Data without models merging with models without data
In Fred C. Boogerd, Frank J. Bruggeman, Jan-Hendrik S. Hofmeyr & Hans V. Westerhoff (eds.), Systems Biology: Philosophical Foundations. Elsevier (2007)
|Abstract||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 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 adequately for the structure of the field: pathway modeling, biological cybernetics, and “omics”. We regard systems biology as merging modeling strategies (supplemented 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 large networks without relying on functional considerations, the question of the “holism” of systems biology, and the epistemic value of the “systeome” project that aspires to become the cutting edge of the field.|
|Keywords||Systems biology Genomics Proteomics|
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