Literature DB >> 21314456

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

Chang F Quo1, Richard A Moffitt, Alfred H Merrill, May D Wang.   

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 .

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Year:  2011        PMID: 21314456      PMCID: PMC3123842          DOI: 10.1089/cmb.2010.0215

Source DB:  PubMed          Journal:  J Comput Biol        ISSN: 1066-5277            Impact factor:   1.479


  30 in total

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Journal:  Proc Natl Acad Sci U S A       Date:  2000-04-25       Impact factor: 11.205

Review 2.  Reverse engineering of biological complexity.

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Review 3.  De novo sphingolipid biosynthesis: a necessary, but dangerous, pathway.

Authors:  Alfred H Merrill
Journal:  J Biol Chem       Date:  2002-05-13       Impact factor: 5.157

Review 4.  Biologically active sphingolipids in cancer pathogenesis and treatment.

Authors:  Besim Ogretmen; Yusuf A Hannun
Journal:  Nat Rev Cancer       Date:  2004-08       Impact factor: 60.716

5.  Robustness in bacterial chemotaxis.

Authors:  U Alon; M G Surette; N Barkai; S Leibler
Journal:  Nature       Date:  1999-01-14       Impact factor: 49.962

6.  Robustness in simple biochemical networks.

Authors:  N Barkai; S Leibler
Journal:  Nature       Date:  1997-06-26       Impact factor: 49.962

Review 7.  Sphingolipids in food and the emerging importance of sphingolipids to nutrition.

Authors:  H Vesper; E M Schmelz; M N Nikolova-Karakashian; D L Dillehay; D V Lynch; A H Merrill
Journal:  J Nutr       Date:  1999-07       Impact factor: 4.798

8.  Heat-induced elevation of ceramide in Saccharomyces cerevisiae via de novo synthesis.

Authors:  G B Wells; R C Dickson; R L Lester
Journal:  J Biol Chem       Date:  1998-03-27       Impact factor: 5.157

9.  Acute activation of de novo sphingolipid biosynthesis upon heat shock causes an accumulation of ceramide and subsequent dephosphorylation of SR proteins.

Authors:  Gary M Jenkins; L Ashley Cowart; Paola Signorelli; Benjamin J Pettus; Charles E Chalfant; Yusuf A Hannun
Journal:  J Biol Chem       Date:  2002-08-27       Impact factor: 5.157

Review 10.  Computational lipidomics: a multiplexed analysis of dynamic changes in membrane lipid composition during signal transduction.

Authors:  Jeffrey S Forrester; Stephen B Milne; Pavlina T Ivanova; H Alex Brown
Journal:  Mol Pharmacol       Date:  2004-04       Impact factor: 4.436

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  4 in total

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Authors:  Alfred H Merrill
Journal:  Chem Rev       Date:  2011-09-26       Impact factor: 60.622

2.  Reverse engineering biomolecular systems using -omic data: challenges, progress and opportunities.

Authors:  Chang F Quo; Chanchala Kaddi; John H Phan; Amin Zollanvari; Mingqing Xu; May D Wang; Gil Alterovitz
Journal:  Brief Bioinform       Date:  2012-07       Impact factor: 11.622

3.  Biological Interpretation of Model-Reference Adaptive Control in a Mass Action Kinetics Metabolic Pathway Model.

Authors:  Chang F Quo; May D Wang
Journal:  Proceedings (IEEE Int Conf Bioinformatics Biomed)       Date:  2011-11

Review 4.  Multiscale computational models of complex biological systems.

Authors:  Joseph Walpole; Jason A Papin; Shayn M Peirce
Journal:  Annu Rev Biomed Eng       Date:  2013-04-29       Impact factor: 9.590

  4 in total

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