MGDrivE: Mosquito Gene Drive Explorer

Description

MGDrivE: Mosquito Gene Drive Explorer

Introduction

Recent developments of CRISPR-Cas9 based homing endonuclease gene drive systems, for the suppression or replacement of mosquito populations, have generated much interest in their use for control of mosquito-borne diseases (such as dengue, malaria, Chikungunya and Zika). This is because genetic control of pathogen transmission may complement or even substitute traditional vector-control interventions, which have had limited success in bringing the spread of these diseases to a halt. Despite excitement for the use of gene drives for mosquito control, current modeling efforts have analyzed only a handful of these new approaches (usually studying just one per framework). Moreover, these models usually consider well-mixed populations with no explicit spatial dynamics. To this end, we are developing MGDrivE (Mosquito Gene DRIVe Explorer), in cooperation with the 'UCI Malaria Elimination Initiative', as a flexible modeling framework to evaluate a variety of drive systems in spatial networks of mosquito populations. This framework provides a reliable testbed to evaluate and optimize the efficacy of gene drive mosquito releases. What separates MGDrivE from other models is the incorporation of mathematical and computational mechanisms to simulate a wide array of inheritance-based technologies within the same, coherent set of equations. We do this by treating the population dynamics, genetic inheritance operations, and migration between habitats as separate processes coupled together through the use of mathematical tensor operations. This way we can conveniently swap inheritance patterns whilst still making use of the same set of population dynamics equations. This is a crucial advantage of our system, as it allows other research groups to test their ideas without developing new models and without the need to spend time adapting other frameworks to suit their needs.

Brief Description

MGDrivE is based on the idea that we can decouple the genotype inheritance process from the population dynamics equations. This allows the system to be treated and developed in three semi-independent modules that come together to form the system. The way this is done will be described later in this document but a reference diagram is shown here.

Previous Work

The original version of this model was based on work by (Deredec et al. 2011; Hancock and Godfray 2007) and adapted to accommodate CRISPR homing dynamics in a previous publication by our team (Marshall et al. 2017). As it was described, we extended this framework to be able to handle a variable number of genotypes, and migration across spatial scenarios. We accomplish this by adapting the equations to work in a tensor-oriented manner, where each genotype can have different processes affecting their particular strain (death rates, mating fitness, sex-ratio bias, et cetera).

Notation and Conventions

Before beginning the full description of the model we will define some of the conventions we followed for the notation of the written description of the system.

• Overlines are used to denote the dimension of a tensor.

• Subscript brackets are used to indicate an element in time. For example: L_{[t-1]} is the larval population at time: t-1.

• Parentheses are used to indicate the parameter(s) of a function. For example: \overline{O(T_{e}+T_{l})} represents the function O evaluated with the parameter: T_{e}+T_{l}

• Matrices follow a 'row-first' indexing order (i: row, j: column)

In the case of one dimensional tensors, each slot represents a genotype of the population. For example, the male population is stored in the following way:

\overline{Am} = \left(\begin{array}{c} g_1 \\ g_2 \\ g_3 \\ \vdots \\ g_n \end{array}\right) _{i}

All the processes that affect mosquitoes in a genotype-specific way are defined and stored in this way within the framework.

There are two tensors of squared dimensionality in the model: the adult females matrix, and the genotype-specific male-mating ability (\overline{\eta}) In the case of the former the rows represent the females' genotype, whilst the columns represent the genotype of the male they mated with:

\overline{\overline{Af}} = \left(\begin{array}{ccccc} g_{11} & g_{12} & g_{13} & \cdots & g_{1n}\\ g_{21} & g_{22} & g_{23} & \cdots & g_{2n}\\ g_{31} & g_{32} & g_{33} & \cdots & g_{3n}\\ \vdots & \vdots & \vdots & \ddots & \vdots\\ g_{n1} & g_{n2} & g_{n3} & \cdots & g_{nn} \end{array}\right) _{ij}

The genotype-specific male mating ability, on the other hand, stores the females' genotype in the rows, and the male genotypes in the columns of the matrix.

References

Deredec A, Godfray HCJ, Burt A (2011). “Requirements for effective malaria control with homing endonuclease genes.” Proceedings of the National Academy of Sciences of the United States of America, 108(43), E874–80. ISSN 1091-6490, doi:10.1073/pnas.1110717108, https://www.pnas.org/content/108/43/E874.

Hancock PA, Godfray HCJ (2007). “Application of the lumped age-class technique to studying the dynamics of malaria-mosquito-human interactions.” Malaria journal, 6, 98. ISSN 1475-2875, doi:10.1186/1475-2875-6-98, https://malariajournal.biomedcentral.com/articles/10.1186/1475-2875-6-98.

Marshall J, Buchman A, C. HMS, Akbari OS (2017). “Overcoming evolved resistance to population-suppressing homing-based gene drives.” Nature Scientific Reports, 1–46. ISSN 2045-2322, doi:10.1038/s41598-017-02744-7, https://www.nature.com/articles/s41598-017-02744-7.

MGDrivE: Inheritance Cube

Description

To model an arbitrary number of genotypes efficiently in the same mathematical framework, we use a 3-dimensional array structure (cube) where each axis represents the following information:

• x: female adult mate genotype

• y: male adult mate genotype

• z: proportion of the offspring that inherits a given genotype (layer)

Details

The cube structure gives us the flexibility to apply tensor operations to the elements within our equations, so that we can calculate the stratified population dynamics rapidly; and within a readable, flexible computational framework. This becomes apparent when we define the equation we use for the computation of eggs laid at any given point in time:

\overline{O(T_x)} = \sum_{j=1}^{n} \Bigg( \bigg( (\beta*\overline{s} * \overline{ \overline{Af_{[t-T_x]}}}) * \overline{\overline{\overline{Ih}}} \bigg) * \Lambda \Bigg)^{\top}_{ij}

In this equation, the matrix containing the number of mated adult females (\overline{\overline{Af}}) is multiplied element-wise with each one of the layers containing the eggs genotypes proportions expected from this cross (\overline{\overline{\overline{Ih}}}). The resulting matrix is then multiplied by a binary 'viability mask' (\Lambda) that filters out female-parent to offspring genetic combinations that are not viable due to biological impediments (such as cytoplasmic incompatibility). The summation of the transposed resulting matrix returns us the total fraction of eggs resulting from all the male to female genotype crosses (\overline{O(T_x)}).

Note: For inheritance operations to be consistent within the framework the summation of each element in the z-axis (this is, the proportions of each one of the offspring's genotypes) must be equal to one.

Drive-specific Cubes

An inheritance cube in an array object that specifies inheritance probabilities (offspring genotype probability) stratified by male and female parent genotypes. MGDrivE provides the following cubes to model different gene drive systems:

• cubeOneLocusTA: 1 Locus Maternal-Toxin/Zygotic-Antidote System

• cubeTwoLocusTA: 2 Locus Maternal-Toxin/Zygotic-Antidote System

• cubeAlleleSail: 3 Locus Allele Sail, similar to Oberhofer et. al.

• cubeASmidler: Split drive design with significant parent-specific impacts

• cubeCLEAVRMF: Cleave and Rescue

• cubeCLEAVRX: Cleave and Rescue, X-Linked

• cubeClvR: 1 Locus Cleave and Rescue (ClvR)

• cubeClvR2: 2 Locus Cleave and Rescue (ClvR)

• cubeConfinableHoming: Confinable Homing

• cubeConfinableHomingX: Confinable Homing, X-Linked

• cubeHoming1RA: Homing Drive with 1 Resistance Allele

• cubeHomingDrive: CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) with 2 Resistance Allele

• cubeHomingDriveSM: CRISPR with Small-Molecule Induction

• cubeXHomingDeposition: CRISPR, X-Linked

• cubeECHACR: 2 Locus Eraser/Chaser Construct, Autosomal

• cubeECHACRX: 2 Locus Eraser/Chaser Construct, X-Linked

• cubeImmunizingReversalMF: Immunizing Reversal/Basic Reversal

• cubeImmunizingReversalX: Immunizing Reversal

• cubeConfinableHomingJOHN: Original Cube Design (We DO NOT recommend using this)

• cubeKillerRescue: Killer-Rescue System

• cubeMEDEA: MEDEA (Maternal Effect Dominant Embryonic Arrest)

• cubeReciprocalTranslocations: Reciprocal Translocation

• cubeRIDL: RIDL (Release of Insects with Dominant Lethality)

• cubeXShredderMF: X-Shredder, Autosomal

• cubeXShredderY: X-Shredder, Y-Linked

• cubeMendelian: Mendelian

• cubeSplitDrive: Split CRISPR Drive

• cubeSplitDriveX: Split CRISTPR Drive, X-Linked

• cubeSplitDriveY: Split CRISTPR Drive, Y-Linked

• cubeTGD: trans-complementing Gene Drive

• cubeTGDX: trans-complementing Gene Drive, X-Linked

• cubeWolbachia: Wolbachia

Functions for Cubes

We provide one auxiliary function to operate on cube objects.

• cube2csv: Export slices of a cube to .csv format

MGDrivE: Model's Mathematical Description

Description

The original version of this model was based on work by (Deredec et al. 2011; Hancock and Godfray 2007) and adapted to accommodate CRISPR homing dynamics in a previous publication by our team (Marshall et al. 2017). As it was described, we extended this framework to be able to handle a variable number of genotypes, and migration across spatial scenarios. We did this by adapting the equations to work in a tensor-oriented manner, where each genotype can have different processes affecting their particular strain (death rates, mating fitness, sex-ratio bias, et cetera).

Inheritance Cube and Oviposition

To allow the extension of the framework to an arbitrary number of genotypes, we transformed traditional inheritance matrices into inheritance cubes, where each of the axis represents the following information:

• x: female adult mate genotype

• y: male adult mate genotype

• z: proportion of the offspring that inherits a given genotype (slice)

The 'cube' structure gives us the flexibility to apply tensor operations to the elements within our equations, so that we can calculate the stratified population dynamics rapidly; and within a readable, flexible computational framework. This becomes apparent when we define the equation we use for the computation of eggs laid at any given point in time:

\overline{O(T_x)} = \sum_{j=1}^{n} \Bigg( \bigg( (\beta*\overline{s} * \overline{ \overline{Af_{[t-T_x]}}}) * \overline{\overline{\overline{Ih}}} \bigg) * \Lambda \Bigg)^{\top}_{ij}

In this equation, the matrix containing the number of mated adult females (\overline{\overline{Af}}) is multiplied element-wise with each one of the slices containing the eggs genotypes proportions expected from this cross (\overline{\overline{\overline{Ih}}}). The resulting matrix is then multiplied by a binary 'viability mask' (\Lambda) that filters out female-parent to offspring genetic combinations that are not viable due to biological impediments (such as cytoplasmic incompatibility). The summation of the transposed resulting matrix returns us the total fraction of eggs resulting from all the male to female genotype crosses (\overline{O(T_{x})}).

Note: For inheritance operations to be consistent within the framework, the summation of each element in the 'z' axis (this is, the proportions of each one of the offspring's genotypes) must be equal to one.

Population Dynamics

During the three aquatic stages, a density-independent mortality process takes place:

\theta_{st}=(1-\mu_{st})^{T_{st}}

Along with a density dependent process dependent on the number of larvae in the environment:

F(L[t])=\Bigg(\frac{\alpha}{\alpha+\sum{\overline{L[t]}}}\Bigg)^{1/T_l}

where \alpha represents the strength of the density-dependent process. This parameter is calculated with:

\alpha=\Bigg( \frac{1/2 * \beta * \theta_e * Ad_{eq}}{R_m-1} \Bigg) * \Bigg( \frac{1-(\theta_l / R_m)}{1-(\theta_l / R_m)^{1/T_l}} \Bigg)

in which \beta is the species' fertility in the absence of gene-drives, Ad_{eq} is the adult mosquito population equilibrium size, and R_{m} is the population growth in the absence of density-dependent mortality. This population growth is calculated with the average generation time (g), the adult mortality rate (\mu_{ad}), and the daily population growth rate (r_{m}):

g=T_{e}+T_{l}+T_{p}+\frac{1}{\mu_{ad}}\\R_{m}=(r_{m})^{g}

Larval Stages

The computation of the larval stage in the population is crucial to the model because the density dependent processes necessary for equilibrium trajectories to be calculated occur here. This calculation is performed with the following equation:

D(\theta_l,T_x) = \left\{ \begin{array}{ll} \theta_{l[0]}^{'}=\theta_l & \quad i = 0 \\ \theta_{l[i+1]}^{'} = \theta_{l[i]}^{'} *F(\overline{L_{[t-i-T_x]}}) & \quad i \leq T_l \end{array} \right.

