Literature DB >> 9367733

Programming the Drosophila embryo.

J W Bodnar1.   

Abstract

A critical step in understanding the mechanisms of development is in defining the steps at the molecular, cellular, and organismal levels in the developmental program for a given organism-so that given the egg one can predict not only how the embryo will develop but also how that embryo evolved from its ancestors. Using methods employed by chemists and engineers in modeling hierarchical systems, I have integrated current theory and experiment into a calculational method that can model early Drosophila embryogenesis on a personal computer. This quantitative calculation tool is simple enough to be useful for experimentalists in designing experiments yet detailed enough for theoreticians to derive new insights on the evolution of developmental genetic networks. By integrating the strengths of theoretical and experimental methods into a single engineering model that can compute the cascade of genetic networks in a real organism, I provide a new calculational tool that can apply current theory to current experimental data to study the evolution of developmental programs. Copyright 1997 Academic Press Limited.

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Year:  1997        PMID: 9367733     DOI: 10.1006/jtbi.1996.0328

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  7 in total

1.  Travelling and splitting of a wave of hedgehog expression involved in spider-head segmentation.

Authors:  Masaki Kanayama; Yasuko Akiyama-Oda; Osamu Nishimura; Hiroshi Tarui; Kiyokazu Agata; Hiroki Oda
Journal:  Nat Commun       Date:  2011-10-11       Impact factor: 14.919

2.  Inferring regulatory networks from experimental morphological phenotypes: a computational method reverse-engineers planarian regeneration.

Authors:  Daniel Lobo; Michael Levin
Journal:  PLoS Comput Biol       Date:  2015-06-04       Impact factor: 4.475

3.  Indeterminacy of reverse engineering of Gene Regulatory Networks: the curse of gene elasticity.

Authors:  Arun Krishnan; Alessandro Giuliani; Masaru Tomita
Journal:  PLoS One       Date:  2007-06-27       Impact factor: 3.240

4.  Implementing arithmetic and other analytic operations by transcriptional regulation.

Authors:  Sean M Cory; Theodore J Perkins
Journal:  PLoS Comput Biol       Date:  2008-05-09       Impact factor: 4.475

5.  Modeling and visualizing cell type switching.

Authors:  Ahmadreza Ghaffarizadeh; Gregory J Podgorski; Nicholas S Flann
Journal:  Comput Math Methods Med       Date:  2014-04-14       Impact factor: 2.238

6.  Multistable switches and their role in cellular differentiation networks.

Authors:  Ahmadreza Ghaffarizadeh; Nicholas S Flann; Gregory J Podgorski
Journal:  BMC Bioinformatics       Date:  2014-05-28       Impact factor: 3.169

7.  In Silico Analysis of Gene Expression Network Components Underlying Pigmentation Phenotypes in the Python Identified Evolutionarily Conserved Clusters of Transcription Factor Binding Sites.

Authors:  Kristopher J L Irizarry; Randall L Bryden
Journal:  Adv Bioinformatics       Date:  2016-09-06
  7 in total

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