Literature DB >> 16155121

Eukaryotic cells are dynamically ordered or critical but not chaotic.

Ilya Shmulevich1, Stuart A Kauffman, Maximino Aldana.   

Abstract

Two important theoretical approaches have been developed to generically characterize the relationship between the structure and function of large genetic networks: the continuous approach, based on reaction-kinetics differential equations, and the Boolean approach, based on difference equations and discrete logical rules. These two approaches do not always coincide in their predictions for the same system. Nonetheless, both of them predict that the highly nonlinear dynamics exhibited by genetic regulatory systems can be characterized into two broad regimes, to wit, an ordered regime where the system is robust against perturbations, and a chaotic regime where the system is extremely sensitive to perturbations. It has been a plausible and long-standing hypothesis that genomic regulatory networks of real cells operate in the ordered regime or at the border between order and chaos. This hypothesis is indirectly supported by the robustness and stability observed in the phenotypic traits of living organisms under genetic perturbations. However, there has been no systematic study to determine whether the gene-expression patterns of real cells are compatible with the dynamically ordered regimes predicted by theoretical models. Using the Boolean approach, here we show what we believe to be the first direct evidence that the underlying genetic network of HeLa cells appears to operate either in the ordered regime or at the border between order and chaos but does not appear to be chaotic.

Mesh:

Year:  2005        PMID: 16155121      PMCID: PMC1224670          DOI: 10.1073/pnas.0506771102

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  24 in total

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Review 2.  Genomics, gene expression and DNA arrays.

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Review 3.  Shape-dependent control of cell growth, differentiation, and apoptosis: switching between attractors in cell regulatory networks.

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5.  A global view of epistasis.

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6.  Distributed robustness versus redundancy as causes of mutational robustness.

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Journal:  Bioessays       Date:  2005-02       Impact factor: 4.345

7.  Cell fates as high-dimensional attractor states of a complex gene regulatory network.

Authors:  Sui Huang; Gabriel Eichler; Yaneer Bar-Yam; Donald E Ingber
Journal:  Phys Rev Lett       Date:  2005-04-01       Impact factor: 9.161

8.  Genetic control of flower morphogenesis in Arabidopsis thaliana: a logical analysis.

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Journal:  Bioinformatics       Date:  1999 Jul-Aug       Impact factor: 6.937

9.  A gene regulatory network model for cell-fate determination during Arabidopsis thaliana flower development that is robust and recovers experimental gene expression profiles.

Authors:  Carlos Espinosa-Soto; Pablo Padilla-Longoria; Elena R Alvarez-Buylla
Journal:  Plant Cell       Date:  2004-10-14       Impact factor: 11.277

10.  Identification of genes periodically expressed in the human cell cycle and their expression in tumors.

Authors:  Michael L Whitfield; Gavin Sherlock; Alok J Saldanha; John I Murray; Catherine A Ball; Karen E Alexander; John C Matese; Charles M Perou; Myra M Hurt; Patrick O Brown; David Botstein
Journal:  Mol Biol Cell       Date:  2002-06       Impact factor: 4.138

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  47 in total

1.  Microdynamics and criticality of adaptive regulatory networks.

Authors:  Ben D MacArthur; Rubén J Sánchez-García; Avi Ma'ayan
Journal:  Phys Rev Lett       Date:  2010-04-19       Impact factor: 9.161

2.  Gene expression dynamics in the macrophage exhibit criticality.

Authors:  Matti Nykter; Nathan D Price; Maximino Aldana; Stephen A Ramsey; Stuart A Kauffman; Leroy E Hood; Olli Yli-Harja; Ilya Shmulevich
Journal:  Proc Natl Acad Sci U S A       Date:  2008-02-04       Impact factor: 11.205

3.  Inference of Boolean networks using sensitivity regularization.

Authors:  Wenbin Liu; Harri Lähdesmäki; Edward R Dougherty; Ilya Shmulevich
Journal:  EURASIP J Bioinform Syst Biol       Date:  2008

4.  Some features of the spread of epidemics and information on a random graph.

Authors:  Rick Durrett
Journal:  Proc Natl Acad Sci U S A       Date:  2010-02-18       Impact factor: 11.205

5.  Biological Information as Set-Based Complexity.

Authors:  David J Galas; Matti Nykter; Gregory W Carter; Nathan D Price; Ilya Shmulevich
Journal:  IEEE Trans Inf Theory       Date:  2010-02-25       Impact factor: 2.501

Review 6.  Beyond D'Arcy Thompson: Future challenges for quantitative biology.

Authors:  Thomas Gregor
Journal:  Mech Dev       Date:  2017-03-21       Impact factor: 1.882

7.  Transition to chaos in random networks with cell-type-specific connectivity.

Authors:  Johnatan Aljadeff; Merav Stern; Tatyana Sharpee
Journal:  Phys Rev Lett       Date:  2015-02-23       Impact factor: 9.161

8.  From Physics to Pharmacology?

Authors:  Richard J Allen; Timothy C Elston
Journal:  Rep Prog Phys       Date:  2011-01

9.  Information propagation within the Genetic Network of Saccharomyces cerevisiae.

Authors:  Sharif Chowdhury; Jason Lloyd-Price; Olli-Pekka Smolander; Wayne C V Baici; Timothy R Hughes; Olli Yli-Harja; Gordon Chua; Andre S Ribeiro
Journal:  BMC Syst Biol       Date:  2010-10-26

10.  Salivary gland branching morphogenesis: a quantitative systems analysis of the Eda/Edar/NFkappaB paradigm.

Authors:  Michael Melnick; Robert D Phair; Smadar A Lapidot; Tina Jaskoll
Journal:  BMC Dev Biol       Date:  2009-06-06       Impact factor: 1.978

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