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