On convergence for hybrid models of gene regulatory networks under polytopic uncertainties: a Lyapunov approach
Hybrid models of genetic regulatory networks allow for a simpler analysis with respect to fully detailed quantitative models, still maintaining the main dynamical features of interest. In this paper we consider a piecewise affine model of a genetic regulatory network, in which the parameters describ...
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Published in | Journal of mathematical biology Vol. 83; no. 6-7; p. 64 |
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Main Authors | , |
Format | Journal Article |
Language | English |
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01.12.2021
Springer Nature B.V |
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Abstract | Hybrid models of genetic regulatory networks allow for a simpler analysis with respect to fully detailed quantitative models, still maintaining the main dynamical features of interest. In this paper we consider a piecewise affine model of a genetic regulatory network, in which the parameters describing the production function are affected by polytopic uncertainties. In the first part of the paper, after recalling how the problem of finding a Lyapunov function is solved in the nominal case, we present the considered polytopic uncertain system and then, after describing how to deal with sliding mode solutions, we prove a result of existence of a parameter dependent Lyapunov function subject to the solution of a feasibility linear matrix inequalities problem. In the second part of the paper, based on the previously described Lyapunov function, we are able to determine a set of domains where the system is guaranteed to converge, with the exception of a zero measure set of times, independently from the uncertainty realization. Finally a three nodes network example shows the validity of the results. |
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AbstractList | Hybrid models of genetic regulatory networks allow for a simpler analysis with respect to fully detailed quantitative models, still maintaining the main dynamical features of interest. In this paper we consider a piecewise affine model of a genetic regulatory network, in which the parameters describing the production function are affected by polytopic uncertainties. In the first part of the paper, after recalling how the problem of finding a Lyapunov function is solved in the nominal case, we present the considered polytopic uncertain system and then, after describing how to deal with sliding mode solutions, we prove a result of existence of a parameter dependent Lyapunov function subject to the solution of a feasibility linear matrix inequalities problem. In the second part of the paper, based on the previously described Lyapunov function, we are able to determine a set of domains where the system is guaranteed to converge, with the exception of a zero measure set of times, independently from the uncertainty realization. Finally a three nodes network example shows the validity of the results. Hybrid models of genetic regulatory networks allow for a simpler analysis with respect to fully detailed quantitative models, still maintaining the main dynamical features of interest. In this paper we consider a piecewise affine model of a genetic regulatory network, in which the parameters describing the production function are affected by polytopic uncertainties. In the first part of the paper, after recalling how the problem of finding a Lyapunov function is solved in the nominal case, we present the considered polytopic uncertain system and then, after describing how to deal with sliding mode solutions, we prove a result of existence of a parameter dependent Lyapunov function subject to the solution of a feasibility linear matrix inequalities problem. In the second part of the paper, based on the previously described Lyapunov function, we are able to determine a set of domains where the system is guaranteed to converge, with the exception of a zero measure set of times, independently from the uncertainty realization. Finally a three nodes network example shows the validity of the results.Hybrid models of genetic regulatory networks allow for a simpler analysis with respect to fully detailed quantitative models, still maintaining the main dynamical features of interest. In this paper we consider a piecewise affine model of a genetic regulatory network, in which the parameters describing the production function are affected by polytopic uncertainties. In the first part of the paper, after recalling how the problem of finding a Lyapunov function is solved in the nominal case, we present the considered polytopic uncertain system and then, after describing how to deal with sliding mode solutions, we prove a result of existence of a parameter dependent Lyapunov function subject to the solution of a feasibility linear matrix inequalities problem. In the second part of the paper, based on the previously described Lyapunov function, we are able to determine a set of domains where the system is guaranteed to converge, with the exception of a zero measure set of times, independently from the uncertainty realization. Finally a three nodes network example shows the validity of the results. |
ArticleNumber | 64 |
Author | Pasquini, Mirko Angeli, David |
Author_xml | – sequence: 1 givenname: Mirko orcidid: 0000-0002-7856-4899 surname: Pasquini fullname: Pasquini, Mirko email: mirko.pasquini16@imperial.ac.uk, pasqu@kth.se organization: Department of Electrical and Electronic Engineering, Imperial College of London, Division of Decision and Control, KTH Royal Institute of Technology, Competence Centre for Advanced Bioproduction by Continuous Processing, AdBIOPRO – sequence: 2 givenname: David surname: Angeli fullname: Angeli, David organization: Department of Electrical and Electronic Engineering, Imperial College of London, Department of Information Engineering, University of Florence |
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Issue | 6-7 |
Keywords | Gene regulatory networks 92C42 Systems biology Linear matrix inequalities 93D30 Lyapunov methods Convergence analysis Polytopic uncertainties |
Language | English |
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SubjectTerms | Applications of Mathematics Approximation Biology Chemical reactions Control theory Convergence Convergence analysis Engineering Gene Regulatory Networks Genes Liapunov functions Linear matrix inequalities Lyapunov methods Mathematical and Computational Biology Mathematical models Mathematics Mathematics and Statistics Parameters Polytopic uncertainties Systems biology Systems stability Transcription factors Uncertainty |
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Title | On convergence for hybrid models of gene regulatory networks under polytopic uncertainties: a Lyapunov approach |
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