📖 Read pages 60-66 of 272 of The Demon in the Machine by Paul Davies

📖 Read pages 60-66 of 251 of The Demon in the Machine: How Hidden Webs of Information Are Finally Solving the Mystery of Life by Paul Davies

So far there’s nothing new for me here. He’s encapsulating a lot of prior books I’ve read. (Though he’s doing an incredible job of it.) There are a handful of references that I’ll want to go take a look at though.

2 thoughts on “📖 Read pages 60-66 of 272 of The Demon in the Machine by Paul Davies”

  1. Bookmarked Statistical Physics of Self-Replication by Jeremy L. England (J. Chem. Phys. 139, 121923 (2013); )

    Self-replication is a capacity common to every species of living thing, and simple physical intuition dictates that such a process must invariably be fueled by the production of entropy. Here, we undertake to make this intuition rigorous and quantitative by deriving a lower bound for the amount of heat that is produced during a process of self-replication in a system coupled to a thermal bath. We find that the minimum value for the physically allowed rate of heat production is determined by the growth rate, internal entropy, and durability of the replicator, and we discuss the implications of this finding for bacterial cell division, as well as for the pre-biotic emergence of self-replicating nucleic acids.

    Syndicated copy also available on arXiv: https://arxiv.org/abs/1209.1179
    Hat tip to Paul Davies in The Demon in the Machine

  2. Bookmarked From bit to it: How a complex metabolic network transforms information into living matter by Andreas Wagner (BMC Systems Biology)

    Organisms live and die by the amount of information they acquire about their environment. The systems analysis of complex metabolic networks allows us to ask how such information translates into fitness. A metabolic network transforms nutrients into biomass. The better it uses information on available nutrient availability, the faster it will allow a cell to divide.
    I here use metabolic flux balance analysis to show that the accuracy I (in bits) with which a yeast cell can sense a limiting nutrient’s availability relates logarithmically to fitness as indicated by biomass yield and cell division rate. For microbes like yeast, natural selection can resolve fitness differences of genetic variants smaller than 10-6, meaning that cells would need to estimate nutrient concentrations to very high accuracy (greater than 22 bits) to ensure optimal growth. I argue that such accuracies are not achievable in practice. Natural selection may thus face fundamental limitations in maximizing the information processing capacity of cells.
    The analysis of metabolic networks opens a door to understanding cellular biology from a quantitative, information-theoretic perspective.
    Received: 01 March 2007 Accepted: 30 July 2007 Published: 30 July 2007

    Hat tip to Paul Davies in The Demon in the Machine

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