Omicsto Whole Organism
This page contains the text of a paper in Energy and Protein Metabolism and Nutrition (ed. I. Ortigues-Marty, N. Miraux & W. Brand-Williams) Wageningen Academic Publishers, Wageningen, pp. 463–472.
Although the rapid advance of the various
omics fields is striking, it
consists mainly of accumulating data in ever-increasing detail at an
ever-increasing rate. There is little basis for believing that this approach is
leading to a deeper understanding of living organisms, and the hope of creating
life de novo remains as remote as it has always been. Changing this will
require a far greater integration of systemic ways of thinking into what is
loosely called systems biology. At the most profound level this means
incorporating a genuine theory of life, but even at a more superficial level the
relatively simple ideas that have come from metabolic control analysis have yet
to be fully incorporated into biotechnological practice.
Proteomics, metabolomics, metabonomics, transcriptomics and so on, together
with systems biology as it is currently understood, are essentially products of
the 21st century, as one can see from the distribution of usages of the various
terms listed in Table 1, which is based on data from PubMed (2007). The
explosive increase in papers about genomics is, of course, a direct consequence
of the vast amount of information that has come from sequencing genomes, and is
a natural development of the subject. The other
omics disciplines are
fruits of the growing amount of genomic information, and studies of the
proteome and transcriptome have already contributed greatly to understanding of
the net of interactions that connect genes to phenotypes (Cornish-Bowden and
Cárdenas, 2001a), and they have been useful tools for early diagnosis of
medical problems. For example, powerful proteomic technologies now have great
potential in cancer research for biomarker discovery, and for addressing the
issue of cancer heterogeneity. Detecting cancer at an early stage, and
predicting how a tumour will develop and how it will respond to therapy, are
areas of research that are already benefitting from proteomics (Celis et
al., 2004; 2006).
Omicsterminology in the biological literature.
|Search term||Total||2000–2007 (%)||First use|
The Table shows information obtained from the PubMed database (PubMed, 2007) as checked in March 2007. The word
genome has been in use at least since 1953, but
genomics as the name of a field of study was invented by the founding editors
of the journal Genomics:
For the newly developing discipline of mapping/sequencing (including analysis of the information) we have adopted the term GENOMICS. We are indebted to T. H. Roderick of the Jackson Laboratory, Bar Harbor, Maine,
for suggesting the term. The new discipline is born from a marriage of molecular and cell biology with classical genetics and is fostered by computational science (McCusick and Ruddle, 1987). Percentages in the third column are approximate, as all of
the totals in the second column change rapidly. The various
omics terms should not be confused with other terms, such as glycosome, spliceosome, trypanosome, etc., that contain the unrelated root -some.
Systems biology, however, is more of a new name for an old approach than a genuinely new way of studying biology. The distinction is important because, as we shall argue in this paper, there is a real need for an integrated approach to biology in which the components of a biological system are analysed in terms of their contributions to the organization of the whole system, but it it is far from clear that that is what systems biology is in its current form. On the contrary, it appears to be just as reductionist as less fashionable areas of biochemistry and molecular biology, differing mainly in being based on an enormously increased body of detailed data available for study.
Expressing the same idea differently, the current obsession with the
accumulation of detailed data is not leading towards a better understanding of
organisms: a theory of biological organization will not appear spontaneously
from beneath a mountain of data, but will need to be actively constructed.
Systems biology in its present form has almost nothing in common with the
general systems theory that Ludwig von Bertalanffy worked to develop, and would
not escape his criticism that
the only goal of science appears to be
analytical, i.e. the splitting up of reality into ever smaller units and the
isolation of individual causal trains (Bertalanffy, 1975). The question
therefore arises of what sort of systemic ideas need to be added to the various
omics fields to enable them to move away from the mere accumulation of
data and towards a real contribution to biological understanding. In a sense
one could hope to move biology away from being a purely descriptive science to
become a predictive science.
