Biology Reference
In-Depth Information
Functional Simplifications
This is a more abstract notion that is best illustrated with
a specific example. Feedback control mechanisms
( Figure 15.1 ), which are ubiquitous in living organisms,
provide one of the best examples of functional simplifica-
tion associated with subsystems on a level of organization
encompassing not only material flow but also information
flow for regulatory purposes. These mechanisms regulate
certain variables in the system but also modify the mathe-
matical form of the functional relationships between vari-
ables. Both of these properties can lead to a simplification
of the system under consideration.
Feedback inhibition effectively maintains certain
metabolic pools at relatively fixed concentrations, thereby
reducing these variables of the system to constants for
many practical purposes. Feedback inhibition also can
simplify the effective mathematical complexity of rate
laws. The kinetics of the regulatory enzyme in these
systems is highly non-linear when characterized in vitro.
However, in situ, the negative feedback tends to simplify
the mathematical form of these kinetics. Therefore, the use
of such simplified relationships in place of the original,
highly non-linear ones can be justified, and it greatly
simplifies the description of the functional relationship of
velocity to reactant and modifier concentrations. This is
a well-known property of negative feedback in electronic
circuits [6] . Adding negative feedback around a transistor,
which has a highly non-linear characteristic, is used to
construct operational amplifiers with linear characteristics.
In this way the non-linear characteristics of many techno-
logical components are purposely 'straightened' to the
point of becoming linear in order to facilitate their analysis
and make their behavior more predictable. However, in
biological systems 'we have to deal with the system we are
given, not the one we want' in most cases, although this
may change somewhat as the field of synthetic biology
matures [7] . The non-linear characteristics in biological
systems are typically not 'straightened' all the way to
linear; if they were, life as we know it would not exist.
However, they are very often 'straightened' to power-law
functions,
systems interact with their environment across boundaries
that have a specific material realization (this critically
important point will be the focus of a later section). Fourth,
to the extent that we can identify this modularity and its
environmental context, our analysis of phenotypic function
and system design, and ultimately our understanding of
biological systems, is greatly facilitated.
Modules
Modules or subsystems are physical entities that can be
viewed from two different perspectives: one with a focus on
generating diversity for the evolution of new system func-
tions, the other with a focus on understanding function and
design of operational systems.
Modules as Elements of Random Change
Familiar examples of modules at the level of primary
structure of macromolecules include amino acids in poly-
peptides and nucleotide bases in DNA and RNA. Modules
at a higher level might include common folds and domains
in proteins, and recognition sequences and exchangeable
cassettes in nucleotide sequences. Such examples are often
invoked to explain the advantages of modularity in creating
the combinatorial expansion of diversity available for
evolutionary innovation.
The shuffling of modules is involved at all levels in the
process of mutation: base changes, deletions, insertions,
inversions, duplications, recombinations, frame-shifts,
truncations, and fusions. Most results are deleterious; a few
are advantageous. Modularity clearly facilitates all these
events. However, these are largely accidents that, at least
initially, may not follow any clear rule at the level of the
nascent system. The 'repurposing' of modules in this
context is random, and only during the process of selection
is the emerging system likely to exhibit rule-like behavior.
Once a system has been refined by the sculpting power
of natural selection, one can begin to ask questions
regarding how well it performs its functions, not only in an
absolute sense but also in relation to alternatives that have
evolved to perform similar functions. In this way one
begins to identify design principles that apply to an entire
class of systems.
a
simplification
that
offers
considerable
advantages.
There are four conclusions to be drawn from the argu-
ment in this section. First, systems, as I have defined them,
are naturally modular and pervasive in nature. Indeed, one
might well use the terms system (or subsystem), component
and module interchangeably. Second, systems function
within the context provided by their environment. Third,
Modules as Designed Products of Selection
The focus here is on modularity at a higher level of system
organization and on the second of the perspectives
mentioned in the introduction of this section. Historically,
biologists have always identified modules for study.
Biochemists have long focused on isolated enzymes as
important modules. At the next level of metabolic organi-
zation one thinks of pathways, branch points and cycles as
FIGURE 15.1 Schematic diagram of a biosynthetic pathway with
end-product inhibition.
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