# fum simulation
MGDrivE2:::sim_trajectory_base_R(x0 = init$M0, times = sim_times,
euler_out <-dt = dt, num_reps = 1,
stepFun = Euler_stepper,
events = events, verbose = FALSE)
# summarize female/male
summarize_females(out = euler_out$state,spn_P = SPN_P)
euler_female_out <- summarize_males(out = euler_out$state)
euler_male_out <- rbind(cbind(euler_female_out,"sex" = "F"),
euler_fm_out <-cbind(euler_male_out, "sex" = "M"))
```

For comparison, we use the default ODE method in **MGDrivE2**. These are numerical integration routines provided by the `deSolve`

package.

```
# run deterministic simulation
sim_trajectory_R(x0 = init$M0, t0 = 0, tt = tmax, dt = dt, S = S,
ODE_out <-hazards = approx_hazards, sampler = "ode",
events = events, verbose = FALSE)
# summarize females/males
summarize_females(out = ODE_out$state, spn_P = SPN_P)
ODE_female_out <- summarize_males(out = ODE_out$state)
ODE_male_out <- rbind(cbind(ODE_female_out,"sex" = "F"),
ODE_fm_out <-cbind(ODE_male_out, "sex" = "M"))
```

```
# add method for plotting
$method <- "euler"
euler_fm_out$method <- "deSolve"
ODE_fm_out
# plot adults
ggplot(data = rbind(euler_fm_out, ODE_fm_out)) +
geom_line(aes(x = time, y = value, color = genotype, linetype = method),
alpha=0.75) +
facet_wrap(facets = vars(sex), scales = "fixed") +
theme_bw() +
ggtitle("SPN: ODE Solution")
```

Because this is a relatively “easy” problem for numerical routines, the lines are more or less exactly on top of each other. This is not going to be true in general, especially for time varying parameters or stiff systems.

- Tau-leaping is an approximate method, before choosing a time step (tau) to use, a good idea is to run a simple one node simulation using the ODE sampler and a large population so the stochastic and deterministic solutions are roughly equal. Then check to see what size of tau gives solutions that converge to about the same answer from the ODEs. It might be smaller than you think.