Eighty years after Heisenberg’s uncertainty principle taught physicists that behaviour at the particle level cannot be predicted, and 25 years after studies of chaotic dynamics taught them that the long-term behaviour of many-particle ensembles cannot be predicted either, it may seem futile to try to make biology something that even modern physics no longer claims to be, but the implied criticism misses the point. The existence of two areas of physics that are now known to be less amenable to prediction than they were once hoped to be hardly alters the fact that there is a vast body of theory underlying physics that does allow the results of many experiments to be predicted. The theory of biology is far more restricted, being essentially limited to the theory of natural selection. This offers a very convincing mechanism for evolution and makes some predictions about the characteristics of a previously unknown species, but has essentially nothing to say about the origin of life, the moment when organized systems learned how to maintain their organization, in other words how to stay alive.
Biology does of course depend on many fragments of theory, such as the understanding of enzyme mechanisms that comes from studies of organic reaction mechanisms, or, most notably, the whole area of energy management known as bioenergetics, which depends on the laws of thermodynamics. However, none of these can be regarded as theories of biology at the same level as the theory of natural selection, because they apply to non-living systems no less (or more) than they do to living systems. Indeed, a major part of our understanding of biochemistry came from the overthrow of vitalism by Buchner (1897)—the recognition that living systems obey the same laws of chemistry and physics as non-living systems. Volcanic activity depends on the laws of thermodynamics just as much as a living organism does, but one would not call thermodynamics a theory of volcanoes. The distinction we are making here (made, of course, by Schrödinger (1944) before us) is that although no one now doubts that adherence to the laws of physics is necessary for life, it is much less clear that the currently known laws are sufficient.
The question of how far systemic ideas have influenced systems biology as it
is currently understood can be answered at a fairly simple level, in terms of
the ideas of metabolic control analysis developed from the seminal
contributions of Kacser and Burns (1973) and of Heinrich and Rapoport (1974),
or at a much more profound (and difficult) level, in terms of the theory of
biological organization developed by Rosen (1991). At the simpler level this
influence already exists: metabolic control analysis already forms a significant
part of systems biology, though not as large a part as it probably should. At
the more profound level it is probably fair to say that Rosen’s ideas have had
no impact at all on ordinary practice. So far as most biologists are concerned
there is no theory of the whole organism, and for most the lack of one has no
importance, as they would agree with Medawar (1977) that discussing the nature
of life represents
a low level in biological conversation.
Medawar’s comment may have had some validity when he made it, as it could be
argued that in the absence of the
omics technology that we now have there
was little that a theory of life or of the whole organism could have
contributed, but that is no longer true, and to advance significantly further
(other than in the accumulation of yet more detailed information) biology will
need to integrate the information that already exists into a whole. It needs
what Woese (2004) has called a guiding vision, because
without an adequate
technological advance the pathway of progress is blocked, and without an
adequate guiding vision there is no pathway, there is no way ahead.
A pessimistic view would liken systems biology to cybernetics: in the middle of the 20th century this was confidently predicted to offer solutions to all problems of organization and regulation. However, apart from giving biochemists the idea of feedback inhibition, it has largely vanished from biological consciousness, after failing to deliver on its early promise. What, then, ought the guiding vision of systems biology to be? In the deepest sense, Rosen’s (M,R) systems may provide this (Cornish-Bowden et al., 2007), but it will be a long time before these have any practical application, except in the negative sense that recognizing that some current objectives are impossible to realize may avoid some futile effort. In the shorter term, the less profound systemic ideas involved in metabolic control analysis are already applicable to current biotechnology, and may offer easier and better ways to success than the brute-force approach that has dominated the field since genetic manipulation became possible. Analysis of metabolic pathways and networks has a great potential in biotechnology and medicine, and constitutes a powerful tool in drug research (Eisenthal and Cornish-Bowden, 1998; Ramos-Montoya et al., 2006).
