A Robustness Analysis of Dynamic Boolean Models of Cellular Circuits

Ariel Bruner, Roded Sharan

Research output: Contribution to journalArticlepeer-review


With ever growing amounts of omics data, the next challenge in biological research is the interpretation of these data to gain mechanistic insights about cellular function. Dynamic models of cellular circuits that capture the activity levels of proteins and other molecules over time offer great expressive power by allowing the simulation of the effects of specific internal or external perturbations on the workings of the cell. However, the study of such models is at its infancy and no large-scale analysis of the robustness of real models to changing conditions has been conducted to date. Here we provide a computational framework to study the robustness of such models using a combination of stochastic simulations and integer linear programming techniques. We apply our framework to a large collection of cellular circuits and benchmark the results against randomized models. We find that the steady states of real circuits tend to be more robust in multiple aspects compared with their randomized counterparts.

Original languageEnglish
Pages (from-to)133-143
Number of pages11
JournalJournal of Computational Biology
Issue number2
StatePublished - Feb 2020


  • Boolean networks
  • attractor computation
  • integer linear programming

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics


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