Biology Reference
In-Depth Information
The systematic genome sequencing programmes did not set out with any
specific hypotheses, save that the provision of such data might be of value (Kell
& Oliver, 2004), and Sulston has stressed the importance of hypothesis-free
measurements at appropriate stages in the growth of a science (Sulston & Ferry,
2002). Equally, the development of technology is also free of specific hypotheses
(again save that their availability would be of scientific value), and it is hard to
imagine working in a modern laboratory without techniques (cloning, sequenc-
ing, PCR, mass spectrometry, etc.) that have only been available for a compara-
tively short time (and many of which secured Nobel prizes for their developers).
Equally, we see that many measurements, especially in postgenomics (Kell
& King, 2000), are designed to be data-driven rather than hypothesis-driven
(hypothesis-dependent). Thus in systems biology, science advances by an itera-
tive and spiralling interplay between deductive and inductive reasoning, with a
substantial amount of technology development also involved.
Our description of the (preferred) development of systems biology as a spiral,
should not be taken to imply that we think of this as unique to systems biology. The
development of many other natural sciences may be and have been described in
similar terms. They can easily be represented as 'the cycle of knowledge' (Fig. 3).
It should also be mentioned that in many presentations of the novelty of
systems biology to audiences of biologists, physicists and chemists, the cycle of
knowledge is presented as something that can now finally be brought into effect
in biology. This has reasons. First, in biology the experimental activities have
become so complex and extensive, and demand such extensive experimental
expertise, that the corresponding scientists have had little opportunity to engage
in the complete cycle of knowledge. Second, molecular cell biology has long
been incomplete in the sense that at any moment an as yet unknown molecule
could turn up and explain experimental phenomena without having implications
of the theories being tested or examined. For instance, when a hypothetical
regulatory effect proposed by a theory is tested by an experiment, an additional,
parallel effect would most often turn up, incapacitating the experimental testing
of the theory. With functional genomics, it has become possible to have a
complete inventory of virtually all relevant molecules, removing this limitation
to the testing of theories. Third, in the case of systems biology, the complexity is
often so great that the experimental and theoretical parts of the cycle cannot be
within the expertise of the same individual. Therewith the cycle of knowledge
is also relevant to indicate the roles various individuals in a project have with
respect to each other.
4.3.2. Systems biology: The top-down/analytic versus the bottom-up/
synthetic strategies
Strategies and methodologies for systems biology come in a number of flavours,
often discriminated as top-down and bottom-up, but also potentially including
