Abstract
BackgroundThere exist several computational tools which allow for the optimisation and inference of biological networks using a Boolean formalism. Nevertheless, the results from such tools yield only limited quantitative insights into the complexity of biological systems because of the inherited qualitative nature of Boolean networks.ResultsWe introduce optPBN, a Matlab-based toolbox for the optimisation of probabilistic Boolean networks (PBN) which operates under the framework of the BN/PBN toolbox. optPBN offers an easy generation of probabilistic Boolean networks from rule-based Boolean model specification and it allows for flexible measurement data integration from multiple experiments. Subsequently, optPBN generates integrated optimisation problems which can be solved by various optimisers.In term of functionalities, optPBN allows for the construction of a probabilistic Boolean network from a given set of potential constitutive Boolean networks by optimising the selection probabilities for these networks so that the resulting PBN fits experimental data. Furthermore, the optPBN pipeline can also be operated on large-scale computational platforms to solve complex optimisation problems. Apart from exemplary case studies which we correctly inferred the original network, we also successfully applied optPBN to study a large-scale Boolean model of apoptosis where it allows identifying the inverse correlation between UVB irradiation, NFκB and Caspase 3 activations, and apoptosis in primary hepatocytes quantitatively. Also, the results from optPBN help elucidating the relevancy of crosstalk interactions in the apoptotic network.SummaryThe optPBN toolbox provides a simple yet comprehensive pipeline for integrated optimisation problem generation in the PBN formalism that can readily be solved by various optimisers on local or grid-based computational platforms. optPBN can be further applied to various biological studies such as the inference of gene regulatory networks or the identification of the interaction's relevancy in signal transduction networks.
Highlights
The Boolean network (BN) modelling framework was first introduced by Kauffmann in 1969 for the study of gene regulatory networks [1]
Based on the results obtained from the optimisation of Schlatter’s model in the probabilistic Boolean network (PBN) format, we quantitatively identified an inverse correlation between UVB irradiation, nuclear factor kappa-B (NFkB) and Caspase 3 activations, and apoptotic activity which could not be demonstrated in the original article due to the qualitative limitation of the Boolean network framework
We present optPBN, a novel optimisation toolbox which provides a simple yet comprehensive pipeline for the generation of integrated optimisation problems in the PBN formalism which can readily be solved by various optimisers on local or grid computational platforms
Summary
The Boolean network (BN) modelling framework was first introduced by Kauffmann in 1969 for the study of gene regulatory networks [1]. BNs only provide a very limited quantitative insight into biological systems due to their inherent qualitative nature of state and time. The PBN formalism allows multiple Boolean functions to be assigned to a certain node with corresponding selection probabilities. This assignment forms a collection of Boolean networks (so-called constituent networks) that are being randomly chosen in accordance with their selection probabilities throughout the course of a simulation of the PBN. The results from such tools yield only limited quantitative insights into the complexity of biological systems because of the inherited qualitative nature of Boolean networks
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