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light on the situation. The models incorporated into the endeavor of systems
biology are certainly based on sound empirical data. However, model building
in this tradition has, for most of the time, either suffered from a relative paucity
of data or was not able to integrate even available data for technical reasons.
Although there is less consensus regarding the roots of systems biology than
with respect to the classification of systems biological models, the table which
O'Malley and Dupré (2005) have compiled from several sources nevertheless
offers a clear picture. Different theories of systems dynamics (among them
cybernetics and systems theory) are mentioned as belonging to the root of
top-down systems biology, whereas classical molecular biology, being a basis
of modeling of metabolic pathways, and genomics are univocally regarded as
forming the root of bottom-up systems biology. Thus, authors agree that two roots
gave rise to the two respective classes of systems biological models and disagree
only on details concerning which forerunner approaches exactly constitute these
roots. We challenge the 'two-roots' view and want to propose three roots instead
because we believe that only a delineation that is fine-grained enough allows
us to adequately reconstruct the structure of the field. Consequently, our main
focus will be on those aspects of different forerunner models that are retained in
systems biology and on the interrelationship between the two classes of models.
Each of the three roots we discern contributes to both classes of systems
biological models, and we intend to show that this is a more adequate recon-
struction of the field than an account in terms of two roots giving rise to two
classes of models in a one-to-one manner. These three roots are (i) modeling
of metabolic and signaling pathways, (ii) biological cybernetics and systems
analysis, and (iii) genomics as well as other 'omics' projects such as proteomics
(the analysis of the proteins expressed in an organism or in a cell during its life
cycle), transcriptomics (the cell-scale study of the genes transcribed and of the
RNA transcripts), and metabolomics (the study of small-molecule metabolite
profiles on the cellular level). A first reason to view 'omics' as a third root
rather than classifying it together with pathway modeling relates to the richness
or poverty in structural and other data of the different roots and branches of
systems biology. Data on the structure of a biological system, mainly on its dif-
ferent components, is what is meant when models are classified as data-rich or
data-poor. Richness in structural data is found neither in pathway modeling nor
in biological cybernetics, which both focus on a few parameters per system only;
it stems exclusively from 'omics', which thus contrasts with both of the other
roots (Table 1). Note that the latter, however, are rich in kinetic data (though not
in the number of independent kinetic parameters). In pathway modeling, these
are data on the dynamics of the components of the system, whereas in biologi-
cal cybernetics mainly data on system-level dynamics are used. Similarly, rich
kinetic data can be found in the models of the two branches of systems biology,
but here the divide is less strict with respect to all three kinds of data.
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