By Matthias Dehmer, Frank Emmert-Streib

ISBN-10: 0470041447

ISBN-13: 9780470041444

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"Many difficulties from the lifestyles sciences might be solid right into a mathematical framework that's in keeping with a graph theoretic description. as a result an intensive knowing of such equipment is more and more very important for the knowledge research and modeling of biochemical, organic and biomedical problems." "This booklet follows a brand new technique together with graph thought from a mathematical viewpoint through learning purposes ofRead more...

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Szabowski, A. (2008) Gene network dynamics controlling keratinocyte migration. Mol. Syst. , 4, 199. H. (1990) Reverse engineering and design recovery: A taxonomy. , 7, 13–17. -P. (2009) Reverse engineering and veriﬁcation of gene networks: principles, assumptions, and limitations of present methods and future perspectives. J. , 144 (3), 190–203. H. (2001) Exploring complex networks. Nature, 410, 268–276. , Tyers, M. (2004) From large networks to small 27 28 Systems Biology 84 85 86 87 88 89 90 91 92 93 94 95 molecules.

P. (1993) Numerical Recipes in C, 2nd edn, Cambridge University Press, New York. Mendes, P. (1993) Gepasi: a software package for modelling the dynamics, steady states and control of biochemical and other systems. CABIOS, 9 (5), 563–571. , Selkov, E. (1999) Mathematical simulation and Systems Biology 109 110 111 112 113 114 115 116 117 118 119 120 121 analysis of cellular metabolism and regulation. Bioinformatics, 15 (9), 749–758. , Kitano, H. 5: a versatile modeling tool for biochemical networks.

1 Network Biology Network biology is the study of the static organization of life as networks of biological entities such as DNA, proteins, RNA, and metabolites [33, 82]. The relationship between these entities is depicted graphically, the result being a set of nodes connected by edges. Usually, the nodes represent the state variables of the system (such as the molecule concentrations), while the connections deﬁne the interaction between the nodes. The networks are characterized by their connectivity, path length, and clustering distributions.

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