Rosen’s theory of (M,R) systems (Rosen, 1991) treats the fundamental
properties of living organisms as metabolism and repair, though replacement
expresses better than repair the intended meaning (Letelier et
al., 2006). It is natural to suppose that
repair includes the
fundamental biological idea of reproduction, but this is not the case, because
Rosen was little concerned with reproduction in the usual sense or with the
other central idea of modern biology, evolution. For most biologists these will
seem to be such crucial omissions that they deprive Rosen’s theory of any
interest it might have. However, the point is that Rosen was interested in life
at a more fundamental level: until the early organisms had succeeded in staying
alive, i.e. in maintaining their organization for a significant period, there
was no question of either reproduction or evolution. It follows, therefore,
that understanding how organisms stay alive is more fundamental than
understanding how they reproduce or evolve.
This essential property that allows an organism to stay alive is metabolic closure, which allows them to preserve their integrity of organization and to be autonomous. We shall discuss later what this implies, but we note at the outset that no machine has any property equivalent to metabolic closure, and the fallacy of the machine metaphor for organisms defeats most attempts to understand them in their entirety. It may have been philosophically tenable for Descartes to hold that organisms are essentially machines, but it is not tenable today. There is a vast gulf between organisms and machines, and at the moment we cannot see how it might be bridged, even in principle, let alone in practice. A sufficiently detailed study of a machine may allow a competent engineer to produce another machine with the same functionality, but we are totally incapable of designing an organism today, and it may even be naive to think that it will ever be possible to design an organism ab initio. Everything that we know about organisms confirms Rosen’s contention that an organism is not a machine.
In January 2007 the popular comic strip Dilbert contained a conversation in
which the question
Your sales representative told us that the product heals
itself. Is that true? received the answer
It’s totally true ... that he
said that. Why was this amusing? It would not have been amusing if the
conversation had been placed at an agricultural fair and the product had been a
disease-resistant breed of pig; it would then have been an uncontroversial
claim, because we all know that animals are capable of recovering from injuries
and illnesses without external intervention. No, it was amusing because the
cartoonist knew that his readers know perfectly well that machines cannot
recover from damage without external help. We know this, but we are tempted to
ignore it in over-optimistic projections of where current biological
engineering will lead.
Philosophically the difference between organisms and machines lies in the different kinds of causation described by Aristotle. Machines and organisms both are open to material causation, as both are constructed from external materials, and both release used materials into their environments. They differ, however, in final causation, because all machines are made for a purpose, to fulfil particular functions, whereas organisms have no final causes. More important, they also differ in efficient causation, because in making a machine the essential decisions about which parts are installed in which locations are external, whereas in an organism the catalysts that decide how the organism is to be constructed are themselves products of the same organism; they are not supplied from outside, and, in Rosen’s words (Rosen, 1991), an organism is closed to efficient causation. The essential idea is illustrated in Figure 1, which represents the enzymes that catalyse the metabolic reactions as being themselves products of the same metabolism. However, there is a serious difficulty with this representation, because synthesis of the enzymes also requires catalysts, which are not shown in Figure 1. The problem is illustrated by the more abstract representation of metabolism and replacement in Fig. 2. This illustrates the problem but does not attempt a solution, as this requires a deeper analysis (Letelier et al., 2006; Cornish-Bowden et al., 2007).
Figure 1. A schematic representation of metabolism and replacement. The conventional idea of metabolism is as a set of chemical reactions, represented here by the steps from S1 to P, catalysed by a series of specific enzymes,
E1 to E4 . However, these enzymes are not supplied from outside and are not indefinitely stable (as represented by the arrows labelled Decay). Accordingly they need to be synthesized (
replaced) by chemical reactions that use
products of metabolism as starting materials.
Figure 2. A m o r e a b s t r a c t r e p r e s e n t a t i o n o f m e t a b o l i s m a n d r e p l a c e m e n t . A l l o f t h e c h e m i c a l r e a c t i o n s o f m e t a b o l i s m a r e r e p r e s e n t e d b y t h e s i n g l e a r r o w f r o m A t o B ; t h u s A ( r e a c t a n t s ) a n d B ( p r o d u c t s ) m u s t b e r e g a r d e d a s s e t s , n o t a s i n d i v i d u a l m e t a b o l i t e s . C a t a l y s i s b y t h e s e t o f e n z y m e s n e e d e d f o r t h e m e t a b o l i s m i s r e p r e s e n t e d b y t h e d a s h e d a r r o w f r o m