In addition to this, we need the larval mortality (\mu_{l}):

%L_{eq}=&\alpha*\lfloor R_{m} -1\rfloor %& \mu_{l}=1-\Bigg( \frac{R_{m} * \mu_{ad}}{1/2 * \beta * (1-\mu_{m})} \Bigg)^{\frac{1}{T_{e}+T_{l}+T_{p}}}

With these mortality processes, we are now able to calculate the larval population:

\overline{L_{[t]}}= \overline{L_{[t-1]}} * (1-\mu_{l}) * F(\overline{L_{[t-1]})}\\ +\overline{O(T_{e})}* \theta_{e} \\ %+\overline{\beta}* \theta_{e} * (\overline{\overline{Af_{(t-T_{e})}}} \circ \overline{\overline{\overline{Ih}}})\\ - \overline{O(T_{e}+T_{l})} * \theta_{e} * D(\theta_{l},0) %\prod_{i=1}^{T_{l}} F(\overline{L_{[t-i]}}) %\theta_{l}

where the first term accounts for larvae surviving one day to the other; the second term accounts for the eggs that have hatched within the same period of time; and the last term computes the number of larvae that have transformed into pupae.

Adult Stages

We are ultimately interested in calculating how many adults of each genotype exist at any given point in time. For this, we first calculate the number of eggs that are laid and survive to the adult stages with the equation:

\overline{E^{'}}= \overline{O(T_{e}+T_{l}+T_{p})} \\ * \bigg(\overline{\xi_{m}} * (\theta_{e} * \theta_{p}) * (1-\mu_{ad}) * D(\theta_{l},T_{p}) \bigg)

With this information we can calculate the current number of male adults in the population by computing the following equation:

\overline{Am_{[t]}}= \overline{Am_{[t-1]}} * (1-\mu_{ad})*\overline{\omega_{m}}\\ + (1-\overline{\phi}) * \overline{E^{'}}\\ + \overline{\nu m_{[t-1]}}

in which the first term represents the number of males surviving from one day to the next; the second one, the fraction of males that survive to adulthood (\overline{E'}) and emerge as males (1-\phi); the last one is used to add males into the population as part of gene-drive release campaigns.

Female adult populations are calculated in a similar way:

\overline{\overline{Af_{[t]}}}= \overline{\overline{Af_{[t-1]}}} * (1-\mu_{ad}) * \overline{\omega_{f}}\\ + \bigg( \overline{\phi} * \overline{E^{'}}+\overline{\nu f_{[t-1]}}\bigg)^{\top} * \bigg( \frac{\overline{\overline{\eta}}*\overline{Am_{[t-1]}}}{\sum{\overline{Am_{[t-1]}}}} \bigg)%\overline{\overline{Mf}}

where we first compute the surviving female adults from one day to the next; and then we calculate the mating composition of the female fraction emerging from pupa stage. To do this, we obtain the surviving fraction of eggs that survive to adulthood (\overline{E'}) and emerge as females (\phi), we then add the new females added as a result of gene-drive releases (\overline{\nu f_{[t-1]}}). After doing this, we calculate the proportion of males that are allocated to each female genotype, taking into account their respective mating fitnesses (\overline{\overline{\eta}}) so that we can introduce the new adult females into the population pool.

Gene Drive Releases and Effects

As it was briefly mentioned before, we are including the option to release both male and/or female individuals into the populations. Another important t hing to emphasize is that we allow flexible releases sizes and schedules. Our ] model handles releases internally as lists of populations compositions so, it is possible to have releases performed at irregular intervals and with different numbers of mosquito genetic compositions as long as no new genotypes are introduced (which have not been previously defined in the inheritance cube).

\overline{\nu} = \bigg\{ \left(\begin{array}{c} g_1 \\ g_2 \\ g_3 \\ \vdots \\ g_n \end{array}\right)_{t=1} , \left(\begin{array}{c} g_1 \\ g_2 \\ g_3 \\ \vdots \\ g_n \end{array}\right)_{t=2} , \cdots , \left(\begin{array}{c} g_1 \\ g_2 \\ g_3 \\ \vdots \\ g_n \end{array}\right)_{t=x} \bigg\}

So far, however, we have not described the way in which the effects of these gene-drives are included into the mosquito populations dynamics. This is done through the use of various modifiers included in the equations:

• \overline{\omega}: Relative increase in mortality (zero being full mortality effects and one no mortality effect)

• \overline{\phi}: Relative shift in the sex of the pupating mosquitoes (zero biases the sex ratio towards males, whilst 1 biases the ratio towards females).

• \overline{\overline{\eta}}: Standardized mating fitness (zero being complete fitness ineptitude, and one being regular mating skills).

• \overline{\beta}: Fecundity (average number of eggs laid).

• \overline{\xi}: Pupation success (zero being full mortality and one full pupation success).

Migration

To simulate migration within our framework we are considering patches (or nodes) of fully-mixed populations in a network structure. This allows us to handle mosquito movement across spatially-distributed populations with a transitions matrix, which is calculated with the tensor outer product of the genotypes populations tensors and the transitions matrix of the network as follows:

\overline{Am_{(t)}^{i}}= \sum{\overline{A_{m}^j} \otimes \overline{\overline{\tau m_{[t-1]}}}} \\ \overline{\overline{Af_{(t)}^{i}}}= \sum{\overline{\overline{A_{f}^j}} \otimes \overline{\overline{\tau f_{[t-1]}}}}

In these equations the new population of the patch i is calculated by summing the migrating mosquitoes of all the j patches across the network defined by the transitions matrix \tau, which stores the mosquito migration probabilities from patch to patch. It is worth noting that the migration probabilities matrices can be different for males and females; and that there's no inherent need for them to be static (the migration probabilities may vary over time to accommodate wind changes due to seasonality).

Parameters

This table compiles all the parameters required to run MGDrivE clustered in six categories:

• Life Stages: These deal with the structure of mosquito population.

• Bionomics: This set of parameters is related to the behavior of the specific mosquito species being modeled.

• Gene Drive: Genotype-specific vectors of parameters that affect how each gene-drive modifies the responses of populations to them.

• Releases: List of vectors that control the release of genetically-modified mosquitoes.

• Population: General mosquito-population parameters that control environmentally-determined variables.

• Network: Related to migration between nodes of population units

Stochasticity

MGDrivE allows stochasticity to be included in the dynamics of various processes; in an effort to simulate processes that affect various stages of mosquitoes lives. In the next section, we will describe all the stochastic processes that can be activated in the program. It should be noted that all of these can be turned on and off independently from one another as required by the researcher.

Mosquito Biology

Oviposition

Stochastic egg laying by female/male pairs is separated into two steps: calculating the number of eggs laid by the females and then distributing laid eggs according to their genotypes. The number of eggs laid follows a Poisson distribution conditioned on the number of female/male pairs and the fertility of each female.

Poisson( \lambda = numFemales*Fertility)

Multinomial sampling, conditioned on the number of offspring and the relative viability of each genotype, determines the genotypes of the offspring.

Multinomial \left(numOffspring, p_1, p_2\dots p_b \right)=\frac{numOffspring!}{p_1!\,p_2\,\dots p_n}p_1^{n_1}p_2^{n_2}\dots p_n^{n_n}

Sex Determination

Sex of the offspring is determined by multinomial sampling. This is conditioned on the number of eggs that live to hatching and a probability of being female, allowing the user to design systems that skew the sex ratio of the offspring through reproductive mechanisms.

Multinomial(numHatchingEggs, p_{female}, p_{female})

Mating Stochastic mating is determined by multinomial sampling conditioned on the number of males and their fitness. It is assumed that females mate only once in their life, therefore each female will sample from the available males and be done, while the males are free to potentially mate with multiple females. The males' ability to mate is modulated with a fitness term, thereby allowing some genotypes to be less fit than others (as seen often with lab releases).

Multinomial(numFemales, p_1f_1, p_2f_2, \dots p_nf_n)

Hatching

Other Stochastic Processes All remaining stochastic processes (larval survival, hatching , pupating, surviving to adult hood) are determined by multinomial sampling conditioned on factors affecting the current life stage. These factors are determined empirically from mosquito population data.

Migration

Variance of stochastic movement (not used in diffusion model of migration).

References

Deredec A, Godfray HCJ, Burt A (2011). “Requirements for effective malaria control with homing endonuclease genes.” Proceedings of the National Academy of Sciences of the United States of America, 108(43), E874–80. ISSN 1091-6490, doi:10.1073/pnas.1110717108, https://www.pnas.org/content/108/43/E874.

Hancock PA, Godfray HCJ (2007). “Application of the lumped age-class technique to studying the dynamics of malaria-mosquito-human interactions.” Malaria journal, 6, 98. ISSN 1475-2875, doi:10.1186/1475-2875-6-98, https://malariajournal.biomedcentral.com/articles/10.1186/1475-2875-6-98.

Marshall J, Buchman A, C. HMS, Akbari OS (2017). “Overcoming evolved resistance to population-suppressing homing-based gene drives.” Nature Scientific Reports, 1–46. ISSN 2045-2322, doi:10.1038/s41598-017-02744-7, https://www.nature.com/articles/s41598-017-02744-7.

Network Class Definition

Description

A Network class object stores all the information for a simulation on a defined landscape.

Format

An R6Class generator object

Constructor

• params: see parameterizeMGDrivE

• driveCube: an inheritance cube

• patchReleases: see basicRepeatedReleases for examples on how to set up release schedules

• migrationMale: a stochastic matrix whose dimensions conform to the number of patches

• migrationFemale: a stochastic matrix whose dimensions conform to the number of patches

• migrationBatch: a list of batch migration parameters. SeebasicBatchMigration

• directory: character string of output directory

• verbose: Chatty? Default is TRUE

Methods

• get_timeAq: see get_timeAq_Network

• get_beta: see get_beta_Network

• get_muAd: see get_muAd_Network

• get_muAq: see get_muAq_Network

• get_alpha: see get_alpha_Network

• get_drivecubeindex: see get_drivecubeindex_Network

• get_tau: see get_tau_Network

• get_genotypesID: see get_genotypesID_Network

• get_genotypesN: see get_genotypesN_Network

• get_eta: see get_eta_Network

• get_phi: see get_phi_Network

• get_omega: see get_omega_Network

• get_xiF: see get_xiF_Network

• get_xiM: see get_xiM_Network

• get_s: see get_s_Network

• get_nPatch: see get_nPatch_Network

• get_conADM: see get_conM_Network

• get_conADF: see get_conF_Network

• get_tNow: see get_tNow_Network

• get_patchReleases: see get_patchReleases_Network

• oneDay_Migration: see oneDay_Migration_Deterministic_Network or see oneDay_Migration_Stochastic_Network

• reset: see reset_Network

• oneDay: see oneDay_Network

• oneRun: see oneRun_Network

• multRun: see multRun_Network

Fields

• parameters: see parameterizeMGDrivE

• patches: a list of Patch objects

• nPatch: number of patches

• simTime: maximum time of simulation

• sampTime: how often to write output, tNow %% sampTime

• driveCube: an inheritance cube

• tNow: current time of simulation (time starts at 2 because time 1 is the initial equilibrium state)

• runID: an identifier for the current simulation run, useful for Monte Carlo simulation

• directory: a character string of where to store output

• conADM: a connection to write male population dynamics out to

• conADF: a connection to write female population dynamics out to

• migrationMale: a stochastic matrix whose dimensions conform to the number of patches

• migrationFemale: a stochastic matrix whose dimensions conform to the number of patches

• migrationBatch: list of items for batch migration in stochastic sim.

