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Our model uses this feature of probabilistic Boolean networks to represent two kinds of stochasticity. The first is the aforementioned uncertainty about the correct function to apply. By employing different possible functions and varying their probability we can see which of them fits the known data better. The second way how we use the probabilistic functions is to model dynamic features of the system. The single processes that influence a variable are split into different functions and we assign each of them a probability. That way we can adjust the probability of the activation of a variable under certain conditions instead of assigning it one fixed value. We can also easily split activation and inactivation into different functions. Depending on the influencing variables the probabilities of the state of the variable change.

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Our model uses this feature of probabilistic Boolean networks to represent two kinds of stochasticity. The first is the aforementioned uncertainty about the correct function to apply. By employing different possible functions and varying their probability we can see which of them fits the known data better. The second way how we use the probabilistic functions is to model dynamic features of the system. The single processes that influence a variable are split into different functions and we assign each of them a probability. That way we can adjust the probability of the activation of a variable under certain conditions instead of assigning it one fixed value. We can also easily split activation and inactivation into different functions. Depending on the influencing variables the probabilities of the state of the variable change.

Our model uses this feature of probabilistic Boolean networks to represent two kinds of stochasticity. The first is the aforementioned uncertainty about the correct function to apply. By employing different possible functions and varying their probability we can see which of them fits the known data better. The second way how we use the probabilistic functions is to model dynamic features of the system. The single processes that influence a variable are split into different functions and we assign each of them a probability. That way we can adjust the probability of the activation of a variable under certain conditions instead of assigning it one fixed value. We can also easily split activation and inactivation into different functions. Depending on the influencing variables the probabilities of the state of the variable change.

Boolean models are a drastic simplification of biological reality, but they have produced valuable results in the past and are especially suited for developmental gene regulatory networks (e.g., Macía et al., 2009). Using them it is possible to model large networks and study their dynamic behavior while leaving out details such as kinetic constants that are usually unknown in real world examples (Kauffman, 2004). Boolean networks limit the state of a gene to either ON or OFF and describe connections between the genes by using logical operators, e.g., AND, OR, NOT (generally written as ∧, ∨, and (¬ in mathematical formulas). For example if two transcription factors A and B are needed to activate gene C this would translate to the logical function C(t + 1) = A(t)∧B(t).

Our model uses this feature of probabilistic Boolean networks to represent two kinds of stochasticity. The first is the aforementioned uncertainty about the correct function to apply. By employing different possible functions and varying their probability we can see which of them fits the known data better. The second way how we use the probabilistic functions is to model dynamic features of the system. The single processes that influence a variable are split into different functions and we assign each of them a probability. That way we can adjust the probability of the activation of a variable under certain conditions instead of assigning it one fixed value. We can also easily split activation and inactivation into different functions. Depending on the influencing variables the probabilities of the state of the variable change.

Our model uses this feature of probabilistic Boolean networks to represent two kinds of stochasticity. The first is the aforementioned uncertainty about the correct function to apply. By employing different possible functions and varying their probability we can see which of them fits the known data better. The second way how we use the probabilistic functions is to model dynamic features of the system. The single processes that influence a variable are split into different functions and we assign each of them a probability. That way we can adjust the probability of the activation of a variable under certain conditions instead of assigning it one fixed value. We can also easily split activation and inactivation into different functions. Depending on the influencing variables the probabilities of the state of the variable change.

Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks

The regulation of gene expression is achieved through gene regulatory networks (GRNs) in which collections of genes interact with one another and other substances in a cell. In order to understand the underlying function of organisms, it is necessary to study the behavior of genes in a gene regulatory network context. Several computational approaches are available for modeling gene regulatory networks with different datasets. In order to optimize modeling of GRN, these approaches must be compared and evaluated in terms of accuracy and efficiency.

Results
In this paper, two important computational approaches for modeling gene regulatory networks, probabilistic Boolean network methods and dynamic Bayesian network methods, are compared using a biological time-series dataset from the Drosophila Interaction Database to construct a Drosophila gene network. A subset of time points and gene samples from the whole dataset is used to evaluate the performance of these two approaches.