[Paleopsych] The Scientist: The Uncertain Future for Central Dogma

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The Uncertain Future for Central Dogma
    Volume 19 | [2]Issue 12 | Page 20 | Jun. 20, 2005

Uncertainty serves as a bridge from determinism and reductionism to a new
picture of biology

    By [4]Arnold F. Goodman, [5]Cláudia M. Bellato and [6]Lily Khidr
    Kenneth Eward/BioGrafx/Photo Researchers Inc.

    Nearly two decades ago, Paul H. Silverman testified before Congress to
    advocate the Human Genome Project. He later became frustrated when the
    exceptions to genetic determinism, discovered by this project and
    other investigations, were not sufficiently incorporated in current
    research and education.

    In "Rethinking Genetic Determinism,"^[7]1 Silverman questioned one of
    the pillars of molecular genetics and documented the need for
    determinism's expansion into a far more valid and reliable
    representation of reality. He would receive correspondence from all
    over the world that reinforced this vision.

    Silverman firmly believed that we needed a wider-angled model, with a
    new framework and terminology, to display what we know and to guide
    future discovery. He also viewed this model as being a catalyst for
    exploring uncertainty, the vast universe of chance differences on a
    cellular and molecular level that can considerably influence
    organismal variability. Uncertainty not only undermines molecular
    genetics' primary pillars of determinism and reductionism, but also
    provides a bridge to future research.

    Arnold Goodman (left) is an associate director of the Center for
    Statistical Consulting at the University of California, Irvine.
    Cláudia Bellato (center) is an independent researcher at CENA,
    University of São Paulo, Brazil. Lily Khidr (right) is a PhD candidate
    at UC-Irvine. They dedicate this article to the memory of Paul
    Silverman and thank Nancy, his wife, for her assistance.

    Various commentaries detail deviation from determinism within the
    cellular cycle. Here we use the term cellular cycle not in the
    traditional sense, but rather to describe the cyclical program that
    starts with gene regulation through transcription, translation,
    post-processing and back into regulation.

    Richard Strohman at UC-Berkeley describes the program in terms of a
    complex regulatory paradigm, which he calls "dynamic epigenetics." The
    program is dynamic because regulation occurs over time, and epigenetic
    because it is above genetics in level of organization.^[8]2 "We
    thought the program was in the genes, and then in the proteins encoded
    by genes," he wrote, but we need to know the rules governing protein
    networks in a cell, as well as the individual proteins themselves.

    John S. Mattick at the University of Queensland focuses upon the
    hidden genetic program of complex organisms.^[9]3 "RNAs and proteins
    may communicate regulatory information in parallel," he writes. This
    would resemble the advanced information systems for network control in
    our brains and in computers. Indeed, recent demonstrations suggest
    that RNA might serve as a genetic backup copy superseding Mendelian

    Gil Ast of Tel Aviv University writes: "Alternative splicing enables a
    minimal number of genes to produce and maintain highly complex
    organisms by orchestrating when, where, and what types of proteins
    they manufacture."^[11]5 About 5% of alternatively spliced human exons
    contain retrotransposon Alu sequences. These elements represent an
    engine for generating alternative splicing.

    Thus we see a genetic control system regulated by protein products,
    RNAs, and interventions from DNA itself. Yet throughout, the
    consideration of genetic uncertainty as a bridge to cellular behavior
    is conspicuously absent.

    Genetic reductionism, the other pillar of molecular genetics, has many
    challengers. Among them is Stephen S. Rothman at UC-Berkeley, who
    described the limits of reductionism in great detail within his
    comprehensive and well-constructed book.^[12]6

    A more recent publication by Marc H.V. Van Regenmortel at France's
    National Center for Scientific Research updated this assessment by
    discussing not only the deficiencies of reductionism, but also current
    ways of overcoming them. "Biological systems are extremely complex and
    have emergent properties that cannot be explained, or even predicted,
    by studying their individual parts."^[13]7


    Molecular genetics appears to be at a crossroads, since neither
    determinism nor reductionism is capable of accurately representing
    cellular behavior. In order to transition from a passive awareness of
    this dilemma to its active resolution, we must move from simply
    loosening the constraints of determinism and reductionism toward a
    more mature and representative combination of determinism,
    reductionism, and uncertainty.

    To facilitate this expansion, we propose a model for the cellular
    cycle. Although only a framework, it provides a vehicle for broader
    and deeper appreciation of the cell. The figure on page 25 provides a
    novel structure for understanding current knowledge of the cycle's
    biological stages, as well as a guide for acquiring new knowledge that
    may include genetic uncertainty.

    Organismal Regulation: The organism specifies its cellular needs
    (bottom red) for the cell to act upon. It converts the comparison of
    proteins with organismal needs into metabolic agents. The organism
    then defines its cellular needs (top red). It employs metabolic
    effects to alter the extra-cellular matrix and signal other needs.

    Cellular Regulation: Within the bounds of a cell's membrane, cellular
    needs transmission (top blue) directs the cell in various ways,
    including proliferation, differentiation, and programmed cell death.
    It uses such factors as receptors and enzymes to yield molecular
    messengers. In the cell's nucleus, chromatin remodeling (bottom blue)
    then rearranges DNA accessibility by uncoiling supercoiled DNA and
    introducing transcription factors.

