Metabolomics, machine learning and modelling: towards an understanding
of the language of cells.
D. Kell. Biochem. Soc. Trans.33 (Pt 3):
In answering the question 'Systems Biology--will it work?' (which
it self-evidently has already), it is appropriate to highlight advances
in philosophy, in new technique development and in novel findings.
In terms of philosophy, we see that systems biology involves an iterative
interplay between linked activities--instance, between theory and
experiment, between induction and deduction and between measurements
of parameters and variables--with more emphasis than has perhaps
been common now being focused on the first in each of these pairs.
In technique development, we highlight closed loop machine learning
and its use in the optimization of scientific instrumentation, and
the ability to effect high-quality and quasi-continuous optical images
of cells. This leads to many important and novel findings. In the
first case, these may involve new biomarkers for disease, whereas
in the second case, we have determined that many biological signals
may be frequency-rather than amplitude-encoded. This leads to a very
different view of how signalling 'works' (equations such as that
of Michaelis and Menten which use only amplitudes, i.e. concentrations,
are inadequate descriptors), lays emphasis on the signal processing
network elements that lie 'downstream' of what are traditionally
considered the signals, and allows one simply to understand how cross-talk
may be avoided between pathways which nevertheless use common signalling
elements. The language of cells is much richer than we had supposed,
and we are now well placed to decode it.