Petri nets: an introduction
The theory of metabolism-repair systems
Applied Mathematics and Computation
On global identifiability for arbitrary model parametrizations
Automatica (Journal of IFAC)
System Modeling in Cellular Biology: From Concepts to Nuts and Bolts
System Modeling in Cellular Biology: From Concepts to Nuts and Bolts
Probabilistic model checking of complex biological pathways
Theoretical Computer Science
Computing chemical organizations in biological networks
Bioinformatics
Switched and PieceWise Nonlinear Hybrid System Identification
HSCC '08 Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control
Information Theoretical Approach to Identification of Hybrid Systems
HSCC '08 Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control
Stochastic dynamics of genetic networks
Bioinformatics
An integrated model of glucose and galactose metabolism regulated by the GAL genetic switch
Computational Biology and Chemistry
Modelling and analysing genetic networks: from boolean networks to petri nets
CMSB'06 Proceedings of the 2006 international conference on Computational Methods in Systems Biology
Qualitative petri net modelling of genetic networks
Transactions on Computational Systems Biology VI
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Understanding how cellular systems build up integrated responses to their dynamically changing environment is one of the open questions in Systems Biology. Despite their intertwinement, signaling networks, gene regulation and metabolism have been frequently modeled independently in the context of well-defined subsystems. For this purpose, several mathematical formalisms have been developed according to the features of each particular network under study. Nonetheless, a deeper understanding of cellular behavior requires the integration of these various systems into a model capable of capturing how they operate as an ensemble. With the recent advances in the "omics” technologies, more data is becoming available and, thus, recent efforts have been driven toward this integrated modeling approach. We herein review and discuss methodological frameworks currently available for modeling and analyzing integrated biological networks, in particular metabolic, gene regulatory and signaling networks. These include network-based methods and Chemical Organization Theory, Flux-Balance Analysis and its extensions, logical discrete modeling, Petri Nets, traditional kinetic modeling, Hybrid Systems and stochastic models. Comparisons are also established regarding data requirements, scalability with network size and computational burden. The methods are illustrated with successful case studies in large-scale genome models and in particular subsystems of various organisms.