body-producing cells in immunological memory, so that any subsequent exposure to a similar antigen can lead to a rapid, and thus more effective, immune response (secondary response).

The computational aspects of the immune system, such as distinguishing of self from nonself, feature extraction, learning, memory, self-regulation, and fault tolerance, have been exploited in the design of artificial immune systems that have been successfully used in applications. The applications are varied and include computer virus detection, anomaly detection in a time series of data, fault diagnosis, pattern recognition, machine learning, bioinformatics, optimization, robotics, and control. Recent research in immunology departs from the self-nonself discrimination model to develop what is known as the “danger theory,” wherein it is believed that the immune system differentiates between dangerous and non-dangerous entities, regardless of whether they belong to self or to nonself. These ideas have started to be exploited in artificial immune systems in the context of computer security.

While artificial immune systems (a.k.a. immunological computation, immunocomputing) constitute an example of a computational paradigm inspired by a very specific subsystem of a biological organism, artificial life takes the opposite approach. Artificial life (ALife) attempts to understand the very essence of what it means to be alive by building ab initio, within in silico computers and other “artificial” media, artificial systems that exhibit properties normally associated only with living organisms. ²⁴ Lindenmayer systems (L-systems), introduced in 1968, can be considered as an early example of artificial life.

L-systems are parallel rewriting systems that, starting with an initial word, proceed by applying rewriting rules in parallel to all the letters of the word, and thus generate new words. ³⁴ They have been most famously used to model plant growth and development, ²⁹ but also for modeling the morphology of other organisms.

Building on the ideas of evolutionary computation, other pioneers of artificial life experimented with evolving populations of “artificial creatures” in simulated environments. ⁹ One ex-

while artificial
immune systems
constitute an
example of a
computational
paradigm inspired
by a very specific
subsystem of
a biological
organism, artificial
life attempts to
understand the very
essence of what it
means to be alive.

ample was the design³⁶ of evolving virtual block creatures that were selected for their ability to swim (or walk, or jump), and that competed for a common resource (controlling a cube) in a physically simulated world endowed with realistic features such as kinematics, dynamics, gravity, collisions, and friction. The result was that creatures evolved which would extend arms towards the cube, while others would crawl or roll to reach it, and some even developed legs that they used to walk towards the cube. These ideas were taken one step further²⁵ by combining the computational and experimental approaches, and using rapid manufacturing technology to fabricate physical robots that were materializations of their virtually evolved computational counterparts. In spite of the simplicity of the task at hand (horizontal locomotion), surprisingly different and complex robots evolved: many of them exhibited symmetry, some moved sideways in a crab-like fashion, and others crawled on two evolved limbs. This marked the emergence of mechanical artificial life, while the nascent field of synthetic biology, discussed later, explores a biological implementation of similar ideas. At the same time, the field of Artificial Life continues to explore directions such as artificial chemistry (abstractions of natural molecular processes), as well as traditionally biological phenomena in artificial systems, ranging from computational processes such as co-evolutionary adaptation and development, to physical processes such as growth, self-replica-tion, and self-repair.

Membrane computing investigates computing models abstracted from the structure and the functioning of living cells, as well as from the way the cells are organized in tissues or higher order structures. ²⁶ More specifically, the feature of the living cells that is abstracted by membrane computing is their compartmentalized internal structure effected by membranes. A generic membrane system is essentially a nested hierarchical structure of cell-like compartments or regions, delimited by “membranes.” The entire system is enclosed in an external membrane, called the skin membrane, and everything outside the skin membrane is considered to be the environment.