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← Steering and Reprogramming Life

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Showing Revision 3 created 01/15/2019 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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    We examine the effects of that
    perturbation, and evaluate how much the
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    system became more or less random.
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    In a random system, for example,
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    a change will not have a major impact,
    because no part of the system can explain
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    any other part of the system, and so
    the perturbation goes unnoticed.
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    We can thus safely say that these
    systems cannot be reprogrammed.
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    But, in non-random causally generated
    systems,
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    some changes will render them
    unrecognizable,
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    causal interventions in these systems make
    them reprogrammable.
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    Moreover, if we remove an element and the
    system gets further away from randomness,
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    we can conclude that the element is not
    part of the algorithmic content,
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    and is unlikely part of the causal
    generating mechanism of the network.
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    It is thus likely to be noise or part of
    another system.
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    Alternatively, if we remove an element and
    the system moves towards randomness,
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    it means that the element is part of the
    causal model that explains
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    other parts of the system's evolution.
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    But, if we remove an element and the
    system does not approach either
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    randomness nor simplicity, the deleted
    element is non-essential in the
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    explanation of the system and likely an
    element produced by
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    the normal course of its evolution.
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    Returning to gene regulation, what we do
    is to apply this concept
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    and evaluate the contribution of
    every gene and gene interaction to the
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    original network, ranking the elements by
    causal contribution.
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    When this ranking is biologically
    interpreted,
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    these tools have demonstrated the ability
    to pinpoint markers of
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    cell function, cell differentiation, and
    cell fate.
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    This shows how these tools can properly
    profile systems elements, and steer,
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    and reprogram systems, such as biological
    cells.
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    If these systems have some elements that
    move the network towards randomness,
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    and other elements that move the network
    towards simplicity,
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    we say these systems are more
    reprogrammable.
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    But, if a system can only move in one
    direction, then it is less reprogrammable.
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    Stem cells will be able only to move
    towards a single direction
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    towards a differentiated cell,
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    while completely differentiated cells will
    be able only to move
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    to the opposite direction.
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    This causal calculus is better equipped to
    tackle the general challenge of
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    causal discovery and science than more
    traditional tools,
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    and is helping scientists better
    understand, and even steer,
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    biological and synthetic systems.