Abstract
In this work, we pose the problem of gene regulatory function identification using a new general framework based on function separation. We prove the uniqueness of the solution within this framework in an almost always sense. Additionally, we develop an iterative scheme, called iterative function separation (IFS), which is guaranteed to converge to a solution. Both these results together guarantee the computation of the unique solution. We also develop theoretical limits on the number of gene expression level measurements (time samples) sufficient to uniquely reconstruct the network. The proposed method (IFS) is validated using synthetic networks both within and outside of the modeling domain of the new framework.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the IEEE Conference on Decision and Control |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 133-138 |
| Number of pages | 6 |
| Volume | 2016-February |
| ISBN (Print) | 9781479978861 |
| DOIs | |
| State | Published - Feb 8 2016 |
| Event | 54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan Duration: Dec 15 2015 → Dec 18 2015 |
Other
| Other | 54th IEEE Conference on Decision and Control, CDC 2015 |
|---|---|
| Country | Japan |
| City | Osaka |
| Period | 12/15/15 → 12/18/15 |
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ASJC Scopus subject areas
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization
Cite this
Iterative function separation for gene regulatory function identification. / Bhushan, Palak; Chang, Young Hwan; Tomlin, Claire J.
Proceedings of the IEEE Conference on Decision and Control. Vol. 2016-February Institute of Electrical and Electronics Engineers Inc., 2016. p. 133-138 7402098.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Iterative function separation for gene regulatory function identification
AU - Bhushan, Palak
AU - Chang, Young Hwan
AU - Tomlin, Claire J.
PY - 2016/2/8
Y1 - 2016/2/8
N2 - In this work, we pose the problem of gene regulatory function identification using a new general framework based on function separation. We prove the uniqueness of the solution within this framework in an almost always sense. Additionally, we develop an iterative scheme, called iterative function separation (IFS), which is guaranteed to converge to a solution. Both these results together guarantee the computation of the unique solution. We also develop theoretical limits on the number of gene expression level measurements (time samples) sufficient to uniquely reconstruct the network. The proposed method (IFS) is validated using synthetic networks both within and outside of the modeling domain of the new framework.
AB - In this work, we pose the problem of gene regulatory function identification using a new general framework based on function separation. We prove the uniqueness of the solution within this framework in an almost always sense. Additionally, we develop an iterative scheme, called iterative function separation (IFS), which is guaranteed to converge to a solution. Both these results together guarantee the computation of the unique solution. We also develop theoretical limits on the number of gene expression level measurements (time samples) sufficient to uniquely reconstruct the network. The proposed method (IFS) is validated using synthetic networks both within and outside of the modeling domain of the new framework.
UR - http://www.scopus.com/inward/record.url?scp=84962020043&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962020043&partnerID=8YFLogxK
U2 - 10.1109/CDC.2015.7402098
DO - 10.1109/CDC.2015.7402098
M3 - Conference contribution
AN - SCOPUS:84962020043
SN - 9781479978861
VL - 2016-February
SP - 133
EP - 138
BT - Proceedings of the IEEE Conference on Decision and Control
PB - Institute of Electrical and Electronics Engineers Inc.
ER -