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Reprogramming multistable monotone systems with application to cell fate control.

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This study shows how to control cell fate decisions in biological networks. We identify specific inputs to guide cellular systems to desired stable states, independent of parameter values.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Dynamical Systems Theory

Background:

  • Multistability in dynamical systems models cellular regulatory networks and cell fate decisions.
  • Distinct stable steady states often correspond to specific cell phenotypes.
  • Monotone network motifs are prevalent in these biological regulatory networks.

Purpose of the Study:

  • To leverage monotone dynamical systems properties for theoretical insights into reprogramming cellular networks.
  • To guide the selection of inputs for transitioning networks to desired stable steady states.
  • To develop methods for identifying and searching for effective reprogramming inputs.

Main Methods:

  • Analysis of monotone dynamical systems with bounded trajectories.
  • Theoretical derivation of input selection strategies for reprogramming.
  • Development of search space pruning guidelines for finite-time procedures.
  • Simulation of recurrent regulatory network motifs (self-activation with mutual antagonism/cooperation).

Main Results:

  • Monotone dynamical systems with bounded trajectories possess minimum and maximum stable steady states.
  • Specific input choices guarantee reprogramming to extreme steady states.
  • An input space is defined that is guaranteed to contain a reprogramming input for intermediate states.
  • Network structure, not parameter values, uniquely determines reprogramming outcomes.

Conclusions:

  • Theoretical framework for guiding input selection in monotone dynamical systems for cell fate control.
  • Practical guidelines for implementing finite-time search procedures for reprogramming inputs.
  • Demonstrated efficacy on key recurrent regulatory network motifs.
  • Results are broadly applicable due to independence from specific parameter values.