English subtitles

← Steering and Reprogramming Life

Get Embed Code
2 Languages

Showing Revision 3 created 01/15/2019 by Paulo Henrique Ferreira.

  1. Not Synced
    Understanding the causal origins and
    mechanistic principles from the behavior
  2. Not Synced
    of an evolving system is one of the major
    challenges of our time.
  3. Not Synced
    For example, how a protein may fold to
    become a functional piece in your body
  4. Not Synced
    or how a drug may help against
    some disease.
  5. Not Synced
    A causal system can be described by an
    algorithmic model evolving over time.
  6. Not Synced
    The length of the shortest computer model
    is called
  7. Not Synced
    the algorithmic information content of
    the system.
  8. Not Synced
    That is how much computer code is needed
    to reproduce the object itself.
  9. Not Synced
    The shorter the description, the more
    likely the system is causally generated.
  10. Not Synced
    The longer its description, the less
    likely it is to be causally generated.
  11. Not Synced
    A dynamic system can usually be
  12. Not Synced
    as a network of interacting elements,
  13. Not Synced
    such as interacting cells or interacting
    genes turning on and off other genes.
  14. Not Synced
    What we want is to figure out the first
    principles driving a system,
  15. Not Synced
    such as a genetic network representing a
  16. Not Synced
    or the causes to the cell to behave in one
    way or another.
  17. Not Synced
    Power approach consistis in finding a set
    of algorithmic models that can explain
  18. Not Synced
    the structure of the system.
  19. Not Synced
    When the system is just a random process
    and, therefore, not causally generated
  20. Not Synced
    it can only be represented by a long
    descriptive model.
  21. Not Synced
    However, systems rich in causal content
    can be represented by a short model,
  22. Not Synced
    because they have a generating mechanism
    that evolves
  23. Not Synced
    into any observable state of the system.
  24. Not Synced
    To determine the causal content of
    an evolving system,
  25. Not Synced
    we peform perturbations.
  26. Not Synced
    In a network, for example, a perturbation
    can be deleting a node or deleting a link.
  27. Not Synced
    We examine the effects of that
    perturbation, and evaluate how much the
  28. Not Synced
    system became more or less random.
  29. Not Synced
    In a random system, for example,
  30. Not Synced
    a change will not have a major impact,
    because no part of the system can explain
  31. Not Synced
    any other part of the system, and so
    the perturbation goes unnoticed.
  32. Not Synced
    We can thus safely say that these
    systems cannot be reprogrammed.
  33. Not Synced
    But, in non-random causally generated
  34. Not Synced
    some changes will render them
  35. Not Synced
    causal interventions in these systems make
    them reprogrammable.
  36. Not Synced
    Moreover, if we remove an element and the
    system gets further away from randomness,
  37. Not Synced
    we can conclude that the element is not
    part of the algorithmic content,
  38. Not Synced
    and is unlikely part of the causal
    generating mechanism of the network.
  39. Not Synced
    It is thus likely to be noise or part of
    another system.
  40. Not Synced
    Alternatively, if we remove an element and
    the system moves towards randomness,
  41. Not Synced
    it means that the element is part of the
    causal model that explains
  42. Not Synced
    other parts of the system's evolution.
  43. Not Synced
    But, if we remove an element and the
    system does not approach either
  44. Not Synced
    randomness nor simplicity, the deleted
    element is non-essential in the
  45. Not Synced
    explanation of the system and likely an
    element produced by
  46. Not Synced
    the normal course of its evolution.
  47. Not Synced
    Returning to gene regulation, what we do
    is to apply this concept
  48. Not Synced
    and evaluate the contribution of
    every gene and gene interaction to the
  49. Not Synced
    original network, ranking the elements by
    causal contribution.
  50. Not Synced
    When this ranking is biologically
  51. Not Synced
    these tools have demonstrated the ability
    to pinpoint markers of
  52. Not Synced
    cell function, cell differentiation, and
    cell fate.
  53. Not Synced
    This shows how these tools can properly
    profile systems elements, and steer,
  54. Not Synced
    and reprogram systems, such as biological
  55. Not Synced
    If these systems have some elements that
    move the network towards randomness,
  56. Not Synced
    and other elements that move the network
    towards simplicity,
  57. Not Synced
    we say these systems are more
  58. Not Synced
    But, if a system can only move in one
    direction, then it is less reprogrammable.
  59. Not Synced
    Stem cells will be able only to move
    towards a single direction
  60. Not Synced
    towards a differentiated cell,
  61. Not Synced
    while completely differentiated cells will
    be able only to move
  62. Not Synced
    to the opposite direction.
  63. Not Synced
    This causal calculus is better equipped to
    tackle the general challenge of
  64. Not Synced
    causal discovery and science than more
    traditional tools,
  65. Not Synced
    and is helping scientists better
    understand, and even steer,
  66. Not Synced
    biological and synthetic systems.