Systems approaches to modelling pathways and networks.

Thomas Pfau, Nils Christian, Oliver Ebenhöh

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

It has become commonly accepted that systems approaches to biology are of outstanding importance to gain understanding from the vast amount of data which is presently being generated by advancing high-throughput technologies. The diversity of methods to model pathways and networks has significantly expanded over the past two decades. Modern and traditional approaches are equally important and recent activities aim at integrating the advantages of both. While traditional methods, based on differential equations, are useful to study the dynamics of small systems, modern constraint-based models can be applied to genome-scale systems, but are not able to capture dynamic features. Integrating different approaches is important to develop consistent theoretical descriptions encompassing various scales of biological information. The rapid progress of the field of theoretical systems biology, however, demonstrates how our fundamental theoretical understanding of biology is gaining momentum. The scientific community has apparently accepted the challenge to truly understand the principles of life.
Original languageEnglish
Pages (from-to)266-279
Number of pages14
JournalBriefings in Functional Genomics
Volume10
Issue number5
Early online date8 Sep 2011
DOIs
Publication statusPublished - 2011

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Systems Biology
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Differential equations
Genes
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Genome
Technology

Keywords

  • systems biology
  • mathematical model
  • metabolism
  • dynamic systems
  • genome-scale networks
  • constraint-based modelling

Cite this

Systems approaches to modelling pathways and networks. / Pfau, Thomas; Christian, Nils; Ebenhöh, Oliver.

In: Briefings in Functional Genomics, Vol. 10, No. 5, 2011, p. 266-279.

Research output: Contribution to journalArticle

Pfau, Thomas ; Christian, Nils ; Ebenhöh, Oliver. / Systems approaches to modelling pathways and networks. In: Briefings in Functional Genomics. 2011 ; Vol. 10, No. 5. pp. 266-279.
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