• mMoveMat: holder object for male migration

• fMoveArray: holder object for female migration

• patchReleases: a list of release schedules for each patch

Examples

 ## Not run: 
 # There are no simple examples for this, so looking at the vignettes would be
 #  most useful.

 # Complete manual with examples, but none explored in depth.
 vignette("MGDrivE-Examples", package = "MGDrivE")

 # One example, explored in great detail. This is probably more helpful.
 vignette("MGDrivE-Run", package = "MGDrivE")

 
## End(Not run)

Patch Class Definition

Description

A Patch is a single well-mixed population that is the smallest unit of simulation for MGDrivE.

Format

An R6Class generator object

Constructor

• patchID: integer ID of this patch

• genotypesID: character vector of genotypes

• timeAq: integer vector of length 3 specifying the length of each aquatic stage

• numPatches: integer, total number of patches in this simulation

• adultEQ: integer, total adult population in this patch for the duration of the simulation

• larvalEQ: integer, total larval population in this patch for the duration of the simulation

• muAq: double vector, length 3, daily death rate for each aquatic stage

• alpha: double, density-dependent centering parameter, see parameterizeMGDrivE

• adultRatioF: named double vector, distribution of adult female genotypes, see parameterizeMGDrivE

• adultRatioM: named double vector, distribution of adult male genotypes, see parameterizeMGDrivE

• larvalRatio: named double vector, distribution of all aquatic genotypes, see parameterizeMGDrivE

• eggReleases: egg release schedule for this patch, see basicRepeatedReleases

• maleReleases: male release schedule for this patch, see basicRepeatedReleases

• femaleReleases: female release schedule for this patch, see basicRepeatedReleases

• matedFemaleReleases: mated females release schedule for this patch, see basicRepeatedReleases

Methods

• set_NetworkPointer: see set_NetworkPointer_Patch

• get_femalePopulation: see get_femalePop_Patch

• get_malePopulation: see get_malePop_Patch

• initialPopulation: see set_initialPopulation_Patch

• setPopulation: see set_population_deterministic_Patch or set_population_stochastic_Patch

• reset: see reset_Patch

• oneDay_initOutput: see oneDay_initOutput_Patch

• oneDay_writeOutput: see oneDay_writeOutput_Patch

• oneDay_migrationIn: see oneDay_migrationIn_Patch

• oneDay_PopDynamics: see oneDay_PopDynamics_Patch

• oneDay_adultD: see oneDay_adultDeath_deterministic_Patch or oneDay_adultDeath_stochastic_Patch

• oneDay_pupaDM: see oneDay_pupaDM_deterministic_Patch or oneDay_pupaDM_stochastic_Patch

• oneDay_larvaDM: see oneDay_larvaDM_deterministic_Patch or oneDay_larvaDM_stochastic_Patch

• oneDay_eggDM: see oneDay_eggDM_deterministic_Patch or oneDay_eggDM_stochastic_Patch

• oneDay_pupation: see oneDay_pupation_deterministic_Patch or oneDay_pupation_stochastic_Patch

• oneDay_releases: see oneDay_releases_Patch

• oneDay_releaseEggs: see oneDay_eggReleases_Patch

• oneDay_mating: see oneDay_mating_deterministic_Patch or oneDay_mating_stochastic_Patch

• oneDay_layEggs: see oneDay_oviposit_deterministic_Patch or oneDay_oviposit_stochastic_Patch

Fields

• patchID: integer ID of this patch

• popAquatic: matrix, nGenotype x sum(timeAquatic), holding all eggs, larva, and pupa

• popMale: vector, nGenotype x 1, holds adult males

• popFemale: matrix, nGenotype x nGenotype, holds mated adult females

• popHolder: vector, nGenotype x 1, temporary population storage

• popPupSex: vector, nGenotype x 1, used in stochastic pupation as another temporary population

• popUnmated: vector, nGenotype x 1, holds unmated females

• popAquatict0: matrix, nGenotype x sum(timeAquatic), holding all eggs, larva, and pupa for reset, see reset_Patch

• popMalet0: vector, nGenotype x 1, holds adult males for reset see reset_Patch

• popFemalet0: matrix, nGenotype x nGenotype, holds mated adult females for reset see reset_Patch

• eggReleases: list of egg releases for this patch. See oneDay_eggReleases_Patch

• maleReleases: list of adult male releases for this patch. See oneDay_releases_Patch

• femaleReleases: list of adult female releases for this patch. See oneDay_releases_Patch

• matedFemaleReleases: list of mated adult female releases for this patch. See oneDay_releases_Patch

• NetworkPointer: a reference to enclosing Network

Aggregate Female Output by Genotype

Description

Aggregate over male mate genotype to convert female matrix output into vector output.

Usage

aggregateFemales(
  readDir,
  writeDir = NULL,
  genotypes,
  remFile = TRUE,
  verbose = TRUE
)

Arguments

Examples

## Not run: 
# This example assumes user has already run MGDrivE and generated output.
#  This also assumes that the user has already split output by patch.
# See vignette for complete example.

# set read/write directory
fPath <- "path/to/data/containing/folder"

# Need genotypes from the cube run in the simulation
#  This is dependent on the simulation run
#  Using Mendelian cube for this example
cube <- cubeMendelian()

# no return value from function
aggregateFemales(readDir= fPath, writeDir = NULL, genotypes = cube$genotypesID,
                 remFile = TRUE)

## End(Not run)

Aggregate Output Over Landscape

Description

This function aggregates the output of a run over the entire output, i.e., all of the patches. It writes the output one level above the folder pointed to by readDir, if writeDir is NULL. Output consists of 2 csv files, one for males and one for females, "...M_LandscapeAgg_Run...csv".

Usage

aggregateOutput(readDir, writeDir=NULL)

Arguments

Examples

## Not run: 
# This assumes user has run MGDrivE and output is in fPath.
#  See vignette for examples on how to run MGDrivE

# read/write dirs
fPath <- "folder/containing/output"
oPath <- "folder/to/write/stuff"

# first, split output by patch and aggregate females by mate genotype
# remember, cube is for example and changes with simulation
#  landscape aggregation will work if females are not aggregated, but it's slower
cube <- cubeMendelian()

splitOutput(readDir = fPath, writeDir = NULL, remFile = TRUE)
aggregateFemales(readDir= fPath, writeDi = NULL, genotypes = cube$genotypesID,
                 remFile = TRUE)

# aggregate mosquitoes over entire landscape
#  no return value
aggregateOutput(readDir = fPath, writeDir = NULL)

## End(Not run)

Make List of Batch Migration Parameters

Description

Sets up a list containing the probability of a batch migration, the fractional amount of males/females that migrate, and the weighted probabilities for where to migrate. The default weights for migration are equal for all patches. These can be changed after running the function. This is only used in oneDay_Migration_Stochastic_Network.

Usage

basicBatchMigration(
  batchProbs = 1e-05,
  sexProbs = c(0.01, 0.01),
  numPatches = 1
)

Arguments

Examples

# to setup for 3 patches
batchMigration = basicBatchMigration(batchProbs = 1e-5, sexProbs = c(0.1, 0.01), numPatches = 3)

Make List of Modified Mosquito Releases

Description

Sets up a release schedule for a single patch, returns a list to be used in oneDay_releases_Patch or oneDay_eggReleases_Patch. This function is no longer intended to be used alone, please use the standard interface, generateReleaseVector.

Usage

basicRepeatedReleases(releaseStart, releaseEnd, releaseInterval, releaseMatrix)

Arguments

Examples

## Not run: 
# Setup for 3 patches but only release in the first with a defined release
#  schedule, for the cube cubeHomingDrive:

patchReleases = replicate(n = 3, expr = {
  list(maleReleases = NULL, femaleReleases = NULL, eggReleases = NULL, matedFemaleReleases = NULL)
},simplify = FALSE)

patchReleases[[1]]$femaleReleases = MGDrivE::basicRepeatedReleases(releaseStart = 5,
                                                          releaseEnd = 30,
                                                          releaseInterval = 5,
                                                          releaseMatrix = matrix(c(5,100),1,2))

patchReleases[[1]]$maleReleases = MGDrivE::basicRepeatedReleases(releaseStart = 50,
                                                        releaseEnd = 60,
                                                        releaseInterval = 1,
                                                        releaseMatrix = matrix(c(5,100),1,2))

## End(Not run)

Calculate Aquatic Stage Survival Probability

Description

Calculate \theta_{st}, density-independent survival probability, given by:

\theta_{st}=(1-\mu_{st})^{T_{st}}

Usage

calcAquaticStageSurvivalProbability(mortalityRate, stageDuration)

Arguments

Calculate Average Generation Time

Description

Calculate g, average generation time, given by:

g=T_e+T_l+T_p+\frac{1}{\mu_{ad}}

Usage

calcAverageGenerationTime(stagesDuration, adultMortality)

Arguments

Calculate Geodesic Distance - Cosine Method

Description

This function calculates geodesic distance using the cosine method.

Usage

calcCos(latLongs, r = 6378137)

Arguments

Examples

# two-column matrix with latitude/longitude, in degrees
latLong = cbind(runif(n = 5, min = 0, max = 90),
                runif(n = 5, min = 0, max = 180))

# cosine distance formula
distMat = calcCos(latLongs = latLong)

Calculate Density-dependent Larval Mortality

Description

Calculate \alpha, the strength of density-dependent mortality during the larval stage, given by:

\alpha=\Bigg( \frac{1/2 * \beta * \theta_e * Ad_{eq}}{R_m-1} \Bigg) * \Bigg( \frac{1-(\theta_l / R_m)}{1-(\theta_l / R_m)^{1/T_l}} \Bigg)

Usage

calcDensityDependentDeathRate(
  fertility,
  thetaAq,
  tAq,
  adultPopSizeEquilibrium,
  populationGrowthRate
)

Arguments

Calculate Exponential Stochastic Matrix

Description

Given a distance matrix from calcVinEll, calculate a stochastic matrix where one step movement probabilities follow an exponential density.

Usage

calcExpKernel(distMat, rate)

Arguments

Details

The distribution and density functions for the exponential kernel are given below:

F(x)=1-e^{-\lambda x}

f(x)=\lambda e^{-\lambda x}

where \lambda is the rate parameter of the exponential distribution.

Examples

# setup distance matrix
# two-column matrix with latitude/longitude, in degrees
latLong = cbind(runif(n = 5, min = 0, max = 90),
                runif(n = 5, min = 0, max = 180))

# Vincenty Ellipsoid  distance formula
distMat = calcVinEll(latLongs = latLong)

# calculate exponential distribution over distances
#  rate is just for example
kernMat = calcExpKernel(distMat = distMat, rate = 10)

Calculate Gamma Stochastic Matrix

Description

Given a distance matrix from calcVinEll, calculate a stochastic matrix where one step movement probabilities follow a gamma density.

Usage

calcGammaKernel(distMat, shape, rate)

Arguments

Details

The distribution and density functions for the gamma kernel are given below:

F(x)=\frac{1}{\Gamma(\alpha)}\gamma(\alpha,\beta x)

f(x)=\frac{\beta^{\alpha}}{\Gamma(\alpha)}x^{\alpha-1}e^{-\beta x}

where \Gamma(\alpha) is the Gamma function, \gamma(\alpha,\beta x) is the lower incomplete gamma function, and \alpha,\beta are the shape and rate parameters, respectively.

Examples

# setup distance matrix
# two-column matrix with latitude/longitude, in degrees
latLong = cbind(runif(n = 5, min = 0, max = 90),
                runif(n = 5, min = 0, max = 180))

# Vincenty Ellipsoid  distance formula
distMat = calcVinEll(latLongs = latLong)

# calculate gamma distribution over distances
#  shape and rate are just for example
kernMat = calcGammaKernel(distMat = distMat, shape = 1, rate = 1)

Calculate Geodesic Distance - Haversine Method

Description

This function calculates geodesic distance using the Haversine method.

