Exploring attractor bifurcations in Boolean networks View Full Text


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Article Info

DATE

2022-05-11

AUTHORS

Nikola Beneš, Luboš Brim, Jakub Kadlecaj, Samuel Pastva, David Šafránek

ABSTRACT

BackgroundBoolean networks (BNs) provide an effective modelling formalism for various complex biochemical phenomena. Their long term behaviour is represented by attractors–subsets of the state space towards which the BN eventually converges. These are then typically linked to different biological phenotypes. Depending on various logical parameters, the structure and quality of attractors can undergo a significant change, known as a bifurcation. We present a methodology for analysing bifurcations in asynchronous parametrised Boolean networks.ResultsIn this paper, we propose a computational framework employing advanced symbolic graph algorithms that enable the analysis of large networks with hundreds of Boolean variables. To visualise the results of this analysis, we developed a novel interactive presentation technique based on decision trees, allowing us to quickly uncover parameters crucial to the changes in the attractor landscape. As a whole, the methodology is implemented in our tool AEON. We evaluate the method’s applicability on a complex human cell signalling network describing the activity of type-1 interferons and related molecules interacting with SARS-COV-2 virion. In particular, the analysis focuses on explaining the potential suppressive role of the recently proposed drug molecule GRL0617 on replication of the virus.ConclusionsThe proposed method creates a working analogy to the concept of bifurcation analysis widely used in kinetic modelling to reveal the impact of parameters on the system’s stability. The important feature of our tool is its unique capability to work fast with large-scale networks with a relatively large extent of unknown information. The results obtained in the case study are in agreement with the recent biological findings. More... »

PAGES

173

References to SciGraph publications

  • 2021-09-13. Aeon 2021: Bifurcation Decision Trees in Boolean Networks in COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY
  • 2004. Elements of Applied Bifurcation Theory in NONE
  • 2015-02-03. Quantitative and logic modelling of molecular and gene networks in NATURE REVIEWS GENETICS
  • 2007-01-01. Decision Diagrams for the Representation and Analysis of Logical Models of Genetic Networks in COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY
  • 2017-09-01. Detecting Attractors in Biological Models with Uncertain Parameters in COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY
  • 2015-07-16. Synthesising Executable Gene Regulatory Networks from Single-Cell Gene Expression Data in COMPUTER AIDED VERIFICATION
  • 2020-10-01. How COVID-19 induces cytokine storm with high mortality in INFLAMMATION AND REGENERATION
  • 2017-04-06. Petri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation in SCIENTIFIC REPORTS
  • 2020-07-14. AEON: Attractor Bifurcation Analysis of Parametrised Boolean Networks in COMPUTER AIDED VERIFICATION
  • 2021-07-15. Computing Bottom SCCs Symbolically Using Transition Guided Reduction in COMPUTER AIDED VERIFICATION
  • 2016-10-07. An efficient algorithm for identifying primary phenotype attractors of a large-scale Boolean network in BMC SYSTEMS BIOLOGY
  • 2012. Bma: Visual Tool for Modeling and Analyzing Biological Networks in COMPUTER AIDED VERIFICATION
  • 2005-01-14. Reconstruction of cellular signalling networks and analysis of their properties in NATURE REVIEWS MOLECULAR CELL BIOLOGY
  • 2007-01-08. Structural and functional analysis of cellular networks with CellNetAnalyzer in BMC SYSTEMS BIOLOGY
  • 2011. Analysis and Control of Boolean Networks, A Semi-tensor Product Approach in NONE
  • 2016-11-08. A Model Checking Approach to Discrete Bifurcation Analysis in FM 2016: FORMAL METHODS
  • 2012-08-07. The Cell Collective: Toward an open and collaborative approach to systems biology in BMC SYSTEMS BIOLOGY
  • 2011-10-28. Logical Modelling of Gene Regulatory Networks with GINsim in BACTERIAL MOLECULAR NETWORKS
  • 2018-02-23. ATLANTIS - Attractor Landscape Analysis Toolbox for Cell Fate Discovery and Reprogramming in SCIENTIFIC REPORTS
  • 2021-01-20. The complex structure of GRL0617 and SARS-CoV-2 PLpro reveals a hot spot for antiviral drug discovery in NATURE COMMUNICATIONS
  • 2003-11. Identification of all steady states in large networks by logical analysis in BULLETIN OF MATHEMATICAL BIOLOGY
  • 2018-05-25. Boolean Networks: Beyond Generalized Asynchronicity in CELLULAR AUTOMATA AND DISCRETE COMPLEX SYSTEMS
  • 2014. Characterization of Reachable Attractors Using Petri Net Unfoldings in COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY
  • 2015-10-07. Computing maximal and minimal trap spaces of Boolean networks in NATURAL COMPUTING
  • 2020-05-05. COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms in SCIENTIFIC DATA
  • 2019-09-23. Controlling Large Boolean Networks with Temporary and Permanent Perturbations in FORMAL METHODS – THE NEXT 30 YEARS
  • 2007-04-12. Algorithms for Finding Small Attractors in Boolean Networks in EURASIP JOURNAL ON BIOINFORMATICS AND SYSTEMS BIOLOGY
  • 2015. Comparative Statistical Analysis of Qualitative Parametrization Sets in HYBRID SYSTEMS BIOLOGY
  • 2013-12-13. Detection of attractors of large Boolean networks via exhaustive enumeration of appropriate subspaces of the state space in BMC BIOINFORMATICS
  • 2019-10-28. Formal Analysis of Qualitative Long-Term Behaviour in Parametrised Boolean Networks in FORMAL METHODS AND SOFTWARE ENGINEERING
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    URI

    http://scigraph.springernature.com/pub.10.1186/s12859-022-04708-9

    DOI

    http://dx.doi.org/10.1186/s12859-022-04708-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1147796833

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/35546394


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