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Converting networks to predictive logic models from perturbation signalling data with CellNOpt.

Enio Gjerga1,2, Panuwat Trairatphisan1, Attila Gabor1

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CellNOpt, a Bioconductor R package, builds logic models from perturbation data and signaling networks. New components enhance its capacity for large datasets, offering dynamic modeling and mechanistic insights.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Network Biology

Background:

  • Understanding intracellular wiring through molecular changes from drug/ligand perturbations is crucial.
  • Increasing capacity to generate large biological datasets necessitates advanced analytical tools.
  • Integrating perturbation data with prior knowledge networks is key for extracting mechanistic insights.

Purpose of the Study:

  • To present updated components and refinements for the CellNOpt R package.
  • To enhance CellNOpt's capability in handling large datasets for dynamic modeling.
  • To provide tools for building logic models from signaling network data.

Main Methods:

  • Development of an efficient integer linear programming solver.
  • Implementation of probabilistic logic for semi-quantitative datasets.
  • Integration of a stochastic Boolean simulator and a tool for identifying missing network links.
  • Inclusion of systematic post-hoc analyses and an R-Shiny interactive tool.

Main Results:

  • Refined CellNOpt components efficiently handle large-scale perturbation data.
  • New features enable dynamic modeling and mechanistic insight extraction.
  • The updated package supports semi-quantitative data analysis and network gap identification.

Conclusions:

  • CellNOpt provides a robust platform for building dynamic logic models from signaling network data.
  • The enhanced package addresses computational demands of large datasets, facilitating systems biology research.
  • Updated tools improve the analysis of molecular perturbations for understanding cellular mechanisms.