Experimental techniques in molecular biology have led to the production of enormous amounts of data on the dynamics of cellular processes. The availability of time series data characterizing genomic, proteomic and metabolic systems must be complemented with formal methods for identifying quantitative models of networks of interactions. Reverse-engineering of regulatory networks is a central issue in modern biology because, beside enabling the computer-based simulation of biological systems, it promotes the understanding of cell functioning and underlies the design of interventions of biotechnological or biomedical relevance. However, standard system identification techniques are unlikely to work out of the box since they must cope with (1) the complexity and the high nonlinearity of biological systems; (2) the quality and type of available biological data; (3) the stochastic nature of chemical interactions; and (4) the interaction of discrete events and continuous dynamics. The aim of this special issue is to present some very recent achievements in system identification tailored to the reconstruction of biological processes.

Call for papers: Special issue on systems identification for biological systems / Ferrari Trecate Giancarlo; Sznaier Mario. - In: INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL. - ISSN 1049-8923. - STAMPA. - 20:7(2010), pp. 842-842.

Call for papers: Special issue on systems identification for biological systems

FERRARI TRECATE, GIANCARLO;
2010

Abstract

Experimental techniques in molecular biology have led to the production of enormous amounts of data on the dynamics of cellular processes. The availability of time series data characterizing genomic, proteomic and metabolic systems must be complemented with formal methods for identifying quantitative models of networks of interactions. Reverse-engineering of regulatory networks is a central issue in modern biology because, beside enabling the computer-based simulation of biological systems, it promotes the understanding of cell functioning and underlies the design of interventions of biotechnological or biomedical relevance. However, standard system identification techniques are unlikely to work out of the box since they must cope with (1) the complexity and the high nonlinearity of biological systems; (2) the quality and type of available biological data; (3) the stochastic nature of chemical interactions; and (4) the interaction of discrete events and continuous dynamics. The aim of this special issue is to present some very recent achievements in system identification tailored to the reconstruction of biological processes.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11571/234492
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