Usage

calcHaversine(latLongs, r = 6378137)

Arguments

Examples

# two-column matrix with latitude/longitude, in degrees
latLong = cbind(runif(n = 5, min = 0, max = 90),
                runif(n = 5, min = 0, max = 180))

# Haversine distance formula
distMat = calcHaversine(latLongs = latLong)

Calculate Zero-inflated Exponential Stochastic Matrix

Description

Given a distance matrix from calcVinEll, calculate a stochastic matrix where one step movement probabilities follow an zero-inflated exponential density with a point mass at zero. The point mass at zero represents the first stage of a two-stage process, where mosquitoes decide to stay at their current node or leave anywhere. This parameter can be calculated from lifetime probabilities to stay at the current node with the helper function calcZeroInflation.

Usage

calcHurdleExpKernel(distMat, rate, p0, eps = 1e-20)

Arguments

Details

If a mosquito leaves its current node, with probability 1-p_{0}, it then chooses a destination node according to a standard exponential density with rate parameter rate.

The distribution and density functions for the zero inflated exponential kernel are given below:

F(x)=p_{0}\theta(x) + (1-p_{0})(1-e^{-\lambda x})

f(x)=p_{0}\delta(x)+(1-p_{0})\lambda e^{-\lambda x}

where \lambda is the rate parameter of the exponential distribution, \theta(x) is the Heaviside step function and \delta(x) is the Dirac delta function.

Examples

# setup distance matrix
# two-column matrix with latitude/longitude, in degrees
latLong = cbind(runif(n = 5, min = 0, max = 90),
                runif(n = 5, min = 0, max = 180))

# Vincenty Ellipsoid  distance formula
distMat = calcVinEll(latLongs = latLong)

# calculate hurdle exponential distribution over distances
#  rate and point mass are just for example
kernMat = calcHurdleExpKernel(distMat = distMat, rate = 1/1e6, p0 = 0.1)

Calculate Distribution of Larval Population

Description

This hidden function calculates the distribution of larvae through time by treating the larval-stage as a discrete-time Markov chain, and solving for the stationary distribution. As the only aquatic population known for initializing MGDrivE is the equilibrium larval population, this acts as an anchor from which to calculate the egg and pupae distributions from (see set_initialPopulation_Patch).

Usage

calcLarvalDist(mu, t)

Arguments

Calculate Equilibrium Larval Population

Description

Equilibrium larval population size to sustain adult population.

Usage

calcLarvalPopEquilibrium(alpha, Rm)

Arguments

Calculate Larval Stage Mortality Rate

Description

Calculate \mu_{l}, the larval mortality, given by

\mu_l=1-\Bigg( \frac{R_m * \mu_{ad}}{1/2 * \beta * (1-\mu_m)} \Bigg)^{\frac{1}{T_e+T_l+T_p}}

Usage

calcLarvalStageMortalityRate(
  generationPopGrowthRate,
  adultMortality,
  fertility,
  aquaticStagesDuration
)

Arguments

Calculate Lognormal Stochastic Matrix

Description

Given a distance matrix from calcVinEll, calculate a stochastic matrix where one step movement probabilities follow a lognormal density.

Usage

calcLognormalKernel(distMat, meanlog, sdlog)

Arguments

Details

The distribution and density functions for the lognormal kernel are given below:

F(x)=\frac{1}{2} + \frac{1}{2} \mathrm{erf}[\frac{\mathrm{ln}x-\mu}{\sqrt{2}\sigma}]

f(x)=\frac{1}{x\sigma\sqrt{2\pi}}\mathrm{exp}\left( -\frac{(\mathrm{ln}x-\mu)^{2}}{2\sigma^{2}} \right)

where \mu is the mean on the log scale, and \sigma is the standard deviation on the log scale.

Examples

# setup distance matrix
# two-column matrix with latitude/longitude, in degrees
latLong = cbind(runif(n = 5, min = 0, max = 90),
                runif(n = 5, min = 0, max = 180))

# Vincenty Ellipsoid  distance formula
distMat = calcVinEll(latLongs = latLong)

# calculate lognormal distribution over distances
#  mean and standard deviation are just for example
kernMat = calcLognormalKernel(distMat = distMat, meanlog = 100, sdlog = 10)

Solve for Omega (additional genotype-specific mortality)

Description

Solves for root of equation of geometrically-distributed lifespan for value of omega.

Usage

calcOmega(mu, lifespanReduction)

Arguments

Examples

# reduce lifespan by 10%
#  Example mu is an average for Aedes
newOmega <- calcOmega(mu = 0.11, lifespanReduction = 0.90)

Calculate Generational Population Growth Rate

Description

Calculate R_{m}, population growth in absence of density-dependent mortality, given by:

(r_{m})^{g}

Usage

calcPopulationGrowthRate(dailyPopGrowthRate, averageGenerationTime)

Arguments

Summary Statistics for Stochastic MGDrivE

Description

This function reads in all repetitions for each patch and calculates either the mean, quantiles, or both. User chooses the quantiles, up to 4 decimal places, and enters them as a vector. Quantiles are calculated empirically. (order does not matter)

Usage

calcQuantiles(readDir, writeDir, mean = TRUE, quantiles = NULL, verbose = TRUE)

Arguments

Details

Given the readDir, this function assumes the follow file structure:

• readDir

  • repetition 1

    • patch 1

    • patch 2

    • patch 3

  • repetition 2

    • patch 1

    • patch 2

    • patch 3

  • repetition 3

  • repetition 4

  • ...
    

Output files are *.csv contain the mean or quantile in the file name, i.e. {M/F}Mean(patchNum).csv and {M/F}Quantile(quantNum)_(patchNum).csv.

Value

Writes output to files in writeDir

Examples

## Not run: 
# This function assumes network$multRun() has been performed, or several
#  network$oneRun() have been performed and all of the data has been split
#  and aggregated.

# read/write paths
fPath <- "path/to/folder/ofFolders/with/data"
oPath <- "my/path/output"

# here, only calculate mean, no quantiles
#  no return value
calcQuantiles(readDir = fPath, writeDir = oPath, mean = TRUE,
              quantiles = NULL)

# here, calculate 2.5% and 97.5% quantiles
calcQuantiles(readDir = fPath, writeDir = oPath, mean = FALSE,
              quantiles = c(0.025, 0.975))

## End(Not run)

Calculate Geodesic Distance - Vincenty Ellipsoid Method

Description

This function calculates geodesic distance using the original Vincenty Ellipsoid method.

Usage

calcVinEll(
  latLongs,
  a = 6378137,
  b = 6356752.3142,
  f = 1/298.257223563,
  eps = 1e-12,
  iter = 100
)

Arguments

Examples

# two-column matrix with latitude/longitude, in degrees
latLong = cbind(runif(n = 5, min = 0, max = 90),
                runif(n = 5, min = 0, max = 180))

# Vincenty Ellipsoid  distance formula
distMat = calcVinEll(latLongs = latLong)

Calculate Geodesic Distance - Vincenty Sphere Method

Description

This function calculates geodesic distance using the Vincenty sphere method.

Usage

calcVinSph(latLongs, r = 6378137)

Arguments

Examples

# two-column matrix with latitude/longitude, in degrees
latLong = cbind(runif(n = 5, min = 0, max = 90),
                runif(n = 5, min = 0, max = 180))

# Vincenty Sphere  distance formula
distMat = calcVinSph(latLongs = latLong)

Calculates the zero-inflation part of a hurdle exponential kernel.

Description

Given the probability of an adult mosquito to stay in the same patch throughout its whole lifespan, and its mortality, it calculates the height of the pulse-density part of the hurdle kernel.

Usage

calcZeroInflation(stayThroughLifespanProbability, adultMortality)

Arguments

Examples

# setup distance matrix
# two-column matrix with latitude/longitude, in degrees
latLong = cbind(runif(n = 5, min = 0, max = 90),
                runif(n = 5, min = 0, max = 180))

# Vincenty Ellipsoid  distance formula
distMat = calcVinEll(latLongs = latLong)

# get hurdle height
# Lets assume 80% stay probs and adult mortality of 0.1
hHeight <- calcZeroInflation(stayThroughLifespanProbability = 0.80,
                             adultMortality = 0.1)

# calculate hurdle exponential distribution over distances
kernMat = calcHurdleExpKernel(distMat = distMat, rate = 10, p0 = hHeight)

Export a Cube to .csv

Description

Export a cube as multiple .csv files (one for each genotype; slices of z-axis). This function will create the directory if it doesn't exist. Files are stored as slice_(z-slice)_(genotype).csv

Usage

cube2csv(cube, directory, digits = 3)

Arguments

Examples

## Not run: 
# output directory
oPath <- "path/to/write/output"

# setup inheritance cube for export, using Mendelian as the example
cube <- cubeMendelian()

# write out
cube2csv(cube = cube, directory = oPath, digits = 3)

## End(Not run)

Inheritance Cube: Split-Drive for Andrea Smidler

Description

This is a modified split-drive construct for split-suppression drives. This is an autosomal, 2-locus drive. The first locus contains the Cas9 allele and the second locus has the gRNA construct. Cas9 efficacy in each sex is dependent upon parent of inheritance. This construct has 3 alleles at the first locus and 4 alleles at the second.

• Locus 1

  • W: Wild-type

  • P: Paternal Cas9

  • M: Maternal Cas9

• Locus 2

  • W: Wild-type

  • G: gRNAs

  • R: Resistant 1

  • B: Resistant 2

Usage

cubeASmidler(
  cMM = 0,
  chMM = 0,
  crMM = 0,
  cPM = 0,
  chPM = 0,
  crPM = 0,
  cMF = 0,
  chMF = 0,
  crMF = 0,
  cPF = 0,
  chPF = 0,
  crPF = 0,
  crF = 0.5,
  crM = 0.5,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: 3-Piece Allele Sail

Description

A generalized implementation of the Allele Sail (doi:10.1038/s41467-024-50992-9) idea.

Usage

cubeAlleleSail(
  cMM = 0,
  crMM = 0,
  cPM = 0,
  crPM = 0,
  cMF = 0,
  crMF = 0,
  cPF = 0,
  crPF = 0,
  dMW = 0,
  dMrW = 0,
  dPW = 0,
  dPrW = 0,
  crF12 = 0.5,
  crM12 = 0.5,
  crF23 = 0.5,
  crM23 = 0.5,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Details

This is an autosomal, 3-locus system. The first locus contains the Cas9 allele, the second locus carries the gRNA, and the third locus is the target. All loci can be linked/unlinked to the locus before it (so, 1 to 2 or 2 to 3). Cas9 efficacy due to provenance (mother vs father) is included.

This construct is very similar to our 2-locus Cleave and Rescue design for Oberhofer et. al.(doi:10.1101/2020.07.09.196253).

This construct has 3 alleles at the first locus, 2 alleles at the second locus, and 3 alleles at the third locus.

• Locus 1

  • W: Wild-type

  • P: Paternal Cas9

  • M: Maternal Cas9

• Locus 2

  • W: Wild-type

  • G: gRNAs

• Locus 3

  • W: Wild-type

  • R: Resistant 1

  • B: Resistant 2

Female deposition is implemented incorrectly. Right now, it is performed on male alleles prior to zygote formation - it should happen post-zygote formation. Since this construct doesn't have HDR, this should be fine.

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: CLEAVR - Cleave and Rescue

Description

This is a novel cube from the Akbari lab. There are 2 loci: the first is a female fertility locus (e.g. doubleSex), where the Cas, gRNAs, and a recoded essential gene go. This locus is inherited in a Mendelian fashion, but is also targeted for destruction by the homing allele. The second locus involves an essential gene, for both males and females, and this is the target of the gRNAs at the first locus. No homing is performed, it is simply destroyed. There is different cutting rates in males and females, with no possibility for a rescuing resistant allele. Females homozygous for the H or B alleles at locus 1 are viable but infertile, while males are unaffected. All animals homozygous at locus two must contain the recoded copy at locus 1 to be viable. This version corresponds to the homing construct being autosomal.

