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Mathematical Biosciences and Engineering

2013, Volume 10, Issue 1: 103-120. doi: 10.3934/mbe.2013.10.103
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Parameter space exploration within dynamic simulations of signaling networks

  • 1.
    DIBRIS Department of Informatics, Bioengineering, Robotics and Systems Engineering, Università degli Studi di Genova - Via Balbi, 5 - 16126 Genova
  • 2.
    DIBRIS Department of Informatics, Bioengineering, Robotics and Systems Engineering, Università degli Studi di Genova - Via Balbi, 5 - 16126 Genov
  • 3.
    DiMa - Department of Management, Università Ca' Foscari - Dorsoduro 3246 - 30123 Venezia
  • 4.
    Di.M.I - Department of Internal Medicine, A.O.U. IRCCS San Martino IST
  • Received: 01 April 2012 Accepted: 29 June 2018 Published: 01 December 2012

  • MSC : Primary: 92C42; Secondary: 92C40.

  • We started offering an introduction to very basic aspects of molecular biology, for the reader coming from computer sciences, information technology, mathematics. Similarly we offered a minimum of information about pathways and networks in graph theory, for a reader coming from the bio-medical sector. At the crossover about the two different types of expertise, we offered some definition about Systems Biology. The core of the article deals with a Molecular Interaction Map (MIM), a network of biochemical interactions involved in a small signaling-network sub-region relevant in breast cancer. We explored robustness/sensitivity to random perturbations. It turns out that our MIM is a non-isomorphic directed graph. For non physiological directions of propagation of the signal the network is quite resistant to perturbations. The opposite happens for biologically significant directions of signal propagation. In these cases we can have no signal attenuation, and even signal amplification. Signal propagation along a given pathway is highly unidirectional, with the exception of signal-feedbacks, that again have a specific biological role and significance. In conclusion, even a relatively small network like our present MIM reveals the preponderance of specific biological functions over unspecific isomorphic behaviors. This is perhaps the consequence of hundreds of millions of years of biological evolution.

    Citation: Cristina De Ambrosi, Annalisa Barla, Lorenzo Tortolina, Nicoletta Castagnino, Raffaele Pesenti, Alessandro Verri, Alberto Ballestrero, Franco Patrone, Silvio Parodi. Parameter space exploration within dynamic simulations of signaling networks[J]. Mathematical Biosciences and Engineering, 2013, 10(1): 103-120. doi: 10.3934/mbe.2013.10.103

    Related Papers:

  • We started offering an introduction to very basic aspects of molecular biology, for the reader coming from computer sciences, information technology, mathematics. Similarly we offered a minimum of information about pathways and networks in graph theory, for a reader coming from the bio-medical sector. At the crossover about the two different types of expertise, we offered some definition about Systems Biology. The core of the article deals with a Molecular Interaction Map (MIM), a network of biochemical interactions involved in a small signaling-network sub-region relevant in breast cancer. We explored robustness/sensitivity to random perturbations. It turns out that our MIM is a non-isomorphic directed graph. For non physiological directions of propagation of the signal the network is quite resistant to perturbations. The opposite happens for biologically significant directions of signal propagation. In these cases we can have no signal attenuation, and even signal amplification. Signal propagation along a given pathway is highly unidirectional, with the exception of signal-feedbacks, that again have a specific biological role and significance. In conclusion, even a relatively small network like our present MIM reveals the preponderance of specific biological functions over unspecific isomorphic behaviors. This is perhaps the consequence of hundreds of millions of years of biological evolution.


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  • © 2013 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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