Logical Modeling and Dynamical Analysis of Cellular Networks.
Wassim Abou-Jaoudé1, Pauline Traynard1, Pedro T Monteiro2
1Computational Systems Biology Team, Institut de Biologie de l'Ecole Normale Supérieure, CNRS UMR8197, INSERM U1024, Ecole Normale Supérieure, PSL Research University Paris, France.
Frontiers in Genetics
|June 16, 2016
Summary
Logical modeling is essential for understanding complex biological networks. This review highlights recent advances in analyzing large-scale logical models, focusing on attractors, signal impact, and model reduction for T helper cell differentiation and cell cycle control.
Area of Science:
- Systems Biology
- Computational Biology
- Bioinformatics
Background:
- Logical formalism is a powerful framework for modeling biological regulatory and signaling networks.
- Existing methods and tools support the definition and analysis of these logical models.
- Analyzing large and complex networks presents significant challenges in systems biology.
Purpose of the Study:
- To review recent methodological advances for analyzing large and intricate logical models.
- To provide an overview of techniques for determining model attractors and reachability.
- To discuss approaches for assessing external signal impact and model reduction.
Main Methods:
- Survey of recent advancements in logical modeling analysis.
- Focus on methods for attractor identification and reachability analysis.
- Examination of techniques for evaluating dynamical responses to external signals and model simplification.
Main Results:
- Several approaches facilitate the analysis of large-scale logical models.
- Methods are presented for determining model attractors and their properties.
- Techniques for assessing signal impact and reducing model complexity are discussed.
Conclusions:
- Recent methodological advances significantly ease the analysis of complex logical models in biology.
- The reviewed approaches are illustrated using models of T helper cell differentiation and mammalian cell cycle control.
- These advancements are crucial for deeper understanding of biological systems through computational modeling.
Keywords:
T cells activation and differentiationattractorscell cycle controldiscrete dynamicslogical modelingreachability analysisregulatory and signaling networkssimulationMore Related Videos
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