ASSA-PBN: A Toolbox for Probabilistic Boolean Networks

Abstract

As a well-established computational framework, probabilistic Boolean networks (PBNs) are widely used for modelling, simulation, and analysis of biological systems. To analyze the steady-state dynamics of PBNs is of crucial importance to explore the characteristics of biological systems. However, the analysis of large PBNs, which often arise in systems biology, is prone to the infamous state-space explosion problem. Therefore, the employment of statistical methods often remains the only feasible solution. We present ${\mathsf{ASSA-PBN}}$ , a software toolbox for modelling, simulation, and analysis of PBNs. ${\mathsf{ASSA-PBN}}$ provides efficient statistical methods with three parallel techniques to speed up the computation of steady-state probabilities. Moreover, particle swarm optimisation (PSO) and differential evolution (DE) are implemented for the estimation of PBN parameters. Additionally, we implement in-depth analyses of PBNs, including long-run influence analysis, long-run sensitivity analysis, computation of one-parameter profile likelihoods, and the visualization of one-parameter profile likelihoods. A PBN model of apoptosis is used as a case study to illustrate the main functionalities of ${\mathsf{ASSA-PBN}}$ and to demonstrate the capabilities of ${\mathsf{ASSA-PBN}}$ to effectively analyse biological systems modelled as PBNs.

Controlling Large Boolean Networks with Temporary and Permanent Perturbations

“…It starts initially in a state s 0 and its state changes in every time step according to the update functions f . The updating may happen in various ways [23,24]. Every such way of updating gives rise to a different dynamics for the network.…”

“…The results are compared with the minimal instantaneous control method developed in [19]. These three algorithms are implemented as part of the software ASSA-PBN [23]. All the experiments are performed on a computer with a CPU of Intel Core i7 @3.1 GHz and 8 GB of DDR3 RAM.…”

Sequential Reprogramming of Boolean Networks Made Practical

“…For the attractor-based sequential reprogramming, the maximal number of perturbations allowed is set as the number of perturbations required by the minimal one-step reprogramming; and we assume all the nodes can be perturbed, thus U = ∅ due to the lack of relevant biological knowledge. The three methods are implemented as part of the software tool ASSA-PBN [14]. All the experiments are performed on a computer with a CPU of Intel Core i7 @3.1 GHz and 8 GB of DDR3 RAM 11 .…”

Algorithms for the Sequential Reprogramming of Boolean Networks

“…To demonstrate the efficiency and effectiveness of the temporary sequential control method, we apply it to three reallife biological networks. The method is implemented in ASSA-PBN [13], based on the model checker MCMAS [11] to encode BNs into BDDs. All the experiments are performed on a computer (MacBook Pro), which has a CPU of Intel Core i7 @3.1 GHz and 8 GB of DDR3 RAM.…”

“…Initialize a vector ρ to store the paths. 13: ρ = COMPUTE PATHS(1, A s , ρ, W, P); // Algorithm 3 14: TS ρ = EXTRACT PATHS(ρ, TS ). 15: break; 16: if flag == False then 17: There is no temporary sequential reprogramming paths within k perturbations.…”