Usage

cubeCLEAVRMF(
  cM1 = 1,
  cM2 = 1,
  cF1 = 1,
  cF2 = 1,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: CLEAVR - Cleave and Rescue

Description

This is a novel cube from the Akbari lab. There are 2 loci: the first is a female fertility locus (e.g. doubleSex), where the Cas, gRNAs, and a recoded essential gene go. This locus is inherited in a Mendelian fashion, but is also targeted for destruction by the homing allele. The second locus involves an essential gene, for both males and females, and this is the target of the gRNAs at the first locus. No homing is performed, it is simply destroyed. There is different cutting rates in males and females, with no possibility for a rescuing resistant allele. Females homozygous for the H or B alleles at locus 1 are viable but infertile, while males are unaffected. All animals homozygous at locus two must contain the recoded copy at locus 1 to be viable. This corresponds to the homing construct being X-linked.

Usage

cubeCLEAVRX(
  cM1 = 1,
  cM2 = 1,
  cF1 = 1,
  cF2 = 1,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: ClvR (Cleave and Rescue)

Description

Based on the Cleave-and-Rescue system of Oberhofer et. al. (doi:10.1073/pnas.1921698117), this is a 2-locus Cas9-based toxin-antidote system. The first locus carries the Cas9, gRNAs, and a recoded copy of an essential gene. The second locus is the targeted essential gene. This gene can be completely haplosufficient (hSuf = 1) or completely haploinsufficient (hSuf = 0). It is assumed that having 2 copies of the gene (be it wild-type at the second locus or recoded at the first) confers complete viability.

Usage

cubeClvR(
  cF = 1,
  crF = 0,
  ccF = cF,
  ccrF = crF,
  cM = 1,
  crM = 0,
  ccM = cM,
  ccrM = crM,
  dW = 0,
  drW = 0,
  ddW = dW,
  ddrW = drW,
  hSuf = 1,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: 2-Locus ClvR (Cleave and Rescue)

Description

Based on the Cleave-and-Rescue system of Oberhofer et. al. (doi:10.1101/2020.07.09.196253), this is a 3-locus Cas9-based toxin-antidote system. The first locus carries the Cas9, the second locus carries the gRNAs, and a recoded copy of an essential gene. The third locus is the targeted essential gene. This gene can be completely haplosufficient (hSuf = 1) or completely haploinsufficient (hSuf = 0). It is assumed that having 2 copies of the gene (be it wild-type at the second locus or recoded at the first) confers complete viability. Additionally, loci 1 and 2 can be linked, given crM and crF, imitating the original 2-locus ClvR system. For this construct, the first locus will have 2 alleles, the second will have 2 alleles, and the third will have 3 alleles:

• Locus 1

  • W: Wild-type

  • C: Cas9

• Locus 2

  • W: Wild-type

  • G: gRNAs and recoded essential gene

• Locus 3

  • W: Wild-type

  • R: Functional resistant

  • B: Non-functional resistant

Usage

cubeClvR2(
  cF = 1,
  crF = 0,
  ccF = cF,
  ccrF = crF,
  cM = 1,
  crM = 0,
  ccM = cM,
  ccrM = crM,
  dW = 0,
  drW = 0,
  ddW = dW,
  ddrW = drW,
  hSuf = 1,
  crossF = 0.5,
  crossM = 0.5,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Details

Female deposition is implemented incorrectly. Right now, it is performed on male alleles prior to zygote formation - it should happen post-zygote formation. Since this construct doesn't have HDR, this should be fine.
Additionally, it is assumed that deposition requries loaded Cas9-RNP complexes from the mother, having Cas9 and no maternal gRNA, even in the presence of paternal gRNA, will not result in maternal deposition mediated cleavage.

Copy-number dependent rates are based on Cas9, not gRNA. The assumption is that RNA is easier to produce, and therefore won't limit cleavage by Cas9.

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: Confinable Homing

Description

This function creates a confinable homing construct, it has 4 alleles at the first locus and 3 alleles at the second.

• W: Wild-type

• H: Homing allele

• A: Antidote allele

• R: No-cost resistance allele

• B: Detrimental resistance allele

Usage

cubeConfinableHoming(
  cF = 1,
  cM = 1,
  chF = 0,
  crF = 0,
  chM = 0,
  crM = 0,
  dR = 0,
  dB = 0,
  crossF = 0,
  crossM = 0,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: Confinable Homing Drive, John

Description

write me

Usage

cubeConfinableHomingJOHN(
  eM = 1,
  eF = 1,
  prRF = 0,
  prRM = 0,
  r = 0,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Inheritance Cube: Confinable Homing, X-Linked

Description

This function creates an X-linked confinable homing construct, it has 5 alleles at the first locus and 4 alleles at the second. No crossovers or homing occurs into the y chromosome

• W: Wild-type

• H: Homing allele

• A: Antidote allele

• R: No-cost resistance allele

• B: Detrimental resistance allele

• Y: Male allele

Usage

cubeConfinableHomingX(
  cF = 1,
  chF = 0,
  crF = 0,
  dR = 0,
  dB = 0,
  crossF = 0,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: ECHACR

Description

This function creates an ECHACR construct, it has 5 alleles at the first locus and 4 alleles at the second.

• W: Wild-type

• H: Homing allele

• E: Eraser allele

• R: No-cost resistance allele

• B: Detrimental resistance allele

• cHW: Rate of homing from H, W -> H transition

• cEH: Rate of homing from E, H -> E transition

• cEW: Rate of homing from E, W -> E transition

Usage

cubeECHACR(
  cHW = 1,
  cEW = 1,
  cEH = 1,
  chHW = 0,
  crHW = 0,
  ceEW = 0,
  crEW = 0,
  ceEH = 0,
  crEH = 0,
  d1 = 0,
  d2 = 0,
  d3 = 0,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Details

This inheritance pattern corresponds to the Active Genetic Neutralizing Elements for Halting or Deleting Gene Drives (doi:10.1016/j.molcel.2020.09.003) publication.

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: ECHACRX

Description

This function creates an X-linked ECHACR construct, it has 5 alleles at the first locus and 4 alleles at the second.

• W: Wild-type

• H: Homing allele

• E: Eraser allele

• R: No-cost resistance allele

• B: Detrimental resistance allele

• cHW: Rate of homing from H, W -> H transition

• cEH: Rate of homing from E, H -> E transition

• cEW2: Rate of homing from E, W -> E transition

Usage

cubeECHACRX(
  cHW = 1,
  cEHW = 1,
  cEW1 = 1,
  cEW2 = 1,
  cEH = 1,
  chHW = 0,
  crHW = 0,
  chEHW = 0,
  crEHW = 0,
  ceEW1 = 0,
  crEW1 = 0,
  ceEW2 = 0,
  crEW2 = 0,
  ceEH = 0,
  crEH = 0,
  d1 = 0,
  d2 = 0,
  d3 = 0,
  dHW = 0,
  dEH = 0,
  dEW = 0,
  drHW = 0,
  drEH = 0,
  drEW = 0,
  crossF = 0,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Details

This inheritance pattern corresponds to the Active Genetic Neutralizing Elements for Halting or Deleting Gene Drives (doi:10.1016/j.molcel.2020.09.003) publication.

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: Homing Drive with 1 Resistance Allele

Description

This function creates an inheritance cube to model a homing gene drive (such as a CRISPR-Cas9 system) that creates 1 type of resistance allele. It assumes no sex-specific inheritance patterns and the construct is on an autosome.

Usage

cubeHoming1RA(
  c = 1,
  ch = 0,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) with 2 Resistance Alleles and maternal deposition

Description

This is a sex-specific version of the original cube cubeHoming1RA. It assumes that the construct is on an autosome and there can be different male/female homing rates. It also has maternal deposition, i.e., when the male provides a W allele to a female with a H allele, some portion are cut during oogenesis. If the maternal deposition parameters are zero (d* parameters), this is a normal CRISPR drive.

Usage

cubeHomingDrive(
  cM = 1,
  cF = 1,
  dF = 0,
  chM = 0,
  crM = 0,
  chF = 0,
  crF = 0,
  dhF = 0,
  drF = 0,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: CRISPR-SM (Clustered Regularly Interspaced Short Palindromic Repeats) with Small-Molecule Induction and 1 Resistance Allele and Maternal Deposition

Description

This is a sex-specific version of CRISPR with small-molecule induced homing. It assumes that the construct is on an autosome and there can be different male/female homing rates. It also has maternal deposition, i.e., when the male provides a W allele to a female with a H allele, some portion are cut during oogenesis. Additionally, this cube is designed for small-molecule induction, i.e., with the SM branch of MGDrivE. It allows the homing (H) allele to be turned off into an O allele, which inherits stably, and so that all offspring of H individuals are O until turned on with the spray. If the maternal deposition parameters are zero (d* parameters), this is a normal CRISPR drive.

Usage

cubeHomingDriveSM(
  cM = 1,
  cF = 1,
  dF = 0,
  chM = 0,
  chF = 0,
  dhF = 0,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: Immunizing Reversal/Basic Reversal

Description

This function creates an Immunizing Reversal construct, it has 5 alleles at 1 locus

• W: Wild-type

• H: Homing allele

• E: Eraser allele

• R: No-cost resistance allele

• B: Detrimental resistance allele

Usage

cubeImmunizingReversalMF(
  cHWM = 1,
  cHWF = 1,
  cEWM = 1,
  cEWF = 1,
  cEHM = 1,
  cEHF = 1,
  chHWM = 0,
  chHWF = 0,
  crHWM = 0,
  crHWF = 0,
  ceEWM = 0,
  ceEWF = 0,
  crEWM = 0,
  crEWF = 0,
  ceEHM = 0,
  ceEHF = 0,
  crEHM = 0,
  crEHF = 0,
  dHW = 0,
  dEW = 0,
  dEH = 0,
  dhHW = 0,
  drHW = 0,
  deEW = 0,
  drEW = 0,
  deEH = 0,
  drEH = 0,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Details

This is the general form for an immunizing reversal drive. If the EW terms are 0, then this simplifies to a basic reversal drive.This drive handles different male and female homing rates, and female deposition from each allele, signifying differential expression from an autosome.

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: Immunizing Reversal

Description

This function creates an Immunizing Reversal construct, it has 5 alleles at 1 locus

• W: Wild-type

• H: Homing allele

• E: Eraser allele

• R: No-cost resistance allele

• B: Detrimental resistance allele

Usage

cubeImmunizingReversalX(
  cHW = 1,
  cEW = 1,
  cEH = 1,
  chHW = 0,
  crHW = 0,
  ceEW = 0,
  crEW = 0,
  ceEH = 0,
  crEH = 0,
  dHW = 0,
  dEW = 0,
  dEH = 0,
  dhHW = 0,
  drHW = 0,
  deEW = 0,
  drEW = 0,
  deEH = 0,
  drEH = 0,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Details

This is the general form for an immunizing reversal drive. If the c_EW and d_EW, parameters are all 0, then this simplifies to a basic reversal drive. This drive represents an X-linked IR drive.

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: Killer-Rescue System

Description

This function creates an inheritance cube to model a Killer-Rescue system. Killer-Rescue is a 2-locus system: one locus has a toxin and the other locus contains the antidote. The loci are assumed independent and are non-homing.
This drive has 3 alleles at locus 1 and 2 alleles and locus 2:

• Locus 1

  • T: Wild-type allele

  • K: "Killer" toxin allele

  • R: Broken toxin allele

• Locus 2

  • W: Wild-type allele

  • A: Antidote allele

Usage

cubeKillerRescue(
  eR = 0,
  Keff = 1,
  Aeff = 1,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: MEDEA (Maternal Effect Dominant Embryonic Arrest)

Description

This function creates an inheritance cube to model a MEDEA drive system. This system was first discovered in flour beetles. It biases inheritance by expressing a maternal toxin such that offspring die unless they express a zygotic antidote.
This drive has 3 alleles at 1 locus:

• W: Wild-type allele

• M: MEDEA allele

• R: Resistance allele

Usage

cubeMEDEA(
  rM = 0,
  rW = 0,
  Teff = 1,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: Mendelian

Description

This function creates a Mendelian Inheritance Cube. It only handles simple, alphabetic genotypes.
The default is 3 alleles at 1 locus, this can be extended to however many alleles one is interested in, but only at 1 locus.

