Using coarse-grained, discrete systems for data-driven inference of regulatory gene networks : Perspectives and limitations for reverse engineering

Using coarse-grained, discrete systems for data-driven inference of regulatory gene networks : Perspectives and limitations for reverse engineering

[1]  S A Kauffman,et al.  Developmental logic and its evolution. , 1987, BioEssays : news and reviews in molecular, cellular and developmental biology.

[2]  Roland Somogyi,et al.  Modeling the complexity of genetic networks: Understanding multigenic and pleiotropic regulation , 1996, Complex..

[3]  Dirk Repsilber,et al.  Reverse engineering of regulatory networks: simulation studies on a genetic algorithm approach for ranking hypotheses. , 2002, Bio Systems.

[4]  M. Kearsey,et al.  QTL analysis in plants; where are we now? , 1998, Heredity.

[5]  E. D. Weinberger,et al.  The NK model of rugged fitness landscapes and its application to maturation of the immune response. , 1989, Journal of theoretical biology.

[6]  Satoru Miyano,et al.  Inferring qualitative relations in genetic networks and metabolic pathways , 2000, Bioinform..

[7]  Patrik D'haeseleer,et al.  Genetic network inference: from co-expression clustering to reverse engineering , 2000, Bioinform..

[8]  Jan T. Kim transsys: A Generic Formalism for Modelling Regulatory Networks in Morphogenesis , 2001, ECAL.

[9]  M Wahde,et al.  Coarse-grained reverse engineering of genetic regulatory networks. , 2000, Bio Systems.

[10]  Stewart W. Wilson The Genetic Algorithm and Simulated Evolution , 1987, ALIFE.

[11]  Jan T. Kim LindEvol: Artificial Models for Natural Plant Evolution , 2000, Künstliche Intell..

[12]  J. Josse,et al.  Quantitative trait loci underlying gene product variation: a novel perspective for analyzing regulation of genome expression. , 1994, Genetics.