Metabolomics, modelling and machine learning in systems biology – towards an understanding of the languages of cells Delivered on 3 July 2005 at the 30th FEBS Congress and 9th IUBMB conference in Budapest

• Published in: The FEBS journal Vol. 273; no. 5; pp. 873 - 894
• Main Author: Kell, Douglas B.
• Format: Journal Article
• Language: English
• Published: 01.03.2006
• ISSN: 1742-464X; 1742-4658
• DOI: 10.1111/j.1742-4658.2006.05136.x

The newly emerging field of systems biology involves a judicious interplay between high‐throughput ‘wet’ experimentation, computational modelling and technology development, coupled to the world of ideas and theory. This interplay involves iterative cycles, such that systems biology is not at all confined to hypothesis‐dependent studies, with intelligent, principled, hypothesis‐generating studies being of high importance and consequently very far from aimless fishing expeditions. I seek to illustrate each of these facets. Novel technology development in metabolomics can increase substantially the dynamic range and number of metabolites that one can detect, and these can be exploited as disease markers and in the consequent and principled generation of hypotheses that are consistent with the data and achieve this in a value‐free manner. Much of classical biochemistry and signalling pathway analysis has concentrated on the analyses of changes in the concentrations of intermediates, with ‘local’ equations − such as that of Michaelis and Menten v =({{V}} max ·{{S}})/({{S}}+{{K}} m ) − that describe individual steps being based solely on the instantaneous values of these concentrations. Recent work using single cells (that are not subject to the intellectually unsupportable averaging of the variable displayed by heterogeneous cells possessing nonlinear kinetics) has led to the recognition that some protein signalling pathways may encode their signals not (just) as concentrations (AM or amplitude‐modulated in a radio analogy) but via changes in the dynamics of those concentrations (the signals are FM or frequency‐modulated). This contributes in principle to a straightforward solution of the crosstalk problem, leads to a profound reassessment of how to understand the downstream effects of dynamic changes in the concentrations of elements in these pathways, and stresses the role of signal processing (and not merely the intermediates) in biological signalling. It is this signal processing that lies at the heart of understanding the languages of cells. The resolution of many of the modern and postgenomic problems of biochemistry requires the development of a myriad of new technologies (and maybe a new culture), and thus regular input from the physical sciences, engineering, mathematics and computer science. One solution, that we are adopting in the Manchester Interdisciplinary Biocentre ( http://www.mib.ac.uk/ ) and the Manchester Centre for Integrative Systems Biology ( http://www.mcisb.org/ ), is thus to colocate individuals with the necessary combinations of skills. Novel disciplines that require such an integrative approach continue to emerge. These include fields such as chemical genomics, synthetic biology, distributed computational environments for biological data and modelling, single cell diagnostics/bionanotechnology, and computational linguistics/text mining.