Reprogramming multistable monotone systems with application to cell fate control
Name
nihms-1608080.pdf
Description
Accepted version
Size
1.51 MB
Format
Adobe PDF
Checksum (MD5)
bc363d75be1c902f4de21d978fc632ff
Author(s)
Shah, Rushina
Vecchio, Domitilla Del
Date Issued
2020
Journal
IEEE Transactions on Network Science and Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Shah, Rushina and Vecchio, Domitilla Del. 2020. "Reprogramming multistable monotone systems with application to cell fate control." IEEE Transactions on Network Science and Engineering, 7 (4).
Version
Author's final manuscript
Abstract
© 2013 IEEE. Multistability is a key property of dynamical systems modeling cellular regulatory networks implicated in cell fate decisions, where, different stable steady states usually represent distinct cell phenotypes. Monotone network motifs are highly represented in these regulatory networks. In this paper, we leverage the properties of monotone dynamical systems to provide theoretical results that guide the selection of inputs that trigger a transition, i.e., reprogram the network, to a desired stable steady state. We first show that monotone dynamical systems with bounded trajectories admit a minimum and a maximum stable steady state. Then, we provide input choices that are guaranteed to reprogram the system to these extreme steady states. For intermediate states, we provide an input space that is guaranteed to contain an input that reprograms the system to the desired state. We then provide implementation guidelines for finite-time procedures that search this space for such an input, along with rules to prune parts of the space during search. We demonstrate these results on simulations of two recurrent regulatory network motifs: self-activation within mutual antagonism and self-activation within mutual cooperation. Our results depend uniquely on the structure of the network and are independent of specific parameter values.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/TNSE.2020.3008135
