Most biological processes are controlled by regulatory networks, which involve various kinds of molecular interactions, including protein-mediated transcriptional regulations, polypeptide receptor–ligand associations, protein modifications by specific enzymes, etc. Decades of genetic and molecular analyses, more recently complemented by high-throughput functional genomic experiments, have progressively uncovered many of the numerous interactions controlling several crucial biological processes (including cell cycle and various developmental pathways). The complexity of the networks delineated often defies intuitive reasoning, consequently calling for the development of proper computational tools. Different mathematical approaches have been proposed to model such genetic networks and to simulate their dynamical behaviour, ranging from quantitative formalisms to crude Boolean models.
Supports the definition, the simulation and the analysis of regulatory graphs, based on the logical formalism. GINsim is a software that displays a window enabling the creation of a new model, the import of a model in a supported format, or the opening of a previously defined model. This tool leans on two main types of graphs: Logical Regulatory Graphs, which model regulatory networks, and State Transition Graphs, which represent their dynamical behavior.
Integrates methods for synchronous, asynchronous and probabilistic (Boolean networks) BNs. This includes reconstructing networks from time series, generating random networks, robustness analysis via perturbation, Markov chain simulations, and identification and visualization of attractors.
Allows to perform structural and qualitative analysis of both mass-flow- and signal-flow-based cellular networks. CellNetAnalyzer is a toolbox for analyzing structure and function of biological networks on the basis of topological, stoichiometric, qualitative (logical) and semi-quantitative modeling approaches requiring no or only few parameters. Metabolic networks can be studied based on stoichiometric and constraint-based modeling approaches whereas signaling and regulatory networks can be explored by qualitative and semi-quantitative modeling approaches.
An open-source R software package for building predictive logic models of signaling networks by training networks derived from prior knowledge to signaling (typically phosphoproteomic) data. CellNOptR features different logic formalisms, from Boolean models to differential equations, in a common framework. These different logic model representations accommodate state and time values with increasing levels of detail. Models built with CellNOptR are useful tools to understand how signals are processed by cells and how this is altered in disease. They can be used to predict the effect of perturbations (individual or in combinations), and potentially to engineer therapies that have differential effects/side effects depending on the cell type or context.
Converts CellNetAnalyzer (CNA) compliant logic models of signalling networks to facilitate handling of combinatorial regulation. MuVal is a free web service that provides an alternative to the applied assumptions of concerted or independent inhibitory mechanisms, which lead to false inferences in “classical” Boolean (or multi-valued) treatment if the reactions analyzed are being influenced by several regulators simultaneously. By the sequential use of MuVal and CNA signalling, the models of the networks can be simulated in terms of multi-valued logic with graded-inhibitory (or activation) responses.
The goal of this software package is to provide intuitive and accessible tools for simulating biological regulatory networks in a Boolean formalism. Using this simulator biologist and bioinformaticians can specify their system in a simple textual language then explore various dynamic behaviors via a web interface or an application programming interface (API) each designed to facilitate scientific discovery, data collection and reporting.
Converts Boolean models into systems of ordinary differential equations (ODE). Odefy is a user-friendly implementation of the HillCube technique suitable for large-scale networks. The software provides access to different models sources, the conversion process and various analysis and export methods. A discrete model converted to an ODE by Odefy displays similar dynamical properties as a mechanistically derived ODE model of the same system.Converts Boolean models into systems of ordinary differential equations (ODE). Odefy is a user-friendly implementation of the HillCube technique suitable for large-scale networks. The software provides access to different models sources, the conversion process and various analysis and export methods. A discrete model converted to an ODE by Odefy displays similar dynamical properties as a mechanistically derived ODE model of the same system.