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Structural Analysis of Biological Networks

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A Systems Theoretic Approach to Systems and Synthetic Biology I: Models and System Characterizations

Abstract

We introduce the idea of structural analysis of biological network models. In general, mathematical representations of molecular systems are affected by parametric uncertainty: experimental validation of models is always affected by errors and intrinsic variability of biological samples. Using uncertain models for predictions is a delicate task. However, given a plausible representation of a system, it is often possible to reach general analytical conclusions on the system’s admissible dynamic behaviors, regardless of specific parameter values: in other words, we say that certain behaviors are structural for a given model. Here we describe a parameter-free, qualitative modeling framework and we focus on several case studies, showing how many paradigmatic behaviors such as multistationarity or oscillations can have a structural nature. We highlight that classical control theory methods are extremely helpful in investigating structural properties.

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Author information

Authors and Affiliations

  1. Dipartimento di Matematica ed Informatica, Università degli Studi di Udine, Via delle Scienze 206, 33100, Udine, Italy

    Franco Blanchini

  2. Department of Mechanical Engineering, University of California at Riverside, 900 University Avenue, Riverside, CA, 92521, USA

    Elisa Franco

Authors
  1. Franco Blanchini
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  2. Elisa Franco
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Corresponding author

Correspondence to Elisa Franco .

Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota, USA

    Vishwesh V. Kulkarni

  2. Bioengineering, Imperial College, London, United Kingdom

    Guy-Bart Stan

  3. Biotechnology, Indian Institute of Technology Madras, Chennai, India

    Karthik Raman

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Blanchini, F., Franco, E. (2014). Structural Analysis of Biological Networks. In: Kulkarni, V., Stan, GB., Raman, K. (eds) A Systems Theoretic Approach to Systems and Synthetic Biology I: Models and System Characterizations. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9041-3_2

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