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← Steering and Reprogramming Life

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Showing Revision 1 created 01/14/2019 by Paulo Henrique Ferreira.

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    Understanding the causal origins and
    mechanistic principles from the behavior
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    of an evolving system is one of the major
    challenges of our time.
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    For example, how a protein may fold to
    become a functional piece in your body
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    or how a drug may help against
    some disease.
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    A causal system can be described by an
    algorithmic model evolving over time.
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    The length of the shortest computer model
    is called
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    the algorithmic information content of
    the system.
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    That is how much computer code is needed
    to reproduce the object itself.
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    The shorter the description, the more
    likely the system is causally generated.
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    The longer its description, the less
    likely it is to be causally generated.
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    A dynamic system can usually be
    represented
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    as a network of interacting elements
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    such as interacting cells or interacting
    genes turning on and off other genes.
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    What we want is to figure out the first
    principles driving a system
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    such as a genetic network representing a
    cell
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    or the causes to the cell to behave in one
    way or another.
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    Power approach consistis in finding a set
    of algorithmic models that can explain
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    the structure of the system.
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    When the system is just a random process
    and therefore not causally generated
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    it can only be represented by a long
    descriptive model.
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    However, systems rich in causal content
    can be represented by a short model
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    because they have a generating mechanism
    that evolves
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    into any observable state of the system.
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    To determine the causal content of
    an evolving system
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    we peform perturbations.
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    In a network, for example, a perturbation
    can be deleting a node or deleting a link.
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