Integrative information management for systems biology

Neil Swainston; Daniel Jameson; Peter Li; Irena Spasic; Pedro Mendes; Norman W. Paton

doi:10.1007/978-3-642-15120-0_13

Integrative information management for systems biology

Neil Swainston, Daniel Jameson, Peter Li, Irena Spasic, Pedro Mendes, Norman W. Paton

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Systems biology develops mathematical models of biological systems that seek to explain, or better still predict, how the system behaves. In bottom-up systems biology, systematic quantitative experimentation is carried out to obtain the data required to parameterize models, which can then be analyzed and simulated. This paper describes an approach to integrated information management that supports bottom-up systems biology, with a view to automating, or at least minimizing the manual effort required during, creation of quantitative models from qualitative models and experimental data. Automating the process makes model construction more systematic, supports good practice at all stages in the pipeline, and allows timely integration of high throughput experimental results into models. © 2010 Springer-Verlag.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)\|Lect. Notes Comput. Sci.
Pages	164-178
Number of pages	14
Volume	6254
DOIs	https://doi.org/10.1007/978-3-642-15120-0_13
Publication status	Published - 2010
Event	7th International Conference on Data Integration in the Life Sciences, DILS 2010 - Gothenburg Duration: 1 Jul 2010 → …

Conference

Conference	7th International Conference on Data Integration in the Life Sciences, DILS 2010
City	Gothenburg
Period	1/07/10 → …

Keywords

computationalsystems biology
workflow

Research Beacons, Institutes and Platforms

Manchester Institute of Biotechnology

Access to Document

10.1007/978-3-642-15120-0_13

Cite this

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title = "Integrative information management for systems biology",

abstract = "Systems biology develops mathematical models of biological systems that seek to explain, or better still predict, how the system behaves. In bottom-up systems biology, systematic quantitative experimentation is carried out to obtain the data required to parameterize models, which can then be analyzed and simulated. This paper describes an approach to integrated information management that supports bottom-up systems biology, with a view to automating, or at least minimizing the manual effort required during, creation of quantitative models from qualitative models and experimental data. Automating the process makes model construction more systematic, supports good practice at all stages in the pipeline, and allows timely integration of high throughput experimental results into models. {\textcopyright} 2010 Springer-Verlag.",

keywords = "computationalsystems biology, workflow",

author = "Neil Swainston and Daniel Jameson and Peter Li and Irena Spasic and Pedro Mendes and Paton, {Norman W.}",

note = "Buj15 Times Cited:0 Cited References Count:22 Lecture Notes in Bioinformatics; 7th International Conference on Data Integration in the Life Sciences, DILS 2010 ; Conference date: 01-07-2010",

year = "2010",

doi = "10.1007/978-3-642-15120-0_13",

language = "English",

isbn = "3642151191",

volume = "6254",

pages = "164--178",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.",

}

Swainston, N, Jameson, D, Li, P, Spasic, I, Mendes, P & Paton, NW 2010, Integrative information management for systems biology. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.. vol. 6254, pp. 164-178, 7th International Conference on Data Integration in the Life Sciences, DILS 2010, Gothenburg, 1/07/10. https://doi.org/10.1007/978-3-642-15120-0_13

Integrative information management for systems biology. / Swainston, Neil; Jameson, Daniel; Li, Peter et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.. Vol. 6254 2010. p. 164-178.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Integrative information management for systems biology

AU - Swainston, Neil

AU - Jameson, Daniel

AU - Li, Peter

AU - Spasic, Irena

AU - Mendes, Pedro

AU - Paton, Norman W.

N1 - Buj15 Times Cited:0 Cited References Count:22 Lecture Notes in Bioinformatics

PY - 2010

Y1 - 2010

N2 - Systems biology develops mathematical models of biological systems that seek to explain, or better still predict, how the system behaves. In bottom-up systems biology, systematic quantitative experimentation is carried out to obtain the data required to parameterize models, which can then be analyzed and simulated. This paper describes an approach to integrated information management that supports bottom-up systems biology, with a view to automating, or at least minimizing the manual effort required during, creation of quantitative models from qualitative models and experimental data. Automating the process makes model construction more systematic, supports good practice at all stages in the pipeline, and allows timely integration of high throughput experimental results into models. © 2010 Springer-Verlag.

AB - Systems biology develops mathematical models of biological systems that seek to explain, or better still predict, how the system behaves. In bottom-up systems biology, systematic quantitative experimentation is carried out to obtain the data required to parameterize models, which can then be analyzed and simulated. This paper describes an approach to integrated information management that supports bottom-up systems biology, with a view to automating, or at least minimizing the manual effort required during, creation of quantitative models from qualitative models and experimental data. Automating the process makes model construction more systematic, supports good practice at all stages in the pipeline, and allows timely integration of high throughput experimental results into models. © 2010 Springer-Verlag.

KW - computationalsystems biology

KW - workflow

U2 - 10.1007/978-3-642-15120-0_13

DO - 10.1007/978-3-642-15120-0_13

M3 - Conference contribution

SN - 3642151191

SN - 9783642151194

VL - 6254

SP - 164

EP - 178

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.

T2 - 7th International Conference on Data Integration in the Life Sciences, DILS 2010

Y2 - 1 July 2010

ER -

Integrative information management for systems biology

Abstract

Conference

Keywords

Research Beacons, Institutes and Platforms

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