Literature DB >> 8375615

Microcomputer-assisted kinetic modeling of mammalian gene expression.

J L Hargrove1.   

Abstract

New software for microcomputers enables predictive, quantitative models of genetic systems to be produced that can account for the elements of time, scale, and feedback control of hierarchical systems. The flow of genetic information during protein synthesis can be addressed by treating each intermediate as a kinetic element in a linked series of reactions. When the rate of transcription changes, the time required to achieve a new level of the encoded protein is expected to be a function of the conversion rates or half-lives of all intermediates. Kinetic modeling may be used to make predictions and integrate primary data concerning rates of transcription, nuclear mRNA dynamics, nucleocytoplasmic transport, translational control, and other processes that govern the rate of synthesis for specific proteins.

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Year:  1993        PMID: 8375615     DOI: 10.1096/fasebj.7.12.8375615

Source DB:  PubMed          Journal:  FASEB J        ISSN: 0892-6638            Impact factor:   5.191


  9 in total

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3.  Kinetic modeling and mathematical analysis indicate that acute phase gene expression in Hep 3B cells is regulated by both transcriptional and posttranscriptional mechanisms.

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4.  Induction of COX-2 by LPS in macrophages is regulated by Tpl2-dependent CREB activation signals.

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5.  Type of noise defines global attractors in bistable molecular regulatory systems.

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6.  Genome-wide analysis of mRNA decay rates and their determinants in Arabidopsis thaliana.

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7.  Embedding mRNA stability in correlation analysis of time-series gene expression data.

Authors:  Lorenzo Farina; Alberto De Santis; Samanta Salvucci; Giorgio Morelli; Ida Ruberti
Journal:  PLoS Comput Biol       Date:  2008-08-01       Impact factor: 4.475

Review 8.  Mathematical Models in the Description of Pregnane X Receptor (PXR)-Regulated Cytochrome P450 Enzyme Induction.

Authors:  Jurjen Duintjer Tebbens; Malek Azar; Elfriede Friedmann; Martin Lanzendörfer; Petr Pávek
Journal:  Int J Mol Sci       Date:  2018-06-15       Impact factor: 5.923

9.  A computational model to predict rat ovarian steroid secretion from in vitro experiments with endocrine disruptors.

Authors:  Nadia Quignot; Frédéric Y Bois
Journal:  PLoS One       Date:  2013-01-11       Impact factor: 3.240

  9 in total

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