requires a fundamental understanding of parallelism, interaction, and causality; the design of complex systems from building blocks, requiring means for composition and encapsulation; and the description of systems at different levels of granularity, requiring methods for abstraction and refinement.

Of course, many of these concepts are not exclusive to reactive systems, but they are critical for understanding them, and they are among the main ideas that render possible the reliable development of truly complex reactivity. The claim made in this article is that these ideas can be of great benefit in the modeling and analysis of biology.

Reactivity and Biology

The process of modeling a piece of biology is very different from that of modeling a human-made system. The motivation is different, the goals are very different, the people involved are also different, the scales are different, and so on. Still the underlying maxim of this article is that a fighter jet and a fruit fly, for example, have many things in common, and that there is much to be gained from using ideas from engineering the former to reverse-engineering the latter, even though the fly is in a way far more complex than the airplane.

Biological systems are ultimately molecular and cellular machines. Using a fixed set of building blocks (molecules) with some fixed functionalities (for example, cleavage, connection, ionization, phosphorylation), cells emerge, and these multilevel machines then utilize various combination techniques to give rise to the life we see around us. The “execution” of a biological system is distributed to a level that is still beyond comprehension. In a sense, a biological system can be viewed, almost on a philosophical level, as a collection of cells (each containing a multitude of molecules) that work in concert and independently to achieve a mutual goal without a central controller that coordinates them all. The cells in such a collection communicate and pass information to each other in order to help achieve their mutual goal. In doing this, they can also affect limited control on their environment.

Let us now concentrate on the pro-
grams (to put it simply, the DNA mol-
ecules) that drive these enormously
distributed processes. In the case of the
fruit fly Drosophila Melanogaster, for ex-
ample, a 165 million base-pair program
defines approximately 13K functions
(proteins). Such programs deal with two
entangled and inseparable tasks: how
to evolve the machine (cell collection)
from a single cell, and how to run the
machine so that it eventually creates
other such machines. At all stages, both
the development and maintenance
are determined by which genes are ex-
pressed and which are not. Effectively,
in each cell, the expressed genes consti-
tute a local control state of the program,
which tells the cell which functions it
should execute and which are irrelevant
to its (current) role. The state is local
because it may be different in different
cells. At all points, communication with
other cells and reaction to the environ-
ment are the keys to determine what
happens next.