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Software

 

 

PyBoolNet

PyBoolNet is a Python package for the generation, manipulation and analysis of Boolean networks. The goal was to provide an intuitive and simple Python interface to Boolean networks. The package features graph analysis algorithms via NetworkX, LTL and CTL model checking via NuSMV and the computation of trap spaces via the Potsdam Answer Set Solving Collection. The documentation includes a detailed tutorial for every feature and a reference of all functions. For discussions, feature requests, comments and bug reports visit the PyBoolNet homepage.


 

TREMPI - Toolkit for Reverse Engineering of Molecular Pathways through Parameter Identification

TREMPPI is a Systems Biology toolkit for research in gene regulation and molecular signalling. This fully integrated, visual toolkit is well suited for the analysis of complex functional modules, e.g. circadian clock. TREMPPI supports multiple ways of encoding various biological knowledge about the system. The goal is to provide as much information as possible about how the structure of the system is likely to look. Such an approach is most valuable if the precise mechanics of the module in question are not known. In this scenario we simply consider all the possibilities for how the components of the module may interact and analyse them one by one and therefore we utilize a very coarse-grained modelling framework of logical networks, focusing solely on qualitative features of a model.


 

Boolean Network of EGFR Pathway Disruption in Colorectal Cancer

Based on the study by Klinger et al. we created a Boolean Network of EGFR pathway with possible disruptions. The data were obtained for 5 cell lines with 8 different treatment combinations. All the possible dynamical models were tested for the properties and comparative qualitative and quantitative analyses were conducted. A related manuscript was submitted to the CMSB 2015 conference.


ERDA (Edge Refinement and Data Assessment)

This python script allows evaluation of network properties based on time series data, in particular, consistency of modeling assumptions concerning network interactions and time series data.
It is supplementary to the 2012 article "Time series dependent analysis of unparametrized Thomas networks" (see Publications) by H. Klarner, H. Siebert and A. Bockmayr, and performs the workflow steps described in section 7.


 

ESTHER

Esther is an interface to a suite of tools for discrete simulation a reverse-engineering of gene regulatory and signal transduction networks. For modeling we employ the framework of asynchronous boolean or multi-valued (commonly referred to as Thomas or Qualitative) networks.