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An information theory approach to homeostasis

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From Cell:

A prevailing view among physiologists is that homeostasis evolves to protect organisms from damaging variation in physiological factors. Here, we propose that homeostasis also evolves to minimize noise in physiological channels. Fluctuations in physiological factors constitute inescapable noise that corrupts the transfer of information through physiological systems. We apply information theory to homeostasis to develop two related ideas. First, homeostatic regulation creates quiet physiological backgrounds for the transmission of all kinds of physiological information. Second, the performance of any homeostatic system influences information processing in other homeostatic systems. This dependence implies that multiple homeostatic systems, embedded within individual organisms, should show strongly nonadditive effects. Paper. (public access) – H. Arthur Woods, J. Keaton Wilson, An information hypothesis for the evolution of homeostasis

Those guys should be cautious about information theory. It isn’t really support for “it all somehow just happens randomly via the magic of natural selection.”

See also: Homeostasis: Life’s balancing act as a challenge to unguided evolution

6 Replies to “An information theory approach to homeostasis

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  2. 2
    Dionisio says:

    Here’s another paper on biology complexity:

    A framework for understanding the characteristics of complexity in biology
    DOI: 10.1186/s40594-016-0047-y

    Full text PDF available.


  3. 3
    Dionisio says:

    A related paper on biological complexity:

    Complexity in biology: Exceeding the limits of reductionism and determinism using complexity theory
    EMBO Reports,
    volume 9,
    issue 1,
    pp 10-14,
    DOI: 10.1038/sj.embor.7401147
    Fulvio Mazzocchi (National Research Council)


  4. 4
    Dionisio says:


    Old paper with gazillion citations by newer papers:


  5. 5
    Dionisio says:

    A January 2018 paper?

    Transcriptomics to Metabolomics: A Network Perspective for Big Data
    Chapter · January 2018?with?54 Reads
    DOI: 10.4018/978-1-5225-2607-0.ch008

    A lot of omics data is generated in a recent decade which flooded the internet with transcriptomic, genomics, proteomics and metabolomics data. A number of software, tools, and web-servers have developed to analyze the big data omics. This review integrates the various methods that have been employed over the years to interpret the gene regulatory and metabolic networks. It illustrates random networks, scale-free networks, small world network, bipartite networks and other topological analysis which fits in biological networks. Transcriptome to metabolome network is of interest because of key enzymes identification and regulatory hub genes prediction. It also provides an insight into the understanding of omics technologies, generation of data and impact of in-silico analysis on the scientific community.



  6. 6
    LocalMinimum says:

    Here, we propose that homeostasis also evolves to minimize noise in physiological channels.

    Noise working diligently to reduce, filter, and control noise. How very helpful!

    In electronics, analogous devices are called band-pass filters, notch filters, or high-or low-pass filters.

    If only engineers could apply Darwin’s wisdom, they wouldn’t have need to come up with such contraptions!

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