Literature DB >> 16146334

A stochastic version of corticosteriod pharmacogenomic model.

Xiaohong Qi1.   

Abstract

The purpose of this study was to develop a stochastic version of corticosteriod fifth generation pharmacogenomic model. The Gillespie algorithm was used to generate the independent time courses of the receptor messenger RNA (mRNA). Initial parameters for the stochastic simulation were adapted from the study by Jin et al. The result obtained from the proposed stochastic model showed an overall agreement with the deterministic fifth generation model. This study suggested that because the stochastic model takes into account the "noise" nature of gene regulation, it would have potential application in pharmacogenomic modeling.

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Year:  2005        PMID: 16146334      PMCID: PMC2751503          DOI: 10.1208/aapsj070113

Source DB:  PubMed          Journal:  AAPS J        ISSN: 1550-7416            Impact factor:   4.009


  37 in total

1.  Engineering stability in gene networks by autoregulation.

Authors:  A Becskei; L Serrano
Journal:  Nature       Date:  2000-06-01       Impact factor: 49.962

2.  Pharmacokinetic variability and therapeutic drug monitoring actions at steady state.

Authors:  M Ramanathan
Journal:  Pharm Res       Date:  2000-05       Impact factor: 4.200

3.  Stochasticity in transcriptional regulation: origins, consequences, and mathematical representations.

Authors:  T B Kepler; T C Elston
Journal:  Biophys J       Date:  2001-12       Impact factor: 4.033

4.  Robustness of circadian rhythms with respect to molecular noise.

Authors:  Didier Gonze; José Halloy; Albert Goldbeter
Journal:  Proc Natl Acad Sci U S A       Date:  2002-01-15       Impact factor: 11.205

5.  Use of a stochastic model to develop understanding of the impact of different patterns of antiretroviral drug use on resistance development.

Authors:  A N Phillips; M Youle; M Johnson; C Loveday
Journal:  AIDS       Date:  2001-11-23       Impact factor: 4.177

Review 6.  Statistical issues with microarrays: processing and analysis.

Authors:  Robert Nadon; Jennifer Shoemaker
Journal:  Trends Genet       Date:  2002-05       Impact factor: 11.639

7.  Regulation of noise in the expression of a single gene.

Authors:  Ertugrul M Ozbudak; Mukund Thattai; Iren Kurtser; Alan D Grossman; Alexander van Oudenaarden
Journal:  Nat Genet       Date:  2002-04-22       Impact factor: 38.330

8.  Pharmacodynamics and pharmacogenomics of methylprednisolone during 7-day infusions in rats.

Authors:  Rohini Ramakrishnan; Debra C DuBois; Richard R Almon; Nancy A Pyszczynski; William J Jusko
Journal:  J Pharmacol Exp Ther       Date:  2002-01       Impact factor: 4.030

9.  Modeling interactions between adrenal suppression and T-helper lymphocyte trafficking during multiple dosing of methylprednisolone.

Authors:  F S Chow; A Sharma; W J Jusko
Journal:  J Pharmacokinet Biopharm       Date:  1999-12

10.  Pharmacokinetic and pharmacoimmunodynamic interactions between prednisolone and sirolimus in adrenalectomized rats.

Authors:  G M Ferron; N A Pyszczynski; W J Jusko
Journal:  J Pharmacokinet Biopharm       Date:  1999-02
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  1 in total

1.  Mixed Effects Modeling Using Stochastic Differential Equations: Illustrated by Pharmacokinetic Data of Nicotinic Acid in Obese Zucker Rats.

Authors:  Jacob Leander; Joachim Almquist; Christine Ahlström; Johan Gabrielsson; Mats Jirstrand
Journal:  AAPS J       Date:  2015-02-19       Impact factor: 4.009

  1 in total

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