scholarly journals Logical modelling and analysis of cellular regulatory networks with GINsim 3.0

2018 ◽  
Author(s):  
Aurélien Naldi ◽  
Céline Hernandez ◽  
Wassim Abou-Jaoudé ◽  
Pedro T. Monteiro ◽  
Claudine Chaouiya ◽  
...  
Keyword(s):  
Cellular Networks ◽  
Kinetic Data ◽  
Regulatory Networks ◽  
Logical Modelling ◽  
Logical Formalism

AbstractThe logical formalism is well adapted to model large cellular networks, for which detailed kinetic data are scarce. This tutorial focuses on this well-established qualitative framework. Relying on GINsim (release 3.0), a software implementing this formalism, we guide the reader step by step towards the definition, the analysis and the simulation of a four-node model of the mammalian p53-Mdm2 network.

Logical modelling of regulatory networks with GINsim 2.3

Biosystems ◽  
2009 ◽  
Vol 97 (2) ◽  
pp. 134-139 ◽  
Author(s):  
A. Naldi ◽  
D. Berenguier ◽  
A. Fauré ◽  
F. Lopez ◽  
D. Thieffry ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Logical Modelling

scholarly journals Constraint-Based Modeling in Systems Biology

10.29007/8w4w ◽  
2018 ◽  
Author(s):  
Alexander Bockmayr
Keyword(s):  
Systems Biology ◽  
Regulatory Networks ◽  
Partial Information ◽  
Kripke Model ◽  
State Transition Graph ◽  
Constraint Based Modeling ◽  
The One ◽  
Logical Formalism ◽  
Different Levels ◽  
Natural Way

The idea of constraint-based modeling in systems biology is to describe a biological system by a set of constraints, i.e., by pieces of partial information about its structure and dynamics. Using constraint-based reasoning one may then draw conclusions about the possible system behaviors.In this talk, we will focus on constraint-based modeling techniques for regulatory networks starting from the discrete logical formalism of René Thomas. In this framework, logic and constraints arise at two different levels. On the one hand, Boolean or multi-valued logic formulae provide a natural way to represent the structure of a regulatory network, which is given by positive and negative interactions (i.e., activation and inhibition) between the network components. On the other hand, temporal logic formulae (e.g. CTL) may be used to reason about the dynamics of the system, represented by a state transition graph or Kripke model.

scholarly journals EpiLog: A software for the logical modelling of epithelial dynamics

F1000Research ◽  
2019 ◽  
Vol 7 ◽  
pp. 1145 ◽  
Author(s):  
Pedro L. Varela ◽  
Camila V. Ramos ◽  
Pedro T. Monteiro ◽  
Claudine Chaouiya
Keyword(s):  
Regulatory Networks ◽  
Cellular Responses ◽  
Tissue Level ◽  
Cellular Level ◽  
Environmental Cues ◽  
Link Type ◽  
Qualitative Models ◽  
Logical Modelling ◽  
Gene Regulatory ◽  
External Signals

Cellular responses are governed by regulatory networks subject to external signals from surrounding cells and to other micro-environmental cues. The logical (Boolean or multi-valued)  framework proved well suited to study such processes at the cellular level, by specifying qualitative models of involved signalling pathways and gene regulatory networks.  Here, we describe and illustrate the main features of EpiLog, a computational tool that implements an extension of the logical framework to the tissue level. EpiLog defines a collection of hexagonal cells over a 2D grid, which embodies a mono-layer epithelium. Basically, it defines a cellular automaton in which cell behaviours are driven by associated logical models subject to external signals.  EpiLog is freely available on the web at http://epilog-tool.org. It is implemented in Java (version ≥1.7 required) and the source code is provided at https://github.com/epilog-tool/epilog under a GNU General Public License v3.0.

scholarly journals Logical Modelling of Regulatory Networks, Methods and Applications

2013 ◽  
Vol 75 (6) ◽  
pp. 891-895 ◽  
Author(s):  
Claudine Chaouiya ◽  
Elisabeth Remy
Keyword(s):  
Regulatory Networks ◽  
Logical Modelling

scholarly journals EpiLog: A software for the logical modelling of epithelial dynamics

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1145 ◽  
Author(s):  
Pedro L. Varela ◽  
Camila V. Ramos ◽  
Pedro T. Monteiro ◽  
Claudine Chaouiya
Keyword(s):  
Regulatory Networks ◽  
Cellular Responses ◽  
Tissue Level ◽  
Cellular Level ◽  
Environmental Cues ◽  
Link Type ◽  
Qualitative Models ◽  
Logical Modelling ◽  
Gene Regulatory ◽  
External Signals

Cellular responses are governed by regulatory networks subject to external signals from surrounding cells and to other micro-environmental cues. The logical (Boolean or multi-valued)  framework proved well suited to study such processes at the cellular level, by specifying qualitative models of involved signalling pathways and gene regulatory networks.  Here, we describe and illustrate the main features of EpiLog, a computational tool that implements an extension of the logical framework to the tissue level. EpiLog defines a collection of hexagonal cells over a 2D grid, which embodies a mono-layer epithelium. Basically, it defines a cellular automaton in which cell behaviours are driven by associated logical models subject to external signals.  EpiLog is freely available on the web at http://epilog-tool.org. It is implemented in Java (version ≥1.7 required) and the source code is provided at https://github.com/epilog-tool/epilog under a GNU General Public License v3.0.

scholarly journals Parallelization of ARACNe, an Algorithm for the Reconstruction of Gene Regulatory Networks

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 25
Author(s):  
Uxía Casal ◽  
Jorge González-Domínguez ◽  
María J. Martín
Keyword(s):  
Cellular Networks ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Parallel Implementation ◽  
Computational Cost ◽  
Spearman Correlation ◽  
Mathematical Methods ◽  
Other Substances ◽  
Gene Regulatory ◽  
High Computational Cost

Gene regulatory networks are graphical representations of molecular regulators that interact with each other and with other substances in the cell to govern the gene expression. There are different computational approaches for the reverse engineering of these networks. Most of them require all gene-gene evaluations using different mathematical methods such as Pearson/Spearman correlation, Mutual Information or topology patterns, among others. The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) is one of the most effective and widely used tools to reconstruct gene regulatory networks. However, the high computational cost of ARACNe prevents its use over large biologic datasets. In this work, we present a hybrid MPI/OpenMP parallel implementation of ARACNe to accelerate its execution on multi-core clusters, obtaining a speedup of 430.46 using as input a dataset with 41,100 genes and 108 samples and 32 nodes (each of them with 24 cores).

Stochastic Geometry Analysis of Cellular Networks

2018 ◽  
Author(s):  
Bartłomiej Błaszczyszyn ◽  
Martin Haenggi ◽  
Paul Keeler ◽  
Sayandev Mukherjee
Keyword(s):  
Cellular Networks ◽  
Stochastic Geometry ◽  
Geometry Analysis
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