Abstract
WebMaBoSS is an easy-to-use web interface for conversion, storage, simulation and analysis of Boolean models that allows to get insight from these models without any specific knowledge of modeling or coding. It relies on an existing software, MaBoSS, which simulates Boolean models using a stochastic approach: it applies continuous time Markov processes over the Boolean network. It was initially built to fill the gap between Boolean and continuous formalisms, i.e., providing semi-quantitative results using a simple representation with a minimum number of parameters to fit. The goal of WebMaBoSS is to simplify the use and the analysis of Boolean models coping with two main issues: 1) the simulation of Boolean models of intracellular processes with MaBoSS, or any modeling tool, may appear as non-intuitive for non-experts; 2) the simulation of already-published models available in current model databases (e.g., Cell Collective, BioModels) may require some extra steps to ensure compatibility with modeling tools such as MaBoSS. With WebMaBoSS, new models can be created or imported directly from existing databases. They can then be simulated, modified and stored in personal folders. Model simulations are performed easily, results visualized interactively, and figures can be exported in a preferred format. Extensive model analyses such as mutant screening or parameter sensitivity can also be performed. For all these tasks, results are stored and can be subsequently filtered to look for specific outputs. This web interface can be accessed at the address: https://maboss.curie.fr/webmaboss/ and deployed locally using docker. This application is open-source under LGPL license, and available at https://github.com/sysbio-curie/WebMaBoSS.
Highlights
The results of experimental observations often lead to non-intuitive behaviours, which could be explained, at the level of protein interactions, by unexpected cross-talks between two signaling pathways or by some gene alterations that could lead to unexpected phenotypes.One way to cope with these issues is to formalize the knowledge about these pathways into networks
The types of conclusions or predictions that can be made with this approach are very qualitative. To bridge these two formalisms, we have developed MaBoSS (Markovian Boolean Stochastic Simulator) which relies on a Boolean framework but uses a stochastic simulation approach by applying a Markov process on the Boolean network (Stoll et al, 2012, 2017)
We provide an example of the web interface for which no details about the functioning of MaBoSS are needed
Summary
The results of experimental observations often lead to non-intuitive behaviours, which could be explained, at the level of protein interactions, by unexpected cross-talks between two signaling pathways or by some gene alterations that could lead to unexpected phenotypes. Biology Graphical Notation, SBGN (Le Novere et al, 2009) has simplified their construction, use and analysis, and has led to think carefully about the level of details and the types of information that is needed to answer a biological question These networks are static and the analyses may be limited to fully address the problem from a dynamical perspective. What makes MaBoSS different from the other tools is that it associates parameter values to each variable of the model representing the speed of activation and inactivation of the corresponding node With this framework, it is possible to compute time trajectories over time, and provide probabilities for the model solutions (corresponding to a vector of the state of the variables). We show how to produce a simulation of the wild type conditions and of mutants (which can be understood as possible alterations of the wild type model), how to run some automatic mutations that can be assimilated to the search for the best drug treatment, and how to save the analyses and export images of the results
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