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Automating the Development of Metabolic Network Models

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

Automating the Development of Metabolic Network Models. / Rozanski, Robert; Bragaglia, Stefano; Ray, Oliver; King, Ross.

Computational Methods in Systems Biology: 13th International Conference, CMSB 2015, Nantes, France, September 16-18, 2015, Proceedings. ed. / Olivier Roux; Jérémie Bourdon. Springer, 2015. p. 145-156 (Lecture Notes in Computer Science; Vol. 9308).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Rozanski, R, Bragaglia, S, Ray, O & King, R 2015, Automating the Development of Metabolic Network Models. in O Roux & J Bourdon (eds), Computational Methods in Systems Biology: 13th International Conference, CMSB 2015, Nantes, France, September 16-18, 2015, Proceedings. Lecture Notes in Computer Science, vol. 9308, Springer, pp. 145-156, 13th Conference on Computational Methods for Systems Biology, 16-18 September 2015, Nantes, France, Nantes, France, 16/09/15. https://doi.org/10.1007/978-3-319-23401-4_13

APA

Rozanski, R., Bragaglia, S., Ray, O., & King, R. (2015). Automating the Development of Metabolic Network Models. In O. Roux, & J. Bourdon (Eds.), Computational Methods in Systems Biology: 13th International Conference, CMSB 2015, Nantes, France, September 16-18, 2015, Proceedings (pp. 145-156). (Lecture Notes in Computer Science; Vol. 9308). Springer. https://doi.org/10.1007/978-3-319-23401-4_13

Vancouver

Rozanski R, Bragaglia S, Ray O, King R. Automating the Development of Metabolic Network Models. In Roux O, Bourdon J, editors, Computational Methods in Systems Biology: 13th International Conference, CMSB 2015, Nantes, France, September 16-18, 2015, Proceedings. Springer. 2015. p. 145-156. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-23401-4_13

Author

Rozanski, Robert ; Bragaglia, Stefano ; Ray, Oliver ; King, Ross. / Automating the Development of Metabolic Network Models. Computational Methods in Systems Biology: 13th International Conference, CMSB 2015, Nantes, France, September 16-18, 2015, Proceedings. editor / Olivier Roux ; Jérémie Bourdon. Springer, 2015. pp. 145-156 (Lecture Notes in Computer Science).

Bibtex

@inproceedings{abfd040385594ac299fe74835b8159b7,
title = "Automating the Development of Metabolic Network Models",
abstract = "Although substantial progress has been made in the automa-tion of many areas of systems biology, from data processing and model building to experimentation, comparatively little work has been done on more encompassing systems that combine all of these aspects. This paper presents an active learning system called Huginn that integrates experiment design and model revision in order to automate scientific reasoning about Metabolic Network Models. We validate our approach in a simulated environment using test cases derived from a state-of-the-art model of yeast metabolism. We show that Huginn can not only improve metabolic models but that it is able to solve a wider range of biochemical problems than previous methods and use a wider range of experiment types. Also, we show how design of extended crucial experiments can be automated using Abductive Logic Programming for the first time.",
author = "Robert Rozanski and Stefano Bragaglia and Oliver Ray and Ross King",
year = "2015",
month = "9",
day = "2",
doi = "10.1007/978-3-319-23401-4_13",
language = "English",
isbn = "9783319234007",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "145--156",
editor = "Olivier Roux and Bourdon, { J{\'e}r{\'e}mie}",
booktitle = "Computational Methods in Systems Biology",

}

RIS - suitable for import to EndNote

TY - GEN

T1 - Automating the Development of Metabolic Network Models

AU - Rozanski, Robert

AU - Bragaglia, Stefano

AU - Ray, Oliver

AU - King, Ross

PY - 2015/9/2

Y1 - 2015/9/2

N2 - Although substantial progress has been made in the automa-tion of many areas of systems biology, from data processing and model building to experimentation, comparatively little work has been done on more encompassing systems that combine all of these aspects. This paper presents an active learning system called Huginn that integrates experiment design and model revision in order to automate scientific reasoning about Metabolic Network Models. We validate our approach in a simulated environment using test cases derived from a state-of-the-art model of yeast metabolism. We show that Huginn can not only improve metabolic models but that it is able to solve a wider range of biochemical problems than previous methods and use a wider range of experiment types. Also, we show how design of extended crucial experiments can be automated using Abductive Logic Programming for the first time.

AB - Although substantial progress has been made in the automa-tion of many areas of systems biology, from data processing and model building to experimentation, comparatively little work has been done on more encompassing systems that combine all of these aspects. This paper presents an active learning system called Huginn that integrates experiment design and model revision in order to automate scientific reasoning about Metabolic Network Models. We validate our approach in a simulated environment using test cases derived from a state-of-the-art model of yeast metabolism. We show that Huginn can not only improve metabolic models but that it is able to solve a wider range of biochemical problems than previous methods and use a wider range of experiment types. Also, we show how design of extended crucial experiments can be automated using Abductive Logic Programming for the first time.

U2 - 10.1007/978-3-319-23401-4_13

DO - 10.1007/978-3-319-23401-4_13

M3 - Conference contribution

SN - 9783319234007

T3 - Lecture Notes in Computer Science

SP - 145

EP - 156

BT - Computational Methods in Systems Biology

A2 - Roux, Olivier

A2 - Bourdon, Jérémie

PB - Springer

ER -