Literature DB >> 21417596

Mathematical modeling of gene expression: a guide for the perplexed biologist.

Ahmet Ay1, David N Arnosti.   

Abstract

The detailed analysis of transcriptional networks holds a key for understanding central biological processes, and interest in this field has exploded due to new large-scale data acquisition techniques. Mathematical modeling can provide essential insights, but the diversity of modeling approaches can be a daunting prospect to investigators new to this area. For those interested in beginning a transcriptional mathematical modeling project, we provide here an overview of major types of models and their applications to transcriptional networks. In this discussion of recent literature on thermodynamic, Boolean, and differential equation models, we focus on considerations critical for choosing and validating a modeling approach that will be useful for quantitative understanding of biological systems.

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Year:  2011        PMID: 21417596      PMCID: PMC3086598          DOI: 10.3109/10409238.2011.556597

Source DB:  PubMed          Journal:  Crit Rev Biochem Mol Biol        ISSN: 1040-9238            Impact factor:   8.250


  83 in total

1.  MEG (Model Extender for Gepasi): a program for the modelling of complex, heterogeneous, cellular systems.

Authors:  P Mendes; D B Kell
Journal:  Bioinformatics       Date:  2001-03       Impact factor: 6.937

Review 2.  Fundamentally different logic of gene regulation in eukaryotes and prokaryotes.

Authors:  K Struhl
Journal:  Cell       Date:  1999-07-09       Impact factor: 41.582

3.  DNA looping and physical constraints on transcription regulation.

Authors:  José M G Vilar; Stanislas Leibler
Journal:  J Mol Biol       Date:  2003-08-29       Impact factor: 5.469

4.  Robustness and fragility of Boolean models for genetic regulatory networks.

Authors:  Madalena Chaves; Réka Albert; Eduardo D Sontag
Journal:  J Theor Biol       Date:  2005-03-19       Impact factor: 2.691

5.  Analysis of homeodomain specificities allows the family-wide prediction of preferred recognition sites.

Authors:  Marcus B Noyes; Ryan G Christensen; Atsuya Wakabayashi; Gary D Stormo; Michael H Brodsky; Scot A Wolfe
Journal:  Cell       Date:  2008-06-27       Impact factor: 41.582

6.  Genomic cis-regulatory logic: experimental and computational analysis of a sea urchin gene.

Authors:  C H Yuh; H Bolouri; E H Davidson
Journal:  Science       Date:  1998-03-20       Impact factor: 47.728

Review 7.  From gradients to stripes in Drosophila embryogenesis: filling in the gaps.

Authors:  R Rivera-Pomar; H Jäckle
Journal:  Trends Genet       Date:  1996-11       Impact factor: 11.639

8.  Synergy between the hunchback and bicoid morphogens is required for anterior patterning in Drosophila.

Authors:  M Simpson-Brose; J Treisman; C Desplan
Journal:  Cell       Date:  1994-09-09       Impact factor: 41.582

9.  Biological systems from an engineer's point of view.

Authors:  Gregory T Reeves; Scott E Fraser
Journal:  PLoS Biol       Date:  2009-01-20       Impact factor: 8.029

10.  Evolution acts on enhancer organization to fine-tune gradient threshold readouts.

Authors:  Justin Crocker; Yoichiro Tamori; Albert Erives
Journal:  PLoS Biol       Date:  2008-11-04       Impact factor: 8.029

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  46 in total

1.  Quantitatively predictable control of Drosophila transcriptional enhancers in vivo with engineered transcription factors.

Authors:  Justin Crocker; Garth R Ilsley; David L Stern
Journal:  Nat Genet       Date:  2016-02-08       Impact factor: 38.330

2.  Nucleocytoplasmic Shuttling of the Mechanosensitive Transcription Factors MRTF and YAP /TAZ.

Authors:  Michael Kofler; András Kapus
Journal:  Methods Mol Biol       Date:  2021

Review 3.  Systems biophysics of gene expression.

Authors:  Jose M G Vilar; Leonor Saiz
Journal:  Biophys J       Date:  2013-06-18       Impact factor: 4.033

4.  Causal Gene Regulatory Network Modeling and Genomics: Second-Generation Challenges.

Authors:  Ellen V Rothenberg
Journal:  J Comput Biol       Date:  2019-05-07       Impact factor: 1.479

5.  Sequence-based model of gap gene regulatory network.

Authors:  Konstantin Kozlov; Vitaly Gursky; Ivan Kulakovskiy; Maria Samsonova
Journal:  BMC Genomics       Date:  2014-12-19       Impact factor: 3.969

6.  Boolean modeling of gene regulatory networks: Driesch redux.

Authors:  David N Arnosti; Ahmet Ay
Journal:  Proc Natl Acad Sci U S A       Date:  2012-10-01       Impact factor: 11.205

Review 7.  How and why to build a mathematical model: A case study using prion aggregation.

Authors:  Mikahl Banwarth-Kuhn; Suzanne Sindi
Journal:  J Biol Chem       Date:  2020-01-31       Impact factor: 5.157

8.  Quantitative modeling of a gene's expression from its intergenic sequence.

Authors:  Md Abul Hassan Samee; Saurabh Sinha
Journal:  PLoS Comput Biol       Date:  2014-03-06       Impact factor: 4.475

9.  LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data.

Authors:  Changlin Wan; Wennan Chang; Yu Zhang; Fenil Shah; Xiaoyu Lu; Yong Zang; Anru Zhang; Sha Cao; Melissa L Fishel; Qin Ma; Chi Zhang
Journal:  Nucleic Acids Res       Date:  2019-10-10       Impact factor: 16.971

10.  Image analysis and empirical modeling of gene and protein expression.

Authors:  Nathanie Trisnadi; Alphan Altinok; Angelike Stathopoulos; Gregory T Reeves
Journal:  Methods       Date:  2012-10-24       Impact factor: 3.608

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