Graph-Based Function Perturbation Analysis for Observability of Multivalued Logical Networkss

• Published in: IEEE transaction on neural networks and learning systems Vol. 32; no. 11; pp. 4839 - 4848
• Main Authors: Wang, Shuling, Li, Haitao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algebraic state-space representation (ASSR); function perturbation; Graph theory; Linear systems; MDM2 protein; multivalued logical network (MVLN); Negative feedback; Nonlinear systems; Observability; Perturbation methods; Robustness; transition graph
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2020.3025912

Observability is a fundamental concept for the synthesis of both linear systems and nonlinear systems. This article devotes to discussing the robustness of observability for multivalued logical networks (MVLNs) subject to function perturbation and establishing a graph-based framework. First, based on the transition graph of undistinguishable pairs of states, a new graph-based criterion is presented for the observability of MVLNs. Second, a candidate set consisting of all suspicious undistinguishable pairs of states is defined, based on the cardinality of which and the graph-based condition, a series of effective criteria are proposed for the robustness of observability subject to function perturbations. Finally, the obtained results are applied to the robust observability analysis of the p53-MDM2 negative feedback regulatory loop.