Biology Reference
In-Depth Information
9
Data without models merging with models
without data
Ulrich Krohs and Werner Callebaut
SUMMARY
Systems biology is largely tributary to genomics and other 'omic' disciplines that
generate vast amounts of structural data. 'Omics', however, lack a theoretical
framework that would allow using these data sets as such (rather than just tiny
bits that are extracted by advanced data-mining techniques) to build explanatory
models that help understand physiological processes. Systems biology provides
such a framework by adding a dynamic dimension to the merely structural
'omics'. It makes use of bottom-up and top-down models. The former are based
on data about systems components, the latter on systems-level data. We trace
back both modeling strategies (which are often used to delineate two branches of
the field) to the modeling of metabolic and signaling pathways in the bottom-up
case and to biological cybernetics and systems theory in the top-down case. We
then argue that three roots of systems biology must be discerned to account ade-
quately for the structure of the field: pathway modeling, biological cybernetics,
and 'omics'. We regard systems biology as merging modeling strategies (supple-
mented by new mathematical procedures) from data-poor fields with data supply
from a field that is quite deficient in explanatory modeling. After characterizing
the structure of the field, we address some epistemological and ontological issues
regarding concepts on which the top-down approach relies and that seem to us to
require clarification. This includes the consequences of identifying modules in
