Literature DB >> 15384528

Scalar equations for synchronous Boolean networks with biological applications.

Christopher Farrow1, Jack Heidel, John Maloney, Jim Rogers.   

Abstract

One way of coping with the complexity of biological systems is to use the simplest possible models which are able to reproduce at least some nontrivial features of reality. Although two value Boolean models have a long history in technology, it is perhaps a little bit surprising that they can also represent important features of living organizms. In this paper, the scalar equation approach to Boolean network models is further developed and then applied to two interesting biological models. In particular, a linear reduced scalar equation is derived from a more rudimentary nonlinear scalar equation. This simpler, but higher order, two term equation gives immediate information about both cycle and transient structure of the network.

Mesh:

Year:  2004        PMID: 15384528     DOI: 10.1109/TNN.2004.824262

Source DB:  PubMed          Journal:  IEEE Trans Neural Netw        ISSN: 1045-9227


  5 in total

1.  Boolean modeling of neural systems with point-process inputs and outputs. Part I: theory and simulations.

Authors:  Vasilis Z Marmarelis; Theodoros P Zanos; Theodore W Berger
Journal:  Ann Biomed Eng       Date:  2009-06-11       Impact factor: 3.934

2.  Synchronization Analysis of Master-Slave Probabilistic Boolean Networks.

Authors:  Jianquan Lu; Jie Zhong; Lulu Li; Daniel W C Ho; Jinde Cao
Journal:  Sci Rep       Date:  2015-08-28       Impact factor: 4.379

3.  Observability of Boolean multiplex control networks.

Authors:  Yuhu Wu; Jingxue Xu; Xi-Ming Sun; Wei Wang
Journal:  Sci Rep       Date:  2017-04-28       Impact factor: 4.379

4.  An efficient algorithm for computing attractors of synchronous and asynchronous Boolean networks.

Authors:  Desheng Zheng; Guowu Yang; Xiaoyu Li; Zhicai Wang; Feng Liu; Lei He
Journal:  PLoS One       Date:  2013-04-09       Impact factor: 3.240

5.  Dynamics of random Boolean networks under fully asynchronous stochastic update based on linear representation.

Authors:  Chao Luo; Xingyuan Wang
Journal:  PLoS One       Date:  2013-06-13       Impact factor: 3.240

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.