Biology Reference
In-Depth Information
We thus have bottom-up modeling as well, since the model can be built from
data about its material components. Pathway models of processes that are going
on in the system may supplement the top-down cybernetic model. The top-down
systems model and the bottom-up pathway models may be refined and adapted
to each other, usually by iterated steps, so that in the end the model is built by
a blending of both basic strategies. This shows that biological cybernetics and
pathway modeling are not completely isolated (which was to be expected, as
pathway modeling makes use of cybernetic control concepts when referring to,
e.g., feedback regulation). Nevertheless, both strategies are clearly discernible
by the reliance of paradigmatic cases on bottom-up and on top-down strategies,
respectively. These different strategies being the criterion for discerning two
branches of systems biology, they should also suffice to discern different roots
of the field.
Model building in the tradition of biological cybernetics and systems anal-
ysis embraces the following steps: (i) singling out a capacity of the organism
or cell; (ii) experimental input-output analysis of the underlying system, pro-
viding the data that go into modeling; (iii) analysis of the data for filtering,
delay, amplification, etc. that is going on in the black box between input and
output; (iv) analyzing the model analytically or numerically; and (v) assessing
the reliability of the results that the model generates. Steps (ii) and (iii), hence
the models themselves, differ from the case of pathway modeling.
5. THE THIRD ROOT OF SYSTEMS BIOLOGY: 'OMICS'
In the 1990s, large-scale sequencing projects were set up as a new field of
biology, which received funding on a large scale. Meanwhile, the application of
new high-throughput technology yielded the complete sequence of the human
genome. Similar projects to compile comprehensive catalogues of cell contents
are run with proteins, metabolic reactions, etc. In addition to genomic projects,
there are projects in proteomics, metabolomics - in short, a new biological
discipline called 'omics.' All 'omic' projects have their specific methods of data
analysis and presentation, but they also all lack modeling strategies. There does
not seem to be much to explain with respect to large collections of structural
data of the 'omic' kind. Only in the systems biological perspective do 'omic'
data sets become interesting for modeling purposes. But these projects were not
created out of nothing either. Though they rely crucially on newly developed
high-throughput methods for data acquisition, they have forerunners in other
projects that catalogued cellular components. We review some of these early
projects in addition to genomics proper as a basis for our discussion of the
transformation of such projects into systems biology.
