A single-cell model of PIP3 dynamics using chemical dimerization

Aidan Macnamara, Frank Stein, Suihan Feng, Carsten Schultz, Julio Saez-Rodriguez

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Most cellular processes are driven by simple biochemical mechanisms such as protein and lipid phosphorylation, but the sum of all these conversions is exceedingly complex. Hence, intuition alone is not enough to discern the underlying mechanisms in the light of experimental data. Toward this end, mathematical models provide a conceptual and numerical framework to formally evaluate the plausibility of biochemical processes. To illustrate the use of these models, here we built a mechanistic computational model of PI3K (phosphatidylinositol 3-kinase) activity, to determine the kinetics of lipid metabolizing enzymes in single cells. The model is trained to data generated upon perturbation with a reversible small-molecule based chemical dimerization system that allows for the very rapid manipulation of the PIP3 (phosphatidylinositol 3,4,5-trisphosphate) signaling pathway, and monitored with live-cell microscopy. We find that the rapid relaxation system used in this work decreased the uncertainty of estimating kinetic parameters compared to methods based on in vitro assays. We also examined the use of Bayesian parameter inference and how the use of such a probabilistic method gives information on the kinetics of PI3K and PTEN activity.

Original languageEnglish (US)
Pages (from-to)2868-2876
Number of pages9
JournalBioorganic and Medicinal Chemistry
Volume23
Issue number12
DOIs
StatePublished - May 29 2015

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Keywords

  • Bayesian
  • Dynamic
  • Modeling
  • Parameter estimation
  • Phosphoinositide signaling

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Medicine
  • Molecular Biology
  • Pharmaceutical Science
  • Drug Discovery
  • Clinical Biochemistry
  • Organic Chemistry

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