Usage

cubeMendelian(
  gtype = c("AA", "Aa", "aa"),
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Generate and Modify Default Genotype-specific Parameters

Description

This is an internal function for cubes.

Usage

cubeModifiers(
  gtype,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Inheritance Cube: 1 Locus Maternal-Toxin/Zygotic-Antidote System

Description

This function creates a 1 locus maternal-toxin/zygotic-antidote system. This is similar to the construct called UDmel. There is no resistance generation in this model.
This drive has 3 alleles at 1 locus:

• A: Maternal-toxin 1, zygotic-antidote 2

• B: Maternal-toxin 2, zygotic-antidote 1

• W: Wild-type allele

Usage

cubeOneLocusTA(
  TAEfficacy = 1,
  TBEfficacy = 1,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: RIDL (Release of Insects with Dominant Lethality)

Description

This function creates a RIDL system. RIDL (Release of Insects with Dominant Lethality), is a form of SIT. Created by Oxitec, this is based on a positive feedback loop using the toxic tTAV gene, controlled under lab conditions by the TetO promoter. This has 2 alleles at 1 locus

• W: Wild-type allele

• R: OX513 RIDL allele

Usage

cubeRIDL(
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: Reciprocal Translocation

Description

This function creates an inheritance cube to model a reciprocal translocation. This technology was the original form of underdominant system. It involves 2 chromosomes, each with two alleles.
This drive has 4 alleles at 2 loci:

• a: Wild-type at locus A

• A: Translocation at locus A

• b: Wile-type at locus B

• B: Translocation at locus B

Usage

cubeReciprocalTranslocations(
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: Split CRISPR Drive with 2 Resistance Alleles and male/female specific homing

Description

This is a sex-specific version of a split CRISPR drive. At one locus is the Cas9, inherited in a Mendelian fashion. At a second, unlinked, locus are the gRNAs. When the two loci occur together, the gRNAs drive, with potential damaged alleles, but the Cas9 remains Mendelian. It is assumed that this is an autosomal drive. This drive corresponds to the confinable gene drive system developed by the Akbari lab.

Usage

cubeSplitDrive(
  cM = 1,
  chM = 0,
  crM = 0,
  ccM = cM,
  cchM = chM,
  ccrM = crM,
  cF = 1,
  chF = 0,
  crF = 0,
  ccF = cF,
  cchF = chF,
  ccrF = crF,
  dW = 0,
  dhW = 0,
  drW = 0,
  ddW = dW,
  ddhW = dhW,
  ddrW = drW,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: X-linked Split CRISPR Drive with 2 Resistance Alleles and male/female specific homing

Description

This is a X-linked, sex-specific version of a split CRISPR drive. At the X locus is the Cas9, inherited in a Mendelian fashion. At a second, unlinked, autosomal locus are the gRNAs. When the two loci occur together, the gRNAs drive, with potential damaged alleles, but the Cas9 remains Mendelian. Deposition in this cube is performed when both pieces come together in females. This drive has 2 loci:

• "Locus" 1, sex chromosomes, has 3 alleles

  • X: Wild-type X chromosome

  • C: X-chromosome carrying a Cas9 construct

  • Y: Wild-type Y chromosome

• Locus 2, autosomal locus, has 4 alleles:

  • W: Wild-type allele

  • G: gRNA allele

  • R: Functional or low-cost resistance allele

  • B: Non-functional or high-cost resistance allele

Usage

cubeSplitDriveX(
  cM = 1,
  chM = 0,
  crM = 0,
  cF = 1,
  chF = 0,
  crF = 0,
  ccF = cF,
  cchF = chF,
  ccrF = crF,
  dW = 0,
  dhW = 0,
  drW = 0,
  ddW = dW,
  ddhW = dhW,
  ddrW = drW,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: Y-linked Split CRISPR Drive with 2 Resistance Alleles

Description

This is a Y-linked version of a split CRISPR drive. At the Y-locus is the Cas9, inherited in a Mendelian fashion. At a second, unlinked, autosomal locus are the gRNAs. When the two loci occur together (i.e. in males), the gRNAs drive, with potential damaged alleles, but the Cas9 remains Mendelian. This drive has 2 loci:

• "Locus" 1, sex chromosomes, has 3 alleles:

  • X: Wild-type X chromosome

  • Y: Wild-type Y chromosome

  • C: Y chromosome with Cas9

• Locus 2, autosomal locus, has 4 alleles:

  • W: Wild-type allele

  • G: gRNA allele

  • R: Functional or low-cost resistance allele

  • B: Non-functional or high-cost resistance allele

Usage

cubeSplitDriveY(
  cM = 1,
  chM = 0,
  crM = 0,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: tGD

Description

The trans-complementing Gene Drive (tGD) is a 1-locus, 2 target site drive. The first target site corresponds to the Cas protein, the second to an effector gene and the gRNAs. There are two sets of gRNAs, because each target site may have different cutting/homing/resistance rates, and each sex can have different rates for all of those things. Additionally, the parent that you receive your Cas from dictates its efficiency. Therefor, this construct has 5 alleles at the first locus and 4 alleles at the second.

• Locus 1

  • W: Wild-type

  • P: Paternal Cas9

  • M: Maternal Cas9

  • R: Resistant allele 1

  • B: Resistant allele 2

• Locus 2

  • W: Wild-type

  • G: gRNAs

  • R: Resistant 1

  • B: Resistant 2

Usage

cubeTGD(
  cM1 = 0,
  cM2 = 0,
  cP1 = 0,
  cP2 = 0,
  hM1 = 0,
  hM2 = 0,
  hP1 = 0,
  hP2 = 0,
  rM1 = 0,
  rM2 = 0,
  rP1 = 0,
  rP2 = 0,
  crM = 0,
  crP = 0,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Details

This drive corresponds to the transcomplementing gene drive developed by the Gantz and Bier lab.

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: tGDX

Description

The trans-complementing Gene Drive (tGD) is a 1-locus, 2 target site drive. The first target site corresponds to the Cas protein, the second to an effector gene and the gRNAs. There are two sets of gRNAs, because each target site may have different cutting/homing/resistance rates, and each sex can have different rates for all of those things. Additionally, the parent that you receive your Cas from dictates its efficiency. Therefor, this construct has 6 alleles at the first locus and 5 alleles at the second.

• Locus 1

  • W: Wild-type

  • P: Paternal Cas9

  • M: Maternal Cas9

  • R: Resistant allele 1

  • B: Resistant allele 2

  • Y: Y allele

• Locus 2

  • W: Wild-type

  • G: gRNAs

  • R: Resistant 1

  • B: Resistant 2

  • Y: Y allele

Usage

cubeTGDX(
  cM1 = 0,
  cM2 = 0,
  cP1 = 0,
  cP2 = 0,
  hM1 = 0,
  hM2 = 0,
  hP1 = 0,
  hP2 = 0,
  rM1 = 0,
  rM2 = 0,
  rP1 = 0,
  rP2 = 0,
  crM = 0,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Details

This drive corresponds to the transcomplementing gene drive developed by the Gantz and Bier lab.

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: 2 Locus Maternal-Toxin/Zygotic-Antidote System

Description

This function creates a 2 locus maternal-toxin/zygotic-antidote system. This is similar to the construct called UDmel. There is no resistance generation in this model.
This drive has 2 unlinked alleles, 1 allele each at 2 loci:

• A: Maternal-toxin 1, zygotic-antidote 2

• a: Wild-type at locus A

• B: Maternal-toxin 2, zygotic-antidote 1

• b: Wild-type at locus B

Usage

cubeTwoLocusTA(
  TAEfficacy = 1,
  TBEfficacy = 1,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: Wolbachia

Description

This function creates an inheritance cube to model a Wolbachia infection. Wolbachia is a parasite that can infect mosquitoes. It biases its inheritance through cytoplasmic incompatibility.
This drive has 2 alleles at 1 locus:

• W: has Wolbachia

• w: does not have Wolbachia

Usage

cubeWolbachia(
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Details

Cytoplasmic Incompatibility:

• male W cross female w -> all offspring die (complete penetrance)

• male w cross female W -> all offspring inherit Wolbachia

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) X-linked with 2 Resistance Allele and Maternal Deposition

Description

This is an X-linked version of the 2 allele cube. It assumes that the construct is on the X chromosome and there is no male homing. It also has maternal deposition, i.e., when the male provides a W allele to a female with an H allele, some portion are cut during oogenesis. If the deposition parameters are zero (*D parameters), this is just an X-linked drive.

Usage

cubeXHomingDeposition(
  cF = 1,
  chF = 0,
  crF = 0,
  dF = 0,
  dhF = 0,
  drF = 0,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: Autosomal X-Shredder

Description

This function creates an inheritance cube to model an autosomal X-Shredder construct. This construct resides on an autosomal chromosome, and chops the X chromosome into many pieces during gametogenesis, destroying the X chromosome. Thus, males may only produce Y gametes and females can become sterile.
This drive has 2 loci:

• Locus 1, the autosomal locus, has 3 alleles:

  • W: Wild-type allele

  • A: Attacking allele, contains the shredder construct

  • B: Broken attacking allele, shredder construct is defunct

• Locus 2, the sex locus, has 3 alleles:

  • X: Wild-type X allele

  • R: X-allele resistant to cleavage

  • Y: Wild-type Y allele

Usage

cubeXShredderMF(
  cM = 1,
  cF = 1,
  crM = 0,
  crF = 0,
  cbM = 0,
  cbF = 0,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Inheritance Cube: Y-Linked X-Shredder

Description

This function creates an inheritance cube to model a Y-linked X-Shredder construct. This construct resides on the Y chromosome, and chops the X chromosome into many pieces during male spermatogenesis, destroying the X chromosome. Thus, males only produce Y gametes.
This drive has 5 alleles at 1 locus:

• X: Wild-type X chromosome

• R: X chromosome resistant to destruction by the shredder construct

• Y: Wild-type Y chromosome

• A: Attacking Y chromosome, a Y chromosome with the shredder construct

• B: Broken Y chromosome, a Y chromosome with a defunct shredder construct

Usage

cubeXShredderY(
  cX = 1,
  crX = 0,
  cB = 0,
  eta = NULL,
  phi = NULL,
  omega = NULL,
  xiF = NULL,
  xiM = NULL,
  s = NULL
)

Arguments

Value

Named list containing the inheritance cube, transition matrix, genotypes, wild-type allele, and all genotype-specific parameters.

Erase all files in a directory

Description

Given a directory path, check that it exists, and if so, delete all its contents.

Usage

eraseDirectory(directory, verbose = TRUE)

Arguments

Examples

## Not run: 
# Path to directory, can tilde expand
myPath <- "~/path/to/write/output"

# Erase directory
#  No return value
eraseDirectory(directory = myPath)

## End(Not run)

Make List of Modified Mosquito Releases

Description

Sets up a release schedule for a single patch, calls basicRepeatedReleases internally.

