Name
Abstract | ||
|---|---|---|
We address the sequential reprogramming of gene regulatory networks modelled as Boolean networks. We develop an attractor-based sequential reprogramming method to compute all sequential reprogramming paths from a source attractor to a target attractor, where only attractors of the network are used as intermediates. Our method is more practical than existing reprogramming methods as it incorporates several practical constraints: (1) only biologically observable states, viz. attractors, can act as intermediates; (2) certain attractors, such as apoptosis, can be avoided as intermediates; (3) certain nodes can be avoided to perturb as they may be essential for cell survival or difficult to perturb with biomolecular techniques; and (4) given a threshold k, all sequential reprogramming paths with no more than k perturbations are computed. We compare our method with the minimal one-step reprogramming and the minimal sequential reprogramming on a variety of biological networks. The results show that our method can greatly reduce the number of perturbations compared to the one-step reprogramming, while having comparable results with the minimal sequential reprogramming. Moreover, our implementation is scalable for networks of more than 60 nodes. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-31304-3_1 | COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY (CMSB 2019) |
Keywords | DocType | Volume |
Cell reprogramming, Boolean networks, Attractors | Conference | 11773 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.35 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hugues Mandon | 1 | 1 | 0.35 |
| Cui Su | 2 | 5 | 3.46 |
| Stefan Haar | 3 | 85 | 14.63 |
| Jun Pang | 4 | 219 | 33.53 |
| Loïc Paulevé | 5 | 204 | 18.68 |