computations. We shall herein address
mostly the second aspect, whereby systems biology aims to understand the
complex interactions in biological systems by using an integrative as opposed
to a reductionist approach. The reductionist approach to biology tries to
identify all the individual components
of functional processes that take place
in an organism, in such a way that the
processes and the interactions between
the components can be understood. In
contrast, systems biology takes a systemic approach in focusing instead on
the interaction networks themselves,
and on the properties of the biological
systems that arise because of these interaction networks. Hence, for example, at the cell level, scientific research
on organic components has focused
strongly on four different interdependent interaction networks, based on
four different “biochemical toolkits:”
nucleic acids (DNA and RNA), proteins,
lipids, carbohydrates, and their building blocks (see Cardelli, 10 whose categorization we follow here).
The genome consists of DNA sequences, some of which are genes that
can be transcribed into messenger
RNA (mRNA), and then translated into
proteins according to the genetic code
that maps 3-letter DNA segments into
amino acids. A protein is a sequence
over the 20-letter alphabet of amino acids. Each gene is associated with other
DNA segments (promoters, enhancers,
or silencers) that act as binding sites
for proteins that activate or repress
the gene’s transcription. Genes interact with each other indirectly, either
through their gene products (mRNA,
proteins), which can act as transcription factors to regulate gene transcription—either as activators or repressors—or through small RNA species
that directly regulate genes.
These gene-gene interactions, together with the genes’ interactions with
other substances in the cell, form the
most basic interaction network of an
organism, the gene regulatory network.
Gene regulatory networks perform
information processing tasks within
the cell, including the assembly and
maintenance of the other networks.
Research into modeling gene regulatory networks includes qualitative
models such as random and probabilistic Boolean networks, asynchronous
as the natural
sciences are rapidly
absorbing ideas
of information
processing, and
the meaning of
computation is
changing as it
embraces concepts
from the natural
sciences, we have
the rare privilege
to take part in
several such
metamorphoses.
automata, and network motifs.
Another point of view, 20 is that the
entire genomic regulatory system can
be thought of as a computational system, the “genomic computer.” Such a
perspective has the potential to yield
insights into both computation as humans historically designed it, and computation as it occurs in nature. There
are both similarities and significant
differences between the genomic computer and an electronic computer. Both
perform computations, the genomic
computer on a much larger scale. However, in a genomic computer, molecular
transport and movement of ions through
electrochemical gradients replace wires,
causal coordination replaces imposed
temporal synchrony, changeable architecture replaces rigid structure, and
communication channels are formed
on an as-needed basis. Both computers have a passive memory, but the genomic computer does not place it in an
a priori dedicated and rigidly defined
place; in addition, the genomic computer has a dynamic memory in which,
for example, trancriptional subcircuits
maintain given regulatory states. In a genomic computer robustness is achieved
by different means, such as by rigorous
selection: non (or poorly)-functional
processes are rapidly degraded by various feedback mechanisms or, at the cell
level, non (or poorly)-functional cells are
rapidly killed by apoptosis, and, at the organism level, non (or poorly)-functional
organisms are rapidly out-competed
by more fit species. Finally, in the case
of a genomic computer, the distinction
between hardware and software breaks
down: the genomic DNA provides both
the hardware and the digital regulatory
code (software).
Proteins and their interactions form
another interaction network in a cell,
that of biochemical networks, which
perform all mechanical and metabolic
tasks inside a cell. Proteins are folded-up strings of amino acids that take
three-dimensional shapes, with possible characteristic interaction sites accessible to other molecules. If the binding of interaction sites is energetically
favorable, two or more proteins may specifically bind to each other to form a
dynamic protein complex by a process
called complexation. A protein complex
may act as a catalyst by bringing together other compounds and facilitating