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Most dynamical models for genomic networks are built upon two current methodologies, one process-based and the other based on Boolean-type networks. Both are problematic when it comes to experimental design purposes in the laboratory. The first approach requires a comprehensive knowledge of the parameters involved in all biological processes a priori,(More)
In systems biology, network models are often used to study interactions among cellular components, a salient aim being to develop drugs and therapeutic mechanisms to change the dynamical behavior of the network to avoid undesirable phenotypes. Owing to limited knowledge, model uncertainty is commonplace and network dynamics can be updated in different ways,(More)
In systems biology, network models are often used as a promising tool to study interactions among cellular components (e.g., genes or proteins). However, these models are typically too complex and biological data is very limited which leads to model uncertainty. Network dynamics involves the evolution of entities over time which is central in developing(More)
This paper proposes a new approach to the analysis and design of biological systems. It will be shown that, upon an application of Time-Scale Separation Principle to a nonlinear biochemical system at steady-state, a rational polynomial function relates the chemical characteristics of slow-rate substances. This functional dependency can be determined by a(More)
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