# Obtain the reached states Obtains the reached states after simulating a Boolean network for a specified number of time-steps. ```r get_reached_states(net, method = c("BNp", "SDDS", "PEW"), params, steps, repeats = NULL, initial_states = NULL, asynchronous = TRUE, update_prob = NULL) ``` ## Arguments - `net`: A network structure of the class `BooleanNetwork` from the `BoolNet` package. - `method`: The parameterization method to be used. Options are: * `"BNp"`: Boolean network with perturbations. * `"SDDS"`: Stochastic discrete dynamical systems. * `"PEW"`: Boolean network with probabilistic edge weights. Each method requires a different format for the `params` argument. - `params`: The parameter values depending on `method`: * For `method = "BNp"`, a single vector of probabilities, equal in length to the number of network nodes. * For `method = "SDDS"`, a list of four equal-length vectors of probabilities: `p00`, `p01`, `p10`, and `p11`, each equal in length to the number of network nodes. * For `method = "PEW"`, a list of two equal-length vectors of probabilities: `p_on` and `p_off`, each as long as the number of network edges, ordered according to `extract_edges`. - `steps`: The number of time-steps (non-negative integer) to simulate the network. - `repeats`: The number of repeats (positive integer). If two or more initial states are provided via `initial_states`, this argument is ignored. If `NULL` (default), then `initial_states` should not be `NULL`. - `initial_states`: The set of initial states as a matrix (where each row corresponds to the binary form of a state) or a vector (for the binary form of a single initial state). The number of matrix columns (or the length of the vector) should match the number of network nodes. The order of the nodes in the columns (or vector) is considered the same as `net$genes`. If `NULL` (default), initial states are chosen randomly for `repeats` number of times based on a uniform distribution, requiring `repeats` not to be `NULL`. - `asynchronous`: If `TRUE`, the asynchronous update scheme is used, where a single node is updated at each time-step. In this case, `update_prob` indicates update probabilities. If `FALSE`, the synchronous update scheme is utilized. - `update_prob`: The probability of updating each variable (node) in each time-step when `asynchronous = TRUE`. It should be a vector of probabilities for each of the nodes which sums up to one. If `NULL` (default), nodes are updated randomly based on a uniform distribution. If `asynchronous = FALSE`, this argument is ignored. ## Details This function returns the reached states (the states in the last time-step) after simulating a network for `steps` time-steps and repeating it for `repeats` number of times. If `initial_states` is `NULL`, then the initial states are chosen randomly based on a uniform distribution for `repeats` number of times, resulting in `repeats` number of reached states. If two or more initial states are provided by the user, then the `repeats` argument is ignored, and one reached state is returned for each initial state. If `repeats` is `NULL`, the number of returned reached states equals the number of initial states (one reached state for each initial state). The arguments `repeats` and `initial_states` should not both be `NULL` simultaneously. ## Returns A matrix where each row is the binary form of a reached state, and each column corresponds to a network node. The order of the nodes in the columns is the same as `net$genes`. ## References Golinelli, O., & Derrida, B. (1989). Barrier heights in the Kauffman model. **Journal De Physique**, 50(13), 1587-1601. Shmulevich, I., Dougherty, E. R., & Zhang, W. (2002). Gene perturbation and intervention in probabilistic Boolean networks. **Bioinformatics**, 18(10), 1319-1331. Trairatphisan, P., Mizera, A., Pang, J., Tantar, A. A., Schneider, J., & Sauter, T. (2013). Recent development and biomedical applications of probabilistic Boolean networks. **Cell communication and signaling**, 11, 1-25. Murrugarra, D., Veliz-Cuba, A., Aguilar, B., Arat, S., & Laubenbacher, R. (2012). Modeling stochasticity and variability in gene regulatory networks. **EURASIP Journal on Bioinformatics and Systems Biology**, 2012, 1-11. Deritei, D., Kunšič, N., & Csermely, P. (2022). Probabilistic edge weights fine-tune Boolean network dynamics. **PLoS Computational Biology**, 18(10), e1010536. ## Examples ```r # >>>>>>>>>>>>>>>> Load network and generate random initial states <<<<<<<<<<<<<<<<< # Load the example network data(lac_operon_net) # Generate a single initial state initial_state <- sample(c(0, 1), length(lac_operon_net$genes), replace = TRUE) # Generate multiple (10) initial states initial_states <- matrix(sample(c(0, 1), 10 * length(lac_operon_net$genes), replace = TRUE), nrow = 10, ncol = length(lac_operon_net$genes)) # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Method: BNp <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< # Define the parameters for the BNp method params <- rep(0.05, length(lac_operon_net$genes)) # No initial states are provided reached_states <- get_reached_states(lac_operon_net, method = "BNp", params = params, steps = 100, repeats = 10) # A single initial state is provided reached_states <- get_reached_states(lac_operon_net, method = "BNp", params = params, steps = 100, initial_states = initial_state, repeats = 10) # Multiple initial states are provided reached_states <- get_reached_states(lac_operon_net, method = "BNp", params = params, steps = 100, initial_states = initial_states) # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Method: SDDS <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< # Define the parameters for the SDDS method props <- rep(0.95, length(lac_operon_net$genes)) params <- list(p00 = props, p01 = props, p10 = props, p11 = props) # No initial states are provided reached_states <- get_reached_states(lac_operon_net, method = "SDDS", params = params, steps = 100, repeats = 10) # A single initial state is provided reached_states <- get_reached_states(lac_operon_net, method = "SDDS", params = params, steps = 100, initial_states = initial_state, repeats = 10) # Multiple initial states are provided reached_states <- get_reached_states(lac_operon_net, method = "SDDS", params = params, steps = 100, initial_states = initial_states) # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Method: PEW <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< # Extract edges from the network edges <- extract_edges(lac_operon_net) # Define the parameters for the PEW method p_on <- runif(nrow(edges)) p_off <- runif(nrow(edges)) params <- list(p_on = p_on, p_off = p_off) # No initial states are provided reached_states <- get_reached_states(lac_operon_net, method = "PEW", params = params, steps = 100, repeats = 10) # A single initial state is provided reached_states <- get_reached_states(lac_operon_net, method = "PEW", params = params, steps = 100, initial_states = initial_state, repeats = 10) # Multiple initial states are provided reached_states <- get_reached_states(lac_operon_net, method = "PEW", params = params, steps = 100, initial_states = initial_states) ```

get_reached_states function