Network inference using steady-state data and goldbeter-koshland kinetics

Chris J. Oates, Bryan T. Hennessy, Yiling Lu, Gordon Mills, Sach Mukherjee

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Motivation: Network inference approaches are widely used to shed light on regulatory interplay between molecular players such as genes and proteins. Biochemical processes underlying networks of interest (e.g. gene regulatory or protein signalling networks) are generally nonlinear. In many settings, knowledge is available concerning relevant chemical kinetics. However, existing network inference methods for continuous, steady-state data are typically rooted in statistical formulations, which do not exploit chemical kinetics to guide inference. Results: Herein, we present an approach to network inference for steady-state data that is rooted in non-linear descriptions of biochemical mechanism. We use equilibrium analysis of chemical kinetics to obtain functional forms that are in turn used to infer networks using steady-state data. The approach we propose is directly applicable to conventional steady-state gene expression or proteomic data and does not require knowledge of either network topology or any kinetic parameters. We illustrate the approach in the context of protein phosphorylation networks, using data simulated from a recent mechanistic model and proteomic data from cancer cell lines. In the former, the true network is known and used for assessment, whereas in the latter, results are compared against known biochemistry. We find that the proposed methodology is more effective at estimating network topology than methods based on linear models.

Original languageEnglish (US)
Article numberbts459
Pages (from-to)2342-2348
Number of pages7
JournalBioinformatics
Volume28
Issue number18
DOIs
StatePublished - Sep 1 2012
Externally publishedYes

Fingerprint

Reaction kinetics
Kinetics
Proteins
Genes
Topology
Proteomics
Chemical Kinetics
Biochemistry
Phosphorylation
Biochemical Phenomena
Kinetic parameters
Gene expression
Regulator Genes
Cells
Network Topology
Linear Models
Protein Phosphorylation
Gene
Equilibrium Analysis
Protein

ASJC Scopus subject areas

  • Statistics and Probability
  • Medicine(all)
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

Oates, C. J., Hennessy, B. T., Lu, Y., Mills, G., & Mukherjee, S. (2012). Network inference using steady-state data and goldbeter-koshland kinetics. Bioinformatics, 28(18), 2342-2348. [bts459]. https://doi.org/10.1093/bioinformatics/bts459

Network inference using steady-state data and goldbeter-koshland kinetics. / Oates, Chris J.; Hennessy, Bryan T.; Lu, Yiling; Mills, Gordon; Mukherjee, Sach.

In: Bioinformatics, Vol. 28, No. 18, bts459, 01.09.2012, p. 2342-2348.

Research output: Contribution to journalArticle

Oates, CJ, Hennessy, BT, Lu, Y, Mills, G & Mukherjee, S 2012, 'Network inference using steady-state data and goldbeter-koshland kinetics', Bioinformatics, vol. 28, no. 18, bts459, pp. 2342-2348. https://doi.org/10.1093/bioinformatics/bts459
Oates, Chris J. ; Hennessy, Bryan T. ; Lu, Yiling ; Mills, Gordon ; Mukherjee, Sach. / Network inference using steady-state data and goldbeter-koshland kinetics. In: Bioinformatics. 2012 ; Vol. 28, No. 18. pp. 2342-2348.
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