Biology Reference
In-Depth Information
Historically, the relationship between wet-lab analysis and modelling, particularly math-
ematical modelling, has suffered from a gulf of understanding of purpose and practice that
has had the overall effect of hampering progress. A high proportion of bench biologists have
never engaged with modelling, while a high proportion of mathematical modellers publish
in journals read almost exclusively by the mathematical and modelling communities. When
modellers do send work to general biological journals, it is often received so badly by reviewers
that it is rejected at once, and authors retreat to their publishing enclaves. As Editor of a research
journal that spans developmental biology and tissue engineering, I have often seen potentially
interesting modelling papers be castigated by peer reviewers in such strong terms that I have
no choice but to reject them from the journal. Often the problem is one of a misunderstanding of
purpose, but attempts to engage with the authors to ask them to make their purpose clear to
biologists tend to fail
d
usually because they are as unaware of what biologists want as biolo-
gists are unaware of what the modelling community is trying to achieve.
The gulf in understanding may develop in student years: most modern universities have
for many decades allowed or compelled undergraduates to focus on a very narrow area of
science from the very beginning of their studies. Even where some attempt is made to expose
students to other fields, this is often now done in such an applied manner (for example,
'physics for biologists') that students gain little chance to experience the deep underlying
cultures of different communities. A few universities have held out against specialization
and insist on a broad curriculum in which students study quantum mechanics in a way
that will enable them later to specialize in physics, while also studying genetics at a level
that will enable them equally well to specialize in that subject. Unfortunately, pressure
from students to specialize early threatens the long-term future of a truly broad scientific
education. This lack of deep exposure to the culture of other fields means that many scientists
enter research careers with little idea of what other disciplines mean by 'scientific under-
standing'. To a physicist, for example, one of the most important hallmarks of understanding
is the ability to express the behaviour of a system as a precise, predictive mathematical equa-
tion. The encapsulation of electromagnetism in Maxwell's four famous equations, of
quantum mechanics in Schr¨dinger's equation, or of the equivalence of mass and energy
in Einstein's iconic E
mc
2
, are regarded as amongst the most important moments in the
development of physical understanding. This is true even though the equations say nothing
directly about what is going on 'under the hood'
d
about detailed mechanisms. Cell biolo-
gists, on the other hand, would tend not to regard the writing of a simple equation in their
field, even if it were quantitatively predictive of behaviour, as a great step forward unless
it took account of the details of underlying mechanisms. In cell biology, high acclaim is
usually given to broadly narrative explanations, often expressed as pathway diagrams of
cause and effect like the ones in Figure 8.7 in this topic. Most of these are free of quantitative
prediction, but are excellent qualitative indicators of the effects that genetic mutation and
drugs would have on the behaviour of a system. Other biologists, such as ecologists, tend
to value a balance between abstraction and detail that is intermediate between the worlds
of physics and of cell biology.
Given these cultural differences in what constitutes 'understanding', it is perhaps not
surprising that the relationship between modelling and wet-lab biology has often failed to
be as fertile as it should be. Nevertheless, there are very good reasons for biologists to engage
with modelling. The new generations of 'cross-over' scientists, who train as biologists and
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