Literature DB >> 27660398

Attractor-Based Obstructions to Growth in Homogeneous Cyclic Boolean Automata.

Bilal Khan1, Yuri Cantor2, Kirk Dombrowski1.   

Abstract

We consider a synchronous Boolean organism consisting of N cells arranged in a circle, where each cell initially takes on an independently chosen Boolean value. During the lifetime of the organism, each cell updates its own value by responding to the presence (or absence) of diversity amongst its two neighbours' values. We show that if all cells eventually take a value of 0 (irrespective of their initial values) then the organism necessarily has a cell count that is a power of 2. In addition, the converse is also proved: if the number of cells in the organism is a proper power of 2, then no matter what the initial values of the cells are, eventually all cells take on a value of 0 and then cease to change further. We argue that such an absence of structure in the dynamical properties of the organism implies a lack of adaptiveness, and so is evolutionarily disadvantageous. It follows that as the organism doubles in size (say from m to 2m) it will necessarily encounter an intermediate size that is a proper power of 2, and suffers from low adaptiveness. Finally we show, through computational experiments, that one way an organism can grow to more than twice its size and still avoid passing through intermediate sizes that lack structural dynamics, is for the organism to depart from assumptions of homogeneity at the cellular level.

Entities:  

Keywords:  Attractor-based obstructions; Cyclic boolean automata; Robustness; Synchronous boolean

Year:  2015        PMID: 27660398      PMCID: PMC5028899          DOI: 10.4172/jcsb.1000209

Source DB:  PubMed          Journal:  J Comput Sci Syst Biol        ISSN: 0974-7230


  8 in total

1.  A System for Identifying Genetic Networks from Gene Expression Patterns Produced by Gene Disruptions and Overexpressions.

Authors: 
Journal:  Genome Inform Ser Workshop Genome Inform       Date:  1998

2.  Superpolynomial growth in the number of attractors in Kauffman networks.

Authors:  Björn Samuelsson; Carl Troein
Journal:  Phys Rev Lett       Date:  2003-03-04       Impact factor: 9.161

3.  Scaling in ordered and critical random boolean networks.

Authors:  J E S Socolar; S A Kauffman
Journal:  Phys Rev Lett       Date:  2003-02-13       Impact factor: 9.161

4.  Number and length of attractors in a critical Kauffman model with connectivity one.

Authors:  Barbara Drossel; Tamara Mihaljev; Florian Greil
Journal:  Phys Rev Lett       Date:  2005-03-04       Impact factor: 9.161

5.  Generating Boolean networks with a prescribed attractor structure.

Authors:  Ranadip Pal; Ivan Ivanov; Aniruddha Datta; Michael L Bittner; Edward R Dougherty
Journal:  Bioinformatics       Date:  2005-09-08       Impact factor: 6.937

6.  Number of attractors in random Boolean networks.

Authors:  Barbara Drossel
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2005-07-14

7.  Algorithms for finding small attractors in Boolean networks.

Authors:  Shu-Qin Zhang; Morihiro Hayashida; Tatsuya Akutsu; Wai-Ki Ching; Michael K Ng
Journal:  EURASIP J Bioinform Syst Biol       Date:  2007

8.  On control of singleton attractors in multiple Boolean networks: integer programming-based method.

Authors:  Yushan Qiu; Takeyuki Tamura; Wai-Ki Ching; Tatsuya Akutsu
Journal:  BMC Syst Biol       Date:  2014-01-24
  8 in total

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