Biology Reference
In-Depth Information
induction of rules, synthesis of general laws. Brahe, Kepler,
Newton provide the paradigm. On the other hand, there are
some biologists who would answer: 'No, there are no rules!
Anything is possible. There is only what exists to be
discovered and history'. This historical view is part of the
Darwin legacy and, according to some, it has become the
dominant view in modern biology. Webster and Goodwin
[26]
provide a fuller account of these two philosophies in
the context of developmental biology.
In the realm of molecular genetics, leaders in the field
have often expressed the latter view explicitly. To para-
phrase a few: The rich variety of mechanisms governing
gene expression is the result of historical accident; Nature
is a tinkerer who haphazardly draws upon what already
exists, not an engineer seeking optimal performance; The
only rule is that there are no rules; Why are there positive
and negative regulators? God only knows; There is no
design
evolutionary constraints that lead to the emergence of
systems with particular properties. The requirement for
a fast temporal response leads to small pool sizes and
negative feedback controls
[2,27
30]
, and in some cases to
cycles that can respond quickly
[31,32]
. The requirement
for robustness also leads to the emergence of negative
feedback controls at many levels, including metabolism
[2,27]
. Pathways that are physically or kinetically short
favor dynamic stability
[2,27,33]
. The need for a commit-
ment step in differentiation selects for hysteretic all-or-
none switches, but there are many different realizations of
such switches
[12,34,35]
. The need to reduce material and
energy costs implies the emergence of cellular allocation
schemes that prioritize the deployment of cellular resources
according to environmental conditions such as the spec-
trum of available carbon sources. Think catabolite repres-
sion as a mechanism producing diauxic growth in the
presence of mixed carbon sources such as lactose and
glucose
[36]
. In Dobzhansky's well-known words,
'Nothing in biology makes sense except in the light of
evolution.' This is certainly true of accident and design in
biology. Both make use of modularity in evolution, but in
very different ways.
e
what works, works, what does not is dead.
I have presented elsewhere simple rules governing
patterns of gene regulation that suggest how these two
philosophies or views can be reconciled in specific cases
[15]
. A recently acquired function may not agree with some
proposed rule. Such discrepancies can reflect historical
contingencies associated with the origins of a mechanism.
Although such discrepancies may be evident initially, they
are not expected to survive long-term selective pressures
that might enforce a rule. Differences may also be seen in
the detailed molecular mechanisms by which a given type
of system is realized. Such differences might be the result
of historical accidents that are functionally neutral, or they
might be governed by additional rules that have yet to be
determined. One can always assume that certain differences
are the result of historical accident, but such an explanation
has no predictive power and tends to stifle the search for
alternative hypotheses. It generally tends to be more
productive if one starts with the working hypothesis that
there are rules. One may end up attributing differences to
historical accident, but in my opinion it is a mistake to start
there.
Thus, accident and rule both play a role, but generally at
different levels and during different periods. The result is
plasticity in biological organization, but within bounds.
Nature is optimizing, but subject to constraints (as is true of
all optimization when properly understood). Mutation
creates the diversity over which optimization acts; selection
is the optimizer. Both the processes of mutation and
selection are subject to constraints. External constraints are
provided by origins and historical contingencies, whereas
internal constraints are imposed by the laws of physics and
the logic and dynamics of system organization.
More specific examples of such constraints include the
limited time available to achieve some result, limitations on
the amount of material or energy available, robustness in
the face of uncertainty, etc. These are all obvious
e
PHENOTYPES
The second of the goals for this chapter is to provide
a generic definition for what we mean by 'phenotype'. To
put this in a larger context consider the grand challenge of
relating genotype to phenotype
[1]
. It involves the funda-
mental unsolved problem of relating the 'genotype'
e
which has a well-defined, generic, digital representation
e
to the 'phenotype,' which has a poorly defined ad hoc
analog representation. Without a rigorous generic defini-
tion of 'phenotype' to provide the context for a deep
understanding of the relation between genotype and
phenotype we are at a loss to know how many qualitatively
distinct phenotypes are in an organism's repertoire, or the
relative fitness of the phenotypes in different environments.
These are practical challenges for clinicians attempting to
develop therapeutic strategies to treat pathology and
bioengineers wishing to redirect normal cellular functions
for biotechnological purposes.
There are two fundamental problems that need to be
solved in relating genotype to phenotype in a given envi-
ronment: (a) the task of going from genome sequence to
a model of the system, and (b) the task of going from the
model to the phenotypic repertoire of the system in a given
environment. The first task is the ongoing effort of exper-
imental biology, and, whether considered from the bottom-
up approach of molecular biology or the top-down
approach of high-throughput technologies and computa-
tion, the magnitude of the problem is enormous
[37]
.An
innovative approach to this first task is provided by the
