Panuwat Trairatphisan

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.

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 interactions relevancy in signal transduction networks.

L-plastin Ser5 phosphorylation in breast cancer cells and in vitro is mediated by RSK downstream of the ERK/MAPK pathway

Deregulated cell migration and invasion are hallmarks of metastatic cancer cells. Phosphorylation on residue Ser5 of the actin‐bundling protein L‐plastin activates L‐plastin and has been reported to be crucial for invasion and metastasis. Here, we investigate signal transduction leading to L‐plastin Ser5 phosphorylation using 4 human breast cancer cell lines. Whole‐genome microarray analysis comparing cell lines with different invasive capacities and corresponding variations in L‐plastin Ser5 phosphorylation level revealed that genes of the ERK/MAPK pathway are differentially expressed. It is noteworthy that in vitro kinase assays showed that ERK/MAPK pathway downstream ribosomal protein S6 kinases α‐1 (RSK1) and α‐3 (RSK2) are able to directly phosphorylate L‐plastin on Ser5. Small interfering RNA‐ or short hairpin RNA‐mediated knockdown and activation/inhibition studies followed by immunoblot analysis and computational modeling confirmed that ribosomal S6 kinase (RSK) is an essential activator of L‐plastin. Migration and invasion assays showed that RSK knockdown led to a decrease of up to 30% of migration and invasion of MDA‐MB‐435S cells. Although the presence of L‐plastin was not necessary for migration/invasion of these cells, immunofluorescence assays illustrated RSK‐dependent recruitment of Ser5‐phosphorylated L‐plastin to migratory structures. Altogether, we provide evidence that the ERK/MAPK pathway is involved in L‐plastin Ser5 phosphorylation in breast cancer cells with RSK1 and RSK2 kinases able to directly phosphorylate L‐plastin residue Ser5.—Lommel, M. J., Trairatphisan, P., Gäbler, K., Laurini, C., Muller, A., Kaoma, T., Vallar, L., Sauter, T., Schaffner‐Reckinger, E., L‐plastin Ser5 phosphorylation in breast cancer cells and in vitro is mediated by RSK downstream of the ERK/MAPK pathway. FASEB J. 30, 1218–1233 (2016). www.fasebj.org

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.

Probabilistic model checking of the PDGF signaling pathway

In this paper, we apply the probabilistic symbolic 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. Moreover, we compare the results from verification and ODE simulation on the PDGF pathway and demonstrate by examples the influence of model structure, parameter values and pathway length on the two analysis methods.

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.

FALCON: a toolbox for the fast contextualization 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 contextualize 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 contextualize networks, which includes a pipeline for conducting parameter analysis, knockouts and easy and fast model investigation. The contextualized 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 thomas.sauter@uni.lu Supplementary information Supplementary data are available at Bioinformatics online.