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Experimental design trade-offs for gene regulatory network inference: An in silico study of the yeast Saccharomyces cerevisiae cell cycle

Markdahl, Johan ; Colombo, Nicolo ; Thunberg, Johan LU and Goncalves, Jorge (2017) 56th IEEE Annual Conference on Decision and Control, CDC 2017 p.423-428
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... (More)
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. (Less)
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author
; ; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2017 IEEE 56th Annual Conference on Decision and Control (CDC)
pages
423 - 428
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
56th IEEE Annual Conference on Decision and Control, CDC 2017
conference location
Melbourne, Australia
conference dates
2017-12-12 - 2017-12-15
external identifiers
  • scopus:85046152661
ISBN
978-1-5090-2873-3
DOI
10.1109/CDC.2017.8263701
language
English
LU publication?
no
id
f9d93a8e-bf69-41da-bdfa-7da2c8412aa7
date added to LUP
2024-09-05 14:54:38
date last changed
2025-04-04 15:08:58
@inproceedings{f9d93a8e-bf69-41da-bdfa-7da2c8412aa7,
  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.}},
  author       = {{Markdahl, Johan and Colombo, Nicolo and Thunberg, Johan and Goncalves, Jorge}},
  booktitle    = {{2017 IEEE 56th Annual Conference on Decision and Control (CDC)}},
  isbn         = {{978-1-5090-2873-3}},
  language     = {{eng}},
  pages        = {{423--428}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Experimental design trade-offs for gene regulatory network inference: An in silico study of the yeast Saccharomyces cerevisiae cell cycle}},
  url          = {{http://dx.doi.org/10.1109/CDC.2017.8263701}},
  doi          = {{10.1109/CDC.2017.8263701}},
  year         = {{2017}},
}