Control of Intracellular Molecular Networks Using Algebraic Methods

Luis Sordo Vieira, Reinhard C. Laubenbacher, David Murrugarra

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Many problems in biology and medicine have a control component. Often, the goal might be to modify intracellular networks, such as gene regulatory networks or signaling networks, in order for cells to achieve a certain phenotype, what happens in cancer. If the network is represented by a mathematical model for which mathematical control approaches are available, such as systems of ordinary differential equations, then this problem might be solved systematically. Such approaches are available for some other model types, such as Boolean networks, where structure-based approaches have been developed, as well as stable motif techniques. However, increasingly many published discrete models are mixed-state or multistate, that is, some or all variables have more than two states, and thus the development of control strategies for multistate networks is needed. This paper presents a control approach broadly applicable to general multistate models based on encoding them as polynomial dynamical systems over a finite algebraic state set, and using computational algebra for finding appropriate intervention strategies. To demonstrate the feasibility and applicability of this method, we apply it to a recently developed multistate intracellular model of E2F-mediated bladder cancerous growth and to a model linking intracellular iron metabolism and oncogenic pathways. The control strategies identified for these published models are novel in some cases and represent new hypotheses, or are supported by the literature in others as potential drug targets. Our Macaulay2 scripts to find control strategies are publicly available through GitHub at

Original languageEnglish
Article number2
JournalBulletin of Mathematical Biology
Issue number1
StatePublished - Jan 1 2020

Bibliographical note

Funding Information:
Sordo Vieira is partially supported by The National Institutes of Health Grant No. 1R01AI135128-01. Laubenbacher is partially supported by The National Institutes of Health Grants No. 1R01AI135128-01, 1U01EB024501-01, and 1R01GM127909-01.

Publisher Copyright:
© 2019, Society for Mathematical Biology.


  • Control
  • Discrete dynamical system
  • Intracellular network
  • Polynomial dynamical systems

ASJC Scopus subject areas

  • Neuroscience (all)
  • Immunology
  • Mathematics (all)
  • Biochemistry, Genetics and Molecular Biology (all)
  • Environmental Science (all)
  • Pharmacology
  • Agricultural and Biological Sciences (all)
  • Computational Theory and Mathematics


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