[Paleopsych] The Scientist: The Uncertain Future for Central Dogma
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The Uncertain Future for Central Dogma
Volume 19 | Issue 12 | Page 20 | Jun. 20, 2005
Uncertainty serves as a bridge from determinism and reductionism to a new
picture of biology
By Arnold F. Goodman, Cláudia M. Bellato and 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,"^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.^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.^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."^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.^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."^7
NEW CELL MODEL
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.^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,^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
1. PH Silverman "Rethinking genetic determinism," The Scientist
18(10): 32-3. [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. [PubMed Abstract]
4. SJ Lolle et al, "Genome-wide non-mendelian inheritance of
extra-genomic information in Arabidopsis," Nature 434: 505-9.
[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. [PubMed
Abstract][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
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