FALCON: A Toolbox for the Fast Contextualisation of Logical Networks.

Motivation: Mathematical modelling of regulatory networks allows for the discovery of knowledge at the system level. However, existing modelling tools are often computation-heavy and do not offer intuitive ways to explore the model, to test hypotheses or to interpret the results biologically. Results: We have developed a computational approach to contextualise logical models of regulatory networks with biological measurements based on a probabilistic description of rule-based interactions between the different molecules. Here, we propose a Matlab toolbox, FALCON, to automatically and efficiently build and contextualise networks, which includes a pipeline for conducting parameter analysis, knockouts, and easy and fast model investigation. The contextualised models could then provide qualitative and quantitative information about the network and suggest hypotheses about biological processes. Availability and implementation: FALCON is freely available for non-commercial users on GitHub under the GPLv3 licence. The toolbox, installation instructions, full documentation and test datasets are available at https://github.com/sysbiolux/FALCON . FALCON runs under Matlab (MathWorks) and requires the Optimization Toolbox. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online

A Study of the PDGF Signaling Pathway with PRISM

In this paper, we apply the probabilistic model checker PRISM to the analysis of a biological system -- the Platelet-Derived Growth Factor (PDGF) signaling pathway, demonstrating in detail how this pathway can be analyzed in PRISM. We show that quantitative verification can yield a better understanding of the PDGF signaling pathway.Comment: In Proceedings CompMod 2011, arXiv:1109.104

Studying Signal Transduction Networks with a Probabilistic Boolean Network Approach

In recent years, various modelling approaches in systems biology have been applied for the study and analysis of signal transduction networks. However, each modelling approach has its inherent advantages and disadvantages, so the choice has to be made based on research objectives and types of data. In this PhD dissertation, we propose probabilistic Boolean network (PBN) as one of the suitable modelling approaches for studying signal transduction networks with steady-state data. The steady-state distribution of molecular states in PBN can be correlated to the steady-state proteomic profiles generated from wet-lab experiments. In addition, the relevance of interactions within signalling networks can be assessed through the optimised selection probabilities. These features make PBNs ideal for describing the properties of signal transduction networks at steady-state with some uncertainty on network topologies. To investigate the applicability of PBNs for the study of signal transduction networks, we developed optPBN, an optimisation and analysis toolbox in the PBN framework. We demonstrated that optPBN can be applied to optimise a large-scale apoptotic network with 96 nodes and 105 interactions. Also, it allows for network contextualisation in a physiological context of primary hepatocytes through the analysis on optimised selection probabilities. Similarly, we also applied optPBN to study deregulated signal transduction networks in pathological contexts, i.e. the PDGF signalling in gastrointestinal stromal tumour (GIST) and the L-plastin signalling in breast cancer cell lines. By integrating prior information on network topology from literature with context-specific experimental data, contextualised PBNs can be derived which in turn provide additional insights into biological systems such as the importance of certain crosstalk interactions and the comparative signal flows at steady-state in non-metastatic versus metastatic cancer cell lines. In addition to the applications on fundamental research, we also explored the applications of PBNs in a pharmaceutical setting where detailed mechanistic models are usually used. Here, we applied optPBN as a tool for network ontextualisation. A proof-of-concept example on a small model demonstrated that optPBN helped to pre-select the suitable network structure according to the provided experimental data prior to the building and optimisation of detailed mechanistic models. Such application is foreseen to be applied in a pharmaceutical setting and to explore additional applications such as combinatorial drugs’ effect and toxicity screening

A Probabilistic Boolean Network Approach for the Analysis of Cancer-Specific Signalling: A Case Study of Deregulated PDGF Signalling in GIST.

BACKGROUND: Signal transduction networks are increasingly studied with mathematical modelling approaches while each of them is suited for a particular problem. For the contextualisation and analysis of signalling networks with steady-state protein data, we identified probabilistic Boolean network (PBN) as a promising framework which could capture quantitative changes of molecular changes at steady-state with a minimal parameterisation. RESULTS AND CONCLUSION: In our case study, we successfully applied the PBN approach to model and analyse the deregulated Platelet-Derived Growth Factor (PDGF) signalling pathway in Gastrointestinal Stromal Tumour (GIST). We experimentally determined a rich and accurate dataset of steady-state profiles of selected downstream kinases of PDGF-receptor-alpha mutants in combination with inhibitor treatments. Applying the tool optPBN, we fitted a literature-derived candidate network model to the training dataset consisting of single perturbation conditions. Model analysis suggested several important crosstalk interactions. The validity of these predictions was further investigated experimentally pointing to relevant ongoing crosstalk from PI3K to MAPK signalling in tumour cells. The refined model was evaluated with a validation dataset comprising multiple perturbation conditions. The model thereby showed excellent performance allowing to quantitatively predict the combinatorial responses from the individual treatment results in this cancer setting. The established optPBN pipeline is also widely applicable to gain a better understanding of other signalling networks at steady-state in a context-specific fashion

A balancing act: Parameter estimation for biological models with steady-state measurements

Problem statement. Constructing a computational model for a biological sys-tem consists of two main steps: (1) specifying the model structure and (2) deter-mining the values for the parameters of the model. Usually, the model structure is represented in the form of a biochemical reaction network and the parameters are the reaction rate constants. The values of the reaction rates can be determined by fitting the model to experimental data by performing parameter estimation. However, the question remains whether the experimental data allow for unique identification of the parameters. To address this problem, one could perform a number of independent parameter estimations and investigate the range of obtained values among those parameter sets that result in a good fit. From the correlation of the obtained parameter sets one could, e.g., study whether only certain parameters are identifiable. This approach requires an effective, efficient and automatic way of performing estimation. In this study we concentrate on the case of fitting a deterministic mathematical model of a biological process, i.e., expressed in terms of a system of ordinary differential equations (ODEs)

optPBN: An Optimisation Toolbox for Probabilistic Boolean Networks

Background There 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. Results We 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. Summary The 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

Recent development and biomedical applications of probabilistic Boolean networks

Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the study of the topology and dynamic aspects of biological systems. The combined use of rule-based representation and probability makes PBN appealing for large-scale modelling of biological networks where degrees of uncertainty need to be considered. A considerable expansion of our knowledge in the field of theoretical research on PBN can be observed over the past few years, with a focus on network inference, network intervention and control. With respect to areas of applications, PBN is mainly used for the study of gene regulatory networks though with an increasing emergence in signal transduction, metabolic, and also physiological networks. At the same time, a number of computational tools, facilitating the modelling and analysis of PBNs, are continuously developed. A concise yet comprehensive review of the state-of-the-art on PBN modelling is offered in this article, including a comparative discussion on PBN versus similar models with respect to concepts and biomedical applications. Due to their many advantages, we consider PBN to stand as a suitable modelling framework for the description and analysis of complex biological systems, ranging from molecular to physiological levels