Identification of Boolean control networks

In this paper the identification of Boolean control networks is addressed. First, necessary and sufficient conditions are obtained for the identification of state equation from input-state data. Then a necessary and sufficient condition for a controllable Boolean network to be observable is presented. Based on these two results, a necessary and sufficient condition for the identification from input-output data is achieved. To practically identify the model, a numerical algorithm is proposed. Two particular cases: (i) identification of systems with a known network graph; (ii) identification of a higher order Boolean network, are also investigated. Finally, the approximate identification for large size networks is explored.

[1]  Aniruddha Datta,et al.  Optimal infinite horizon control for probabilistic Boolean networks , 2006, 2006 American Control Conference.

[2]  M. Aldana Boolean dynamics of networks with scale-free topology , 2003 .

[3]  Aniruddha Datta,et al.  External control in Markovian genetic regulatory networks: the imperfect information case , 2004, Bioinform..

[4]  H. Othmer,et al.  The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in Drosophila melanogaster. , 2003, Journal of theoretical biology.

[5]  D. di Bernardo,et al.  How to infer gene networks from expression profiles , 2007, Molecular systems biology.

[6]  M. Ng,et al.  Control of Boolean networks: hardness results and algorithms for tree structured networks. , 2007, Journal of theoretical biology.

[7]  Sui Huang,et al.  Regulation of Cellular States in Mammalian Cells from a Genomewide View , 2002, Gene Regulations and Metabolism.

[8]  Daizhan Cheng,et al.  Input-state incidence matrix of Boolean control networks and its applications , 2010, Syst. Control. Lett..

[9]  D. Bernardo,et al.  A Yeast Synthetic Network for In Vivo Assessment of Reverse-Engineering and Modeling Approaches , 2009, Cell.

[10]  Daizhan Cheng,et al.  A Linear Representation of Dynamics of Boolean Networks , 2010, IEEE Transactions on Automatic Control.

[11]  Daizhan Cheng,et al.  Controllability and observability of Boolean control networks , 2009, Autom..

[12]  B. Drossel,et al.  Number and length of attractors in a critical Kauffman model with connectivity one. , 2004, Physical review letters.

[13]  Daizhan Cheng,et al.  Realization of Boolean control networks , 2010, Autom..

[14]  Stuart A. Kauffman,et al.  The origins of order , 1993 .

[15]  S. Huang,et al.  Shape-dependent control of cell growth, differentiation, and apoptosis: switching between attractors in cell regulatory networks. , 2000, Experimental cell research.

[16]  Daizhan Cheng,et al.  Input-State Approach to Boolean Networks , 2009, IEEE Transactions on Neural Networks.

[17]  Sangsoo Kim,et al.  An efficient top-down search algorithm for learning Boolean networks of gene expression , 2006, Machine Learning.

[18]  Satoru Miyano,et al.  Algorithms for Identifying Boolean Networks and Related Biological Networks Based on Matrix Multiplication and Fingerprint Function , 2000, J. Comput. Biol..

[19]  S. Kauffman Metabolic stability and epigenesis in randomly constructed genetic nets. , 1969, Journal of theoretical biology.

[20]  John Archibald Wheeler,et al.  At Home in the Universe , 1994 .

[21]  Daizhan Cheng,et al.  Model Construction of Boolean Network via Observed Data , 2011, IEEE Transactions on Neural Networks.

[22]  S Fuhrman,et al.  Reveal, a general reverse engineering algorithm for inference of genetic network architectures. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[23]  ndtedited by Julio Collado-Vides and Ralf Hofest Gene regulation and metabolism , 2002 .