Usage

generateReleaseVector(driveCube, releasesParameters, nameGenotypes = NULL)

Arguments

Examples

# setup a drive cube, using Mendelian as the example
cube <- cubeMendelian()

# setup release parameter list
#  releasesStart is the time of first release
#  releasesNumber is the number of releases
#  releasesInterval is the number of days between releases
#  releaseProportion is the number of mosquitoes released
relParams <- list(releasesStart = 25, releasesNumber = 1,
                  releasesInterval = 0, releaseProportion = 10)

# generate male releases
mRelVec <- generateReleaseVector(driveCube = cube,
                                 releasesParameters = relParams)

# generate mated female releases
fRelVec <- generateReleaseVector(driveCube = cube,
                                 releasesParameters = relParams,
                                 nameGenotypes = list(c("AA","AA", 10),
                                                      c("AA","aa", 10)))

Get alpha

Description

Return density dependent mortality, see calcDensityDependentDeathRate

Usage

get_alpha_Network(ix)

Arguments

Get beta

Description

Return size of wild-type egg batch

Usage

get_beta_Network()

Get conADF

Description

Return connection where adult female dynamics are written to

Usage

get_conF_Network()

Get conADM

Description

Return connection where adult male dynamics are written to

Usage

get_conM_Network()

Get Element(s) of Drive Cube by Index

Description

Return elements or slices of drive cube. If all NULL return entire cube.

Usage

get_drivecubeindex_Network(fG = NULL, mG = NULL, oG = NULL)

Arguments

Get eta

Description

Get eta

Usage

get_eta_Network(fIdx)

Arguments

Get female Population

Description

Return females (nGenotypes X nGenotypes matrix)

Usage

get_femalePop_Patch()

Get genotypesID

Description

Return character vector of possible genotypes

Usage

get_genotypesID_Network()

Get genotypesN

Description

Return number of possible genotypes

Usage

get_genotypesN_Network()

Get male Population

Description

Return males (nGenotypes vector)

Usage

get_malePop_Patch()

Get muAd

Description

Return adult mortality

Usage

get_muAd_Network()

Get muAq

Description

Return larval mortality, see calcLarvalStageMortalityRate

Usage

get_muAq_Network()

Get nPatch

Description

Return number of patches

Usage

get_nPatch_Network()

Get omega

Description

Return genotype-specific multiplicative modifier of adult mortality

Usage

get_omega_Network()

Get Patch Release Schedule

Description

Return the release schedule for a patch for male or female

Usage

get_patchReleases_Network(patch, sex = "M")

Arguments

Get phi

Description

Return genotype-specific sex ratio at emergence

Usage

get_phi_Network()

Get s

Description

Return genotype-specific fractional reduction(increase) in fertility

Usage

get_s_Network()

Get tNow

Description

Return current simulation time

Usage

get_tNow_Network()

Get Female Viability Mask (tau)

Description

Get Female Viability Mask (tau)

Usage

get_tau_Network(fG = NULL, mG = NULL, oG = NULL)

Arguments

Get timeAq

Description

Return duration of aquatic stages.

Usage

get_timeAq_Network(stage = NULL)

Arguments

Get xiF

Description

Return genotype-specific female pupatory success

Usage

get_xiF_Network()

Get xiM

Description

Return genotype-specific male pupatory success

Usage

get_xiM_Network()

Utility to Imitate ggplot2 Colors

Description

Sample at equally spaced intervals along the color wheel

Usage

ggColUtility(n, alpha = 0.75)

Arguments

Kernels Parameters

Description

A named list containing maximum likelihood fitted parameter values from mosquito dispersal estimates.

Usage

data(kernels)

Format

named list with 5 elements:

lnorm_mean

log mean of log-normal density

lnorm_sd

log standard deviation of log-normal density

gamma_shape

shape parameter of gamma density

gamma_sd

rate parameter of gamma density

exp_rate

rate parameter of exponential density

Movement Matrix: All 2

Description

A movement matrix for simulation with 3 patches.

Usage

data(moveMatAll2)

Format

A matrix with 3 rows and 3 columns:

Patches 1 and 3 are sources for patch 2, which is a sink.

Movement Matrix: Cascade 3

Description

A movement matrix for simulation with 3 patches.

Usage

data(moveMatCascade3)

Format

A matrix with 3 rows and 3 columns:

Mosquitoes in patch 1 have equal probability to stay or move to 2; mosquitoes in patch 2 have equal probability to stay or move to 3; mosquitoes in patch 3 stay there.

Movement Matrix: Diagonal

Description

A movement matrix for simulation with 3 patches.

Usage

data(moveMatDiag)

Format

A matrix with 3 rows and 3 columns:

3 independent patches.

Movement Matrix: Diagonal One City

Description

A movement matrix for simulation with 1 patch.

Usage

data(moveMatDiagOneCity)

Format

A matrix with 1 rows and 1 columns:

A 1 by 1 matrix with entry 1.

Movement Matrix: Die

Description

A movement matrix for simulation with 3 patches.

Usage

data(moveMatDie)

Format

A matrix with 3 rows and 3 columns:

All entries of matrix are 0 for testing that all mosquitoes will be killed.

Movement Matrix: Independent 3

Description

A movement matrix for simulation with 3 patches.

Usage

data(moveMatIndependent3)

Format

A matrix with 3 rows and 3 columns:

Mosquitoes in patch 1 stay with probability 0.975, move to patch 2 with probability 0.025, mosquitoes in patch 2 and 3 stay in their patches.

Movement Matrix: Mixed Spill

Description

A movement matrix for simulation with 3 patches.

Usage

data(moveMatMixedSpil)

Format

A matrix with 3 rows and 3 columns:

Mosquitoes in patch 1 stay with probability 0.999, move to patch 2 with probability 0.001, mosquitoes in patch 2 and 3 stay in their patches.

Movement Matrix: Tale of Two Cities

Description

A movement matrix for simulation with 2 patches.

Usage

data(moveMatTaleOfTwoCities)

Format

A matrix with 2 rows and 2 columns:

Mosquitoes do not move between the two patches.

Movement Matrix: Tri-diagonal

Description

A movement matrix for simulation with 12 patches.

Usage

data(moveMatTriDiagonal)

Format

A matrix with 12 rows and 12 columns:

Tri-diagonal matrix with approximately 0.985 probability on diagonal and rest of probability mass on k-1 and k+1 off-diagonal elements.

Movement Matrix: Triple

Description

A movement matrix for simulation with 3 patches.

Usage

data(moveMatTriple)

Format

A matrix with 3 rows and 3 columns:

All entries of matrix are 1 for testing that mosquitoes will be produced.

Run Simulation

Description

Run multiple simulations on this network

Usage

multRun_Network(verbose = TRUE)

Arguments

Normalise a Numeric Vector

Description

Normalise a numeric vector to sum to one

Usage

normalise(vector)

Arguments

Deterministic Inter-Patch Migration

Description

Deterministic model of interpatch migration from each patch. popFemale/popMale is retrieved from each patch using get_femalePop_Patch/get_malePop_Patch. Migration location is determined from the supplied matrices, private$migrationFemale or private$migrationMale. Final migration status is held in private$fMoveArray or private$mMoveMat.
Batch migration is not used in the deterministic model.

Usage

oneDay_Migration_Deterministic_Network()

Details

This function handles outbound and inbound migration. See MGDrivE-Model, 'Migration' section for more details on how inter-patch migration is handled.

Stochastic Inter-Patch Migration

Description

Stochastic model of interpatch migration from each patch. popFemale/popMale is retrieved from each patch using get_femalePop_Patch/get_malePop_Patch. Migration location is determined from the supplied matrices, private$migrationFemale or private$migrationMale. Migration is modeled as a Multinomial process parameterized by migration location probabilities corresponding to each patch . Movement is sampled from rmultinom.
Batch migration begins as a rbinom sampled from private$migrationBatch$batchProbs.If there is batch migration, the location of migration is sampled uniformly (see sample), parameterized by private$migrationBatch$moveProbs. The amount of each sex that migrations is sampled from rbinom, parameterized by private$migrationBatch$sexProbs.

Usage

oneDay_Migration_Stochastic_Network()

Details

This function handles outbound and inbound migration. See MGDrivE-Model, 'Migration' section for more details on how inter-patch migration is handled.

Run a Single Day on a Network

Description

Runs a single day of simulation on a Network object, handling population dynamics, migration, population update, and output.

Usage

oneDay_Network()

Daily Population Dynamics for a Patch

Description

Run population dynamics (including migration) for this patch.
Performed in this order, see the following for each function:
Adult Death: oneDay_adultDeath_deterministic_Patch or oneDay_adultDeath_stochastic_Patch
Pupa Death/Maturation: oneDay_pupaDM_deterministic_Patch or oneDay_pupaDM_stochastic_Patch
Larva Death/Maturation: oneDay_larvaDM_deterministic_Patch or oneDay_larvaDM_stochastic_Patch
Egg Death/Maturation: oneDay_eggDM_deterministic_Patch or oneDay_eggDM_stochastic_Patch
Pupation: oneDay_pupation_deterministic_Patch or oneDay_pupation_stochastic_Patch
Releases: oneDay_releases_Patch
Mating: oneDay_mating_deterministic_Patch or oneDay_mating_stochastic_Patch
Lay Eggs: oneDay_oviposit_deterministic_Patch or oneDay_oviposit_stochastic_Patch
Release Eggs: oneDay_eggReleases_Patch

Usage

oneDay_PopDynamics_Patch()

Deterministic Adult Survival

Description

Daily adult survival is calculated according to

\overline{\overline{Af_{[t-1]}}} * (1-\mu_{ad}) * \overline{\omega_{m/f}}

, where \mu_{ad} corresponds to adult mortality rate and \overline{\omega_{m/f}} corresponds to genotype-specific male/female mortality effects.

Usage

oneDay_adultDeath_deterministic_Patch()

Stochastic Adult Survival

Description

Daily adult survival is sampled from a binomial distribution where survival probability is given by

(1-\mu_{ad}) * \overline{\omega_m/f}

. \mu_{ad} corresponds to adult mortality rate and \overline{\omega_m/f} corresponds to genotype-specific mortality effects.

Usage

oneDay_adultDeath_stochastic_Patch()

Deterministic Egg Death and Pupation

Description

Daily egg survival is calculated according to

\overline{E_{[t-1]}} * (1-\mu_{aq})

, where \mu_{aq} corresponds to daily non-density-dependent aquatic mortality. Eggs transition into larvae at the end of T_e.
See parameterizeMGDrivE for how these parameters are derived.

Usage

oneDay_eggDM_deterministic_Patch()

Stochastic Egg Death and Pupation

Description

Daily egg survival is sampled from a binomial distribution, where survival probability is given by 1-\mu_{aq}. \mu_{aq} corresponds to daily non-density-dependent aquatic mortality.
Eggs transition into larvae at the end of T_e.
See parameterizeMGDrivE for how these parameters are derived.

Usage

oneDay_eggDM_stochastic_Patch()

Release Eggs in a Patch

Description

Based on this patch's release schedule, generateReleaseVector, this function handles daily egg releases.

Usage

oneDay_eggReleases_Patch()

Initialize Output from Focal Patch

Description

Writes output to the text connections specified in the enclosing Network.

Usage

oneDay_initOutput_Patch()

Deterministic Larva Death and Pupation

Description

Calculate the number of larvae surviving from day to day, given by:

\overline{L_{[t-1]}} * (1-\mu_{aq}) * F(L)

. F(L), the density dependence is calculated as

F(L[t])=\Bigg(\frac{\alpha}{\alpha+\sum{\overline{L[t]}}}\Bigg)^{1/T_l}

. See parameterizeMGDrivE for how these parameters are derived. Pupation has no parameters, so the final day of larvae naturally enter the pupal state.

Usage

oneDay_larvaDM_deterministic_Patch()

Stochastic Larva Death and Pupation

Description

The daily number of larvae surviving is drawn from a binomial distribution, where survival probability is given by

(1-\mu_{aq}) * F(L)

. F(L), the density dependence is calculated as

F(L[t])=\Bigg(\frac{\alpha}{\alpha+\sum{\overline{L[t]}}}\Bigg)^{1/T_l}

. See parameterizeMGDrivE for how these parameters are derived. Pupation has no parameters, so the final day of larvae naturally enter the pupal state.

