Evidence map›Paper›PMID 21314456›Full record

ArticleJournal of computational biology : a journal of computational molecular cell biology2011

Adaptive control model reveals systematic feedback and key molecules in metabolic pathway regulation.

Chang F Quo, Richard A Moffitt, Alfred H Merrill, May D Wang

Abstract read
In one paragraph

Article in Journal of computational biology : a journal of computational molecular cell biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Multiscale computational models of complex biological systems.Annual review of biomedical engineering · 2013
    Review
  2. Article
  3. Biological Interpretation of Model-Reference Adaptive Control in a Mass Action Kinetics Metabolic Pathway Model.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2011
    Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Chang F QuoWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA. cfquo@gatech.edu
Richard A Moffitt
Alfred H Merrill
May D Wang

Funding

Tissue and Animal UseU54CA119338 · NCI · EMORY UNIVERSITY · PI SIMONS, JONATHAN W. · 2005 to 2009
$18.4M
Nanotechnology Linking Biomarkers with Cancer BehaviorR01CA108468 · NCI · EMORY UNIVERSITY · PI NIE, SHUMING · 2004 to 2008
$6.9M
NCI NIH HHS R01CA108468NCI NIH HHS U54 CA119338NCI NIH HHS U54CA119338
6 · The paper itself

Abstract

Robust behavior in metabolic pathways resembles stabilized performance in systems under autonomous control. This suggests we can apply control theory to study existing regulation in these cellular networks. Here, we use model-reference adaptive control (MRAC) to investigate the dynamics of de novo sphingolipid synthesis regulation in a combined theoretical and experimental case study. The effects of serine palmitoyltransferase over-expression on this pathway are studied in vitro using human embryonic kidney cells. We report two key results from comparing numerical simulations with observed data. First, MRAC simulations of pathway dynamics are comparable to simulations from a standard model using mass action kinetics. The root-sum-square (RSS) between data and simulations in both cases differ by less than 5%. Second, MRAC simulations suggest systematic pathway regulation in terms of adaptive feedback from individual molecules. In response to increased metabolite levels available for de novo sphingolipid synthesis, feedback from molecules along the main artery of the pathway is regulated more frequently and with greater amplitude than from other molecules along the branches. These biological insights are consistent with current knowledge while being new that they may guide future research in sphingolipid biology. In summary, we report a novel approach to study regulation in cellular networks by applying control theory in the context of robust metabolic pathways. We do this to uncover potential insight into the dynamics of regulation and the reverse engineering of cellular networks for systems biology. This new modeling approach and the implementation routines designed for this case study may be extended to other systems. Supplementary Material is available at www.liebertonline.com/cmb .

Indexed as

Feedback, PhysiologicalMetabolic Networks and PathwaysModels, BiologicalAlgorithmsCell LineHumansSphingolipidsSphingolipids

Identifiers

PMID21314456
PMCPMC3123842

What OpenQuestion holds

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Registered trials

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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.