Monod is an abstract computational model inspired by cellular microbiology. In Monod, a program is not a linear sequence of instructions, but a set of simple programlets which operate on each other and on data according to well-defined rules and stochastic forces, in analogy with proteins and nucleotide sequences in a cell. Monod is also a software implementation of this computational model on standard computer hardware and so provides an accessible software laboratory with which one can run experiments.
Monod should naturally accomodate parallel processing, and fits very nicely in the context of evolutionary algorithms alongside genetic programming, where it offers homologous crossover, among other aspects. The basic principle upon which Monod is premised is that biological cells perform computations. The underlying computational model seems to possess many desirable qualities, like high parallelism, adaptability and tolerance of complexity. These qualities are thoroughly lacking in traditional computational paradigms. Monod offers an opportunity to understand the origin of these qualities, their relationships and perhaps to deduce useful lessons.
The name “Monod” should be pronounced the same as the word “mono”, since the last 'd' is silent. It refers to Jacques Monod, the celebrated, Nobel-prize-winning microbiologist who participated in the discovery of basic cell regulation mechanisms, allostery and messenger RNA, to name a few contributions. With his frequent colleague Fran\cois Jacob, he made many predictions, some dating from before the discovery of the structure of DNA, concerning the operational control of gene expression. Most predictions have been validated over time. He is also the author of a wondrous book about the philosophy of biology, Le Hasard et la Necessité: Essai sur la philosophie naturelle de la biologie moderne (Chance and Necessity: Essay on the natural philosophy of modern biology), published in 1970 [Monod 1970]. In the rest of this chapter, we present an overall introduction to the project, its goals and an overview of the large-scale design. Then we contrast Monod with other existing biologically-inspired computational approaches. We present a quick overview of the results, a history and future prospects. Finally, we answer the question: “What is Monod?”