Markdahl, J;
Colombo, N;
Thunberg, J;
Goncalves, J;
(2017)
Experimental Design Trade-offs for Gene Regulatory Network Inference: an in Silico Study of the Yeast Saccharomyces Cerevisiae Cell Cycle.
In:
Proceedings of the 2017 IEEE 56th Annual Conference on Decision and Control (CDC).
(pp. pp. 423-428).
IEEE: Melbourne, VIC, Australia.
Preview |
Text
Colombo_ExtractPage1.pdf - Accepted Version Download (264kB) | Preview |
Abstract
Time-series of high throughput gene sequencing data intended for gene regulatory network (GRN) inference are often short due to the high costs of sampling cell systems. Moreover, experimentalists lack a set of quantitative guidelines that prescribe the minimal number of samples required to infer a reliable GRN model. We study the temporal resolution of data vs. quality of GRN inference in order to ultimately overcome this deficit. The evolution of a Markovian jump process model for the Ras/cAMP/PKA pathway of proteins and metabolites in the G 1 phase of the Saccharomyces cerevisiae cell cycle is sampled at a number of different rates. For each time-series we infer a linear regression model of the GRN using the LASSO method. The inferred network topology is evaluated in terms of the area under the precision-recall curve (AUPR). By plotting the AUPR against the number of samples, we show that the tradeoff has a, roughly speaking, sigmoid shape. An optimal number of samples corresponds to values on the ridge of the sigmoid.
Type: | Proceedings paper |
---|---|
Title: | Experimental Design Trade-offs for Gene Regulatory Network Inference: an in Silico Study of the Yeast Saccharomyces Cerevisiae Cell Cycle |
Event: | IEEE 56th Annual Conference on Decision and Control (CDC) |
Location: | Melbourne, AUSTRALIA |
Dates: | 12 December 2017 - 15 December 2017 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/CDC.2017.8263701 |
Publisher version: | https://doi.org/10.1109/CDC.2017.8263701 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Mathematical model, Economic indicators, Inference, algorithms, In vivo, Data models, Navigation, Throughput |
UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences |
URI: | https://discovery.ucl.ac.uk/id/eprint/10059506 |
Archive Staff Only
View Item |