![spacer](https://webarchive.library.unt.edu/eot2008/20090119135044im_/http://www.ehponline.org/siteimages/transpixel.gif)
| | ![spacer](https://webarchive.library.unt.edu/eot2008/20090119135044im_/http://www.ehponline.org/siteimages/transpixel.gif) | Systems Biology, the Second Time Around Charles DeLisi Boston University
Boston, MA |
|
|
![spacer](https://webarchive.library.unt.edu/eot2008/20090119135044im_/http://www.ehponline.org/siteimages/transpixel.gif) |
When T.S. Eliot (Eliot 1963) wrote “. . . the end
of all our exploring Will be to arrive where we started And know the place
for the first time,” he was probably not thinking explicitly of science,
but as science is deeply imbedded in the human condition, we should not be
surprised that these words ring true.
Systems biology as a quantitative science dates at least
to Hermann von Helmholtz, the 19th century German physicist whose studies of
metabolism led to the first law of thermodynamics. Helmholtz explored human
physiology in its entirety, making fundamental contributions to audition, vision,
the conduction of the nervous impulse and, perhaps most important in so far
as systems biology is concerned, physiologic energy balance. Our understanding
of physiologic systems has of course evolved substantially during the past
150 years, and today sophisticated, if domain-specific, mathematical models
are used to simulate, plan, and interpret experiments in numerous branches
of biomedicine including endocrinology, cardiovascular physiology, immunology,
neurophysiology, and the cognitive sciences. Moreover, with the completion
of the first phase of the visible human project, which provides high-resolution
MR (magnetic resonance) and CT (computed tomography) imaging scans of male
and female anatomies, we can seriously contemplate coupling organ-level models
that integrate anatomical, biophysical, and physiologic data to produce a computer-based
virtual human.
Molecules are not currently the building blocks of useful
organ-level models. Instead, the cell is modeled at low resolution, if not
as a black box. For example, a model of the humoral immune response might
include B-cell trafficking, stimulation by antigen, and regulation by T cells.
The
dynamics of helper and suppressor T cells and their interaction with antigen-presenting
cells could be modeled as a separate subsystem, or module, whose output served
as input to the B-cell module. The response of B cells to antigen would then
be modeled using experimentally determined rate constants for antigen-receptor
interaction to obtain receptor occupancy, and a phenomenologic function determined
experimentally would relate occupancy and T-cell state to antibody secretion
and B-cell proliferation rate.
The levels of depth that would not be modeled explicitly
are apparent. The antigen-receptor rate constants could themselves in
principle be calculated in terms of the detailed atomic-level structures of
the antigen and immunoglobulin receptors, using long- and short-range force
fields determined by quantum chemical calculations and thermodynamic measurements.
Such calculations, even if crystal structures were available and the force
fields were known precisely, would need to take into account conformational
rearrangements in surface side chains, some backbone adaptation, and solvent
restructuring. Such calculations are currently too difficult to perform routinely
with even moderate precision.
Similarly, one could in principle model by any number of
methods--physical chemical, probabilistic, etc.--the signaling
pathways leading from receptor occupancy to gene activation, with all the
various post-translational
modifications and their dependence on the state of the cell, terminating
in the modulation of sets of genes combinatorially regulated by sets of transcription
factors. But the information required is currently far too sketchy for detailed
cell-based models to be useful inputs to organ- and tissue-level models.
The
advantages of including such deeper-level models explicitly would be a)
the connection they may provide between the (dynamic) state of the cell’s
surface and the gene-protein-metabolite network topology in the
interior of the cell, thus providing an entrée to a global-integrated
model; b) their ability to integrate cell physiology with cell anatomy--just
as a virtual human would integrate anatomy and physiology at the organ level;
and c) the foundation they would provide for deep design; that is, for
rational molecular manipulations aimed at production of prespecified phenotypes.
Although historical and global perspectives remind us that
we are not in an entirely new place, profound changes have occurred in recent
years--changes that are driving a fundamental shift in the culture and
content of the life sciences. One such change is, of course, genomic decoding--work
that has only just begun. The next 5-10 years will see the production
of complete lists of parts of eukaryotic cells, and the next 15-20 years
will see reasonably complete wiring diagrams. But--a worn analogy not
withstanding--understanding a cell from its list of parts is far more
complex than understanding a Boeing 747 airplane or many other complex systems.
The cell is not hard wired, therefore a “wiring diagram” only provides,
after much analysis, a combinatorially rich repertoire of circuit modules,
particular subsets of which are selected by particular environments. And because
a cell’s environment is in fugue, the problem of systems biology is understanding
the rules of subset selection, and connecting recurrent functional modules
to phenotype.
There are many ways to carry out such a program at various
levels of spatial and temporal resolution. The level selected depends on experimental
or clinical goals. But regardless of the approach used, connecting the genomic
revolution and a biology that would understand the cell as a hierarchical system
of environmentally selected functional modules is a long-term program. Along
the way, as our understanding deepens and as our models attain broader phenomenologic
coverage, we can expect to attain a greatly accelerated understanding of evolutionary
and developmental biology and greater precision in identifying drug targets
and individualizing therapies.
While genomics--and I use the word canonically--does
not in itself enable a cell systems biology, it is providing the tools and
data that embolden us to begin thinking and working seriously toward that goal.
But it is doing much more. It has married the two most powerful technologies
of the 20th century--computer science and molecular biology. Computer
science is the sine qua non for postgenomic biology, and the dexterity
with which its leaders have responded to the biological challenge is one of
the great stories in the sociology of science. Nevertheless, the fundamental
cultural challenge remains with the biology community itself . The pace of
progress will continue to be rate limited by the ability of our universities
to educate a new generation of biologists. Not an easy task for organizations
that--for some good and some not so good reasons--remain instinctively
conservative, even as they sow the seeds of revolution.
Charles DeLisi
Bioinformatics Program
Boston University
Boston, MA
Chares DeLisi is Arthur Metcalf Professor of Science
and Engineering and chair of the All-University Doctoral Program in Bioinformatics
at Boston University. From 1990-2000 he was dean of the College of
Engineering. Before moving to Boston he was professor and chair of Biomathematical
Sciences at the Mount Sinai School of Medicine (1987-1990), director
of the Department of Energy’s Health and Environmental Research Programs
(1985-1987), and chief of Theoretical Immunology and Mathematical
Biology at the National Institutes of Health. He has authored or co-authored
more
than 200 research papers in biophysical chemistry, genomics, and immunology
and is the recipient of numerous awards including the Presidential Citizens
Medal from President Clinton for his role in initiating the Human Genome
Project.
|
|
![spacer](https://webarchive.library.unt.edu/eot2008/20090119135044im_/http://www.ehponline.org/siteimages/transpixel.gif) |
|
![spacer](https://webarchive.library.unt.edu/eot2008/20090119135044im_/http://www.ehponline.org/siteimages/transpixel.gif) |
|
| |