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Soft Statistical Mechanics for Biology

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Data Mining Techniques for the Life Sciences

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2449))

Abstract

The multilevel organization of nature is self-evident: proteins do interact among them to give rise to an organized metabolism and the same hierarchical organization is in action for gene expression, tissue and organ architectures, and ecological systems.

The still more common approach to such state of affairs is to think that causally relevant events originate from the lower level in the form of perturbations, that climb up the hierarchy reaching the ultimate layer of macroscopic behavior (e.g., causing a specific disease). Such rigid bottom-up causative model is unable to offer realistic models of many biological phenomena.

Complex network approach allows to uncover the nature of multilevel organization, but in order to operationally define the organization principles of biological systems, we need to go further and complement network approach with sensible measures of order and organization. These measures, while keeping their original physical meaning, must not impose theoretical premises not verifiable in biological frameworks. We will show here how relatively simple and largely hypothesis-free multidimensional statistics tools can satisfactorily meet these criteria.

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Authors and Affiliations

  1. Istituto Superiore di Sanità AND Sapienza University, Environment and Health Department AND Department of Experimental Medicine, Rome, Italy

    Mariano Bizzarri & Alessandro Giuliani

Authors
  1. Mariano Bizzarri
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  2. Alessandro Giuliani
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Corresponding author

Correspondence to Alessandro Giuliani .

Editor information

Editors and Affiliations

  1. University of Pavia, Pavia, Italy

    Oliviero Carugo

  2. Genome Institute and Bioinformatics Institute, Singapore, Singapore

    Frank Eisenhaber

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Bizzarri, M., Giuliani, A. (2022). Soft Statistical Mechanics for Biology. In: Carugo, O., Eisenhaber, F. (eds) Data Mining Techniques for the Life Sciences. Methods in Molecular Biology, vol 2449. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2095-3_11

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  • DOI: https://doi.org/10.1007/978-1-0716-2095-3_11

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2094-6

  • Online ISBN: 978-1-0716-2095-3

  • eBook Packages: Springer Protocols

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