Usage

oneDay_larvaDM_stochastic_Patch()

Deterministic Mating

Description

Mating is calculated as the outer product of newly emerging adult females and all-current adult males, modulated by \overline{\overline{\eta}}, the genotype-specific male mating fitness. \overline{\overline{\eta}} corresponds to all female (rows) and male (columns) genotypes, to perform any type of assortative mating.
If there are no adult males, the unmated females experience one day of death, calculated as

\overline{Af_t} * (1-\mu_{ad}) * \overline{\omega_f}

, and remain unmated until tomorrow.

Usage

oneDay_mating_deterministic_Patch()

Stochastic Mating

Description

Mating for each newly emerging adult female genotype is sampled from a multinomial distribution with probabilities equal to the adult male population vector multiplied by \overline{\overline{\eta}}, the genotype-specific male mating fitness. \overline{\overline{\eta}} corresponds to all female (rows) and male (columns) genotypes, to perform any type of assortative mating.
If there are no adult males, the unmated females experience one day of death, sampled from a binomial distribution parameterized by

(1-\mu_{ad}) * \overline{\omega_f}

, and remain unmated until tomorrow.

Usage

oneDay_mating_stochastic_Patch()

Inbound Migration

Description

Accumulate all inbound migration to this patch.

Usage

oneDay_migrationIn_Patch(maleIn, femaleIn)

Arguments

Deterministic Oviposition

Description

Calculate the number of eggs oviposited by female mosquitoes following:

\overline{O(T_x)} = \sum_{j=1}^{n} \Bigg( \bigg( (\beta*\overline{s} * \overline{ \overline{Af_{[t]}}}) * \overline{\overline{\overline{Ih}}} \bigg) * \Lambda \Bigg)^{\top}_{ij}

Usage

oneDay_oviposit_deterministic_Patch()

Stochastic Oviposition

Description

Calculate the number of eggs oviposited by female mosquitoes following:

\overline{O(T_x)} = \sum_{j=1}^{n} \Bigg( \bigg( (\beta*\overline{s} * \overline{ \overline{Af_{[t]}}}) * \overline{\overline{\overline{Ih}}} \bigg) * \Lambda \Bigg)^{\top}_{ij}

The deterministic result for number of eggs is used as the mean of a Poisson-distributed number of actual eggs oviposited.

Usage

oneDay_oviposit_stochastic_Patch()

Deterministic Pupa Death and Pupation

Description

Daily pupa survival is calculated according to

\overline{P_{[t-1]}} * (1-\mu_{aq})

, where \mu_{aq} corresponds to daily non-density-dependent aquatic mortality.
See parameterizeMGDrivE for how these parameters are derived.

Usage

oneDay_pupaDM_deterministic_Patch()

Stochastic Pupa Death and Pupation

Description

Daily pupa survival is sampled from a binomial distribution, where survival probability is given by

1-\mu_{aq}

. \mu_{aq} corresponds to daily non-density-dependent aquatic mortality.
See parameterizeMGDrivE for how these parameters are derived.

Usage

oneDay_pupaDM_stochastic_Patch()

Deterministic Pupation

Description

Pupa first undergo one extra day of survival, calculated as

\overline{P_{[t-1]}} * (1-\mu_{ad})

. This is an artifact of the conversion from continuous to discrete time (as mentioned in the original Hancock paper this model is derived from).
Then, pupation into adult males is calculated as

(1-\overline{\phi}) * \overline{P_{[t]}}

and into adult females as

\overline{\phi} * \overline{P_{[t]}}

Usage

oneDay_pupation_deterministic_Patch()

Stochastic Pupation

Description

Pupa first undergo one extra day of survival, calculated as a binomial over

\overline{P_{[t-1]}} * (1-\mu_{ad})

. This is an artifact of the conversion from continuous to discrete time (as mentioned in the original Hancock paper this model is derived from).
Then, pupation is sampled from a binomial, where (1-\overline{\phi}) is the genotype-specific probability of becoming male, and \overline{\phi} is the genotype-specific of becoming female.

Usage

oneDay_pupation_stochastic_Patch()

Release Male/Female/Mated-Female Mosquitoes in a Patch

Description

Based on this patch's release schedule, generateReleaseVector, this function handles daily releases.

Usage

oneDay_releases_Patch()

Write Output from Focal Patch

Description

Writes output to the text connections specified in the enclosing Network.

Usage

oneDay_writeOutput_Patch()

Run Simulation

Description

Run a single simulation on this network.

Usage

oneRun_Network(verbose = TRUE)

Arguments

parameterizeMGDrivE

Description

Generate parameters for simulation on a Network. Parameters include: average generation time g, population growth rate R_{m}, aquatic mortality \mu_{Aq}, and aquatic survival \theta_{Aq}, which are shared between patches and calculated by calcAverageGenerationTime, calcPopulationGrowthRate, and calcLarvalStageMortalityRate.
Patch-specific parameters \alpha and L_{eq} are calculated for each patch by calcDensityDependentDeathRate and calcLarvalPopEquilibrium.

Usage

parameterizeMGDrivE(
  runID = 1L,
  nPatch,
  simTime,
  sampTime = 1L,
  tEgg = 1L,
  tLarva = 14L,
  tPupa = 1L,
  beta = 32,
  muAd = 0.123,
  popGrowth = 1.096,
  AdPopEQ,
  LarPopRatio,
  AdPopRatio_F,
  AdPopRatio_M,
  inheritanceCube
)

Arguments

Examples

# using default parameters for 2 patches
#  using different population sizes for patches
simPars <- parameterizeMGDrivE(nPatch = 2, simTime = 365,
                               AdPopEQ = c(100,200), inheritanceCube = cubeMendelian())

Plot

Description

Plots several traces from MGDrivE, assuming each set is another repetition from the same experiment.
Given the readDir, this function assumes the follow file structure:

• readDir

  • repetition 1

    • patch 1

    • patch 2

    • patch 3

  • repetition 2

    • patch 1

    • patch 2

    • patch 3

  • repetition 3

  • repetition 4

  • ...

Usage

plotMGDrivEMult(readDir, whichPatches = NULL, totalPop = FALSE,
                       nonZeroGen = FALSE, lwd = 0.75, alpha = 0.75)

Arguments

Details

This function plots output from one run or one set of runs after being analyzed. Setting totalPop to FALSE keeps it from plotting the total population. NonZeroGen accounts for genotypes that could exist, but are not created in the simulation. Default is FALSE, as this is easier to read on a plot.

Examples

## Not run: 
# Requires the user to have run MGDrivE, logically stochastic, analyzed
#  the data, and stored it in the directory shown below.
# See vignette for complete example

# Folder where single run is stored
fPath <- "path/to/data/containing/folder"

# plot output to see effect
plotMGDrivEMult(readDir=fPath,totalPop = TRUE,lwd=3.5,alpha=1)

## End(Not run)

Plot

Description

Plots one run from MGDrivE

Usage

plotMGDrivESingle(readDir, whichPatches = NULL, totalPop = FALSE,
                         nonZeroGen = FALSE, lwd = 0.75, alpha = 0.75)

Arguments

Details

This function plots output from one run or one set of runs after being analyzed. Setting totalPop to FALSE keeps it from plotting the total population. NonZeroGen accounts for genotypes that could exist, but are not created in the simulation. Default is FALSE, as this is easier to read on a plot.

Examples

## Not run: 
# Requires the user to have run MGDrivE, deterministic or stochastic, analyzed
#  the data, and stored it in the directory shown below.
# See vignette for complete example

# Folder where single run is stored
fPath <- "path/to/data/containing/folder"

# plot output to see effect
plotMGDrivESingle(readDir=fPath,totalPop = TRUE,lwd=3.5,alpha=1)

## End(Not run)

Quantiles Function

Description

Calculate the given quantiles of a matrix.

Usage

quantileC(Trials, Probs)

Arguments

Details

This function calculates the given quantiles over the rows of an integer matrix. It uses method 8 of the stat::quantiles() function. It gives the same result, to numerical accuracy, and is designed to handle matrix input. It is only designed to work on integer matrices!

Value

Numeric Matrix

Dirichlet Distribution

Description

Make a single draw from a Dirichlet distribution with the shape parameter one.

Usage

rDirichlet(migrationPoint)

Arguments

Reset Network

Description

Reset a Network between runs, useful for Monte Carlo simulation. This calls reset_Patch on each patch and resets tNow = 2 and increments the runID.

Usage

reset_Network(verbose = TRUE)

Arguments

Reset Patch to Initial Conditions

Description

Resets a patch to its initial configuration so that a new one does not have to be created and allocated in the network (for Monte Carlo simulation).

Usage

reset_Patch(verbose = TRUE)

Arguments

Retrieve Output

Description

Read in output from directory. The resulting object will be a nested list; outermost nesting dimension indexes runID, within runID elements are split by sex and innermost nesting is over patches.

Usage

retrieveOutput(readDir, verbose = TRUE)

Arguments

Value

Nested List

Examples

## Not run: 
# Example assumes user has run and analyzed MGDrivE.
#  See vignette for examples of how to do that.

# set read directory
fPath <- "path/to/split/aggregated/output"

# read in data as nested lists
dataList <- retrieveOutput(readDir = fPath)

## End(Not run)

Set Network Pointer

Description

Set a reference to the enclosing Network object

Usage

set_NetworkPointer_Patch(NetworkPointer)

Arguments

Set Initial Population

Description

This hidden function distributes the population at time 0 in the steady-state conformation. This involves finding the number of mosquitoes in each day of the aquatic stages, and then splitting adults into male and female. Each stage is appropriately split amongst the initial population genotypes (see parameterizeMGDrivE). It internally calls calcLarvalDist to determine the distribution of larvae before setting the eggs and pupa from that.

Usage

set_initialPopulation_Patch(
  adultEQ = adultEQ,
  larvalEQ = larvalEQ,
  adultRatioF = adultRatioF,
  adultRatioM = adultRatioM,
  larvalRatio = larvalRatio,
  timeAq = timeAq,
  muAq = muAq,
  alpha = alpha
)

Arguments

Set Initial Population Deterministic

Description

Calls set_initialPopulation_Patch to initialize a steady-state population distribution.

Usage

set_population_deterministic_Patch(
  adultEQ = adultEQ,
  larvalEQ = larvalEQ,
  adultRatioF = adultRatioF,
  adultRatioM = adultRatioM,
  larvalRatio = larvalRatio,
  timeAq = timeAq,
  muAq = muAq,
  alpha = alpha
)

Arguments

Set Initial Population Stochastic

Description

Calls set_initialPopulation_Patch to initialize a steady-state population distribution. Populations are then rounded to integer values.

Usage

set_population_stochastic_Patch(
  adultEQ = adultEQ,
  larvalEQ = larvalEQ,
  adultRatioF = adultRatioF,
  adultRatioM = adultRatioM,
  larvalRatio = larvalRatio,
  timeAq = timeAq,
  muAq = muAq,
  alpha = alpha
)

Arguments

Setup MGDrivE

Description

Initialize methods in Patch to run deterministic or stochastic simulations. This sets internal function definitions so that oneRun_Network and multRun_Network run either deterministic or stochastic functions.

Usage

setupMGDrivE(stochasticityON = FALSE, verbose = TRUE)

Arguments

Examples

# run deterministic MGDrivE
setupMGDrivE(stochasticityON = FALSE)

# run stochastic MGDrivE
setupMGDrivE(stochasticityON = TRUE)

Split Output by Patch

Description

Split output into multiple files by patches.

Usage

splitOutput(readDir, writeDir = NULL, remFile = TRUE, verbose = TRUE)

Arguments

Examples

## Not run: 
# This example assumes user has already run MGDrivE and generated output.
#  If that's untree, see vignette for complete example
fPath <- "path/to/data/containing/folder"
oPath <- "path/to/write/output"

# split data by patch, keep original files
#  no return value
splitOutput(readDir = fPath, writeDir = oPath, remFile = FALSE)

# Alternatively, remove the original files and write new ones in their place
fPath <- "path/to/data/containing/folder"

splitOutput(readDir = fPath, writeDir = NULL, remFile = TRUE)

## End(Not run)