Evidence map›Paper›PMID 22369292›Full record

ArticleBMC systems biology2012

In silico labeling reveals the time-dependent label half-life and transit-time in dynamical systems.

Thomas Maiwald, Julie Blumberg, Andreas Raue, Stefan Hengl, Marcel Schilling, Sherwin Kb Sy, Verena Becker, Ursula Klingmüller, Jens Timmer

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Article in BMC systems biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Thomas MaiwaldCenter for Systems Biology, Freiburg, Germany. tmaiwald@gmail.com
Julie Blumberg
Andreas Raue
Stefan Hengl
Marcel Schilling
Sherwin Kb Sy
Verena Becker
Ursula Klingmüller
Jens Timmer

Funding

Systems Biology of Cell Decision ProcessesP50GM068762 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI YAFFE, MICHAEL B · 2003 to 2012
$32.3M
NIGMS NIH HHS GM68762
6 · The paper itself

Abstract

backgroundMathematical models of dynamical systems facilitate the computation of characteristic properties that are not accessible experimentally. In cell biology, two main properties of interest are (1) the time-period a protein is accessible to other molecules in a certain state - its half-life - and (2) the time it spends when passing through a subsystem - its transit-time. We discuss two approaches to quantify the half-life, present the novel method of in silico labeling, and introduce the label half-life and label transit-time. The developed method has been motivated by laboratory tracer experiments. To investigate the kinetic properties and behavior of a substance of interest, we computationally label this species in order to track it throughout its life cycle. The corresponding mathematical model is extended by an additional set of reactions for the labeled species, avoiding any double-counting within closed circuits, correcting for the influences of upstream fluxes, and taking into account combinatorial multiplicity for complexes or reactions with several reactants or products. A profile likelihood approach is used to estimate confidence intervals on the label half-life and transit-time.

resultsApplication to the JAK-STAT signaling pathway in Epo-stimulated BaF3-EpoR cells enabled the calculation of the time-dependent label half-life and transit-time of STAT species. The results were robust against parameter uncertainties.

conclusionsOur approach renders possible the estimation of species and label half-lives and transit-times. It is applicable to large non-linear systems and an implementation is provided within the PottersWheel modeling framework (http://www.potterswheel.de).

Indexed as

Computational BiologyJanus KinasesLikelihood FunctionsSignal TransductionSTAT Transcription FactorsTerminology as TopicTime FactorsJanus KinasesSTAT Transcription Factors

Identifiers

PMID22369292
PMCPMC3395849

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.