    Transcription: Transcription (left green) DNA serves as the template
    for RNAs, both regulatory sequences and pre-messenger RNAs. It
    transcribes polymerases and binding partners into heterogeneous
    nuclear RNAs. Pre-messenger RNAs then undergo highly regulated
    splicing and processing (right green). They turn pre-messenger RNAs
    into mature messenger RNAs.

    Translation: Within the cytoplasm, messenger RNAs and ribosomes
    translate 2D-unfolded proteins (left magenta). Secondary structuring
    and thermodynamic energy (right magenta) then enable physical
    formations that complete the process with folded proteins and

    Postprocessing: Again within the cytoplasm, tertiary structuring and
    modification (top aqua) use assemblers, modifiers and protein subunits
    to supply regulated proteins. Then feedback regulation (bottom aqua)
    produces heritable gene expression from small RNAs, proteins and DNA.
    The proteins and gene expression, rather than being an endpoint, now
    begin the whole process over again by signaling other cells, altering
    and maintaining the genome, and editing RNA transcripts.

    Model for the Cellular Cycle

    Helen M. Blau was a keynote speaker at the recent UC-Irvine stem-cell
    symposium in memory of Paul Silverman and Christopher Reeve.^[17]8 She
    observed: "Where we look and how we look determine what we see."
    Although only a brief prescription, we now propose an approach to the
    exploration for uncertainty that involves both where we look and how
    we look. We examine those cellular-cycle outputs having a relatively
    high likelihood of diversity and its frequent companion, uncertainty.

    As an example of exploring for uncertainty in a cellular cycle,
    consider the following example: Suppose an organismal regulatory
    program for cellular differentiation might alter the signaling milieu
    in the extracellular matrix. The signal is internalized by a cell,
    which might, in turn, alter transcription, produce mature messenger
    RNAs, produce the 3D-folded proteins, and feed back to alter gene
    expression for all daughter cells.

    Now suppose the ECM signaling milieu is altered with a probability p1;
    the signal is internalized by a cell with a probability p2;
    transcription will change with a probability p3; mature mRNAs are
    produced with a probability p4, producing the 3D-folded protein with a
    probability p5 and altering heritable gene expression with a
    probability p6. The probabilities p2, p3, p4, p5, and p6 are all
    conditional on results from the step preceding them, so that the
    resulting probability of altered heritable gene expression is the
    product of all of them. Although this probability may be small, is it
    not preferable to know its form and to later estimate it, than to
    simply ignore its existence?

    When we consider all possible stage alterations, the diversity of
    outputs and complexity of our probability calculations will increase.
    If we also consider all possible interactions, the diversity of
    outputs and complexity of probability calculations will increase quite

    The implications reach far beyond the regulation of a single cell or
    organism. Sean B. Carroll of the University of Wisconsin, Madison,
    summarizes evolutionary developmental biology,^[18]9 invoking Jacques
    Monod's landmark Chance and Necessity, and the Democritus quote upon
    which it is based: "Everything existing in the universe is the fruit
    of chance and necessity."

    Why wouldn't chance also be included in our observations of biology at
    the molecular level? We've proposed a brief overview of the "what" and
    "how" for constructing an uncertainty bridge from genetic determinism
    and reductionism to actual cellular behavior. We hope and believe it
    meets the spirit of Paul Silverman's prescient vision, as well as his
    final wishes.


    1. PH Silverman "Rethinking genetic determinism," The Scientist
    18(10): 32-3. [[19]Full Text]  May 24, 2004
    2. R Strohman "A new paradigm for life: beyond genetic determinism,"
    California Monthly 2001, 111: 4-27.
    3. JS Mattick "The hidden genetic program of complex organisms," Sci
    Am 2004, 291: 60-7. [[20]PubMed Abstract]
    4. SJ Lolle et al, "Genome-wide non-mendelian inheritance of
    extra-genomic information in Arabidopsis," Nature 434: 505-9.
    [[21]Publisher Full Text]  March 24, 2005
    5. G Ast "The alternative genome," Sci Am 2005, 292: 58-65.
    6. SS Rothman Lessons from the Living Cell: The Limits of Reductionism
    New York: McGraw-Hill 2001.
    7. MHV Van Regenmortel "Reductionism and complexity in molecular
    biology," EMBO Reports 2004, 5: 1016-20. [[22]PubMed
    Abstract][[23]Publisher Full Text]
    8. HM Blau "Stem-cell scenarios: adult bone-marrow to brain and
    brawn," Developing Stem-Cell Therapies: A Symposium in Memory of Paul
    H. Silverman and Christopher Reeve University of California,
    Irvine October 20, 2004.
    9. SB Carroll Endless Forms Most Beautiful New York: W.H. Norton


    4. mailto:agoodman at uci.edu
    5. mailto:bellato at cena.usp.br
    6. mailto:lkhidr at uci.edu
   14. http://www.the-scientist.com/content/figures/0890-3670-050620-20-1-l3.jpg
   15. http://www.the-scientist.com/content/figures/0890-3670-050620-20-1-l3.jpg
   16. http://www.the-scientist.com/content/figures/0890-3670-050620-20-1-l3.jpg
   19. http://www.the-scientist.com/2004/05/24/32/1
   20. http://www.biomedcentral.com/pubmed/15487671
   21. http://dx.doi.org/10.1038/nature03380
   22. http://www.biomedcentral.com/pubmed/15520799

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