Definition
Linear additive network is composed of two components: (1) A directed and loops-allowed graph. The nodes represent genes or other molecules such as transcriptional factors (TFs) that are involved in the regulatory process and are connected by directed edges with weight matrix W whose elements denote the connective strengths. A positive weight W ij means that node i is activated by j, while a negative weight W ij means that i is inhibited by j. A zero weight W ij implies no interaction. Each gene responds based upon its received weighted signals indirectly through activation function. (2) The second component is the activation function f(x) = x. In other words, the regulatory interactions in this model are supposed to be linear.
Once the topology of the network is determined, it can be used to capture the dynamics of the considered gene regulatory systems.
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D’haeseleer P, Wen X, Fuhrman S, Somogyi R (1999) Linear modeling of mRNA expression levels during CNS development and injury. In: Proceedings of the Pacific Symposium on Biocomputing, Hawaii, 4:41–52
Wessels LF, Van Someren EP, Reinders MJ (2001) A comparison of genetic network models. In: Proceedings of the Pacific Symposium on Biocomputing, Hawaii, pp 508–519
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Zhang, ZY. (2013). Linear Additive Network Model. In: Dubitzky, W., Wolkenhauer, O., Cho, KH., Yokota, H. (eds) Encyclopedia of Systems Biology. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9863-7_721
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DOI: https://doi.org/10.1007/978-1-4419-9863-7_721
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