There are many other biologically-inspired computational approaches besides Monod. Not surprisingly, our understanding of biology has increased in parallel with our ability to create more powerful computational machines. There have been many interactions in both directions between these two fields.
“As a result of these bilateral interactions between computing and biology, it is possible to identify three different approaches, namely biologically motivated computing, computationally motivated biology and computing with biological mechanisms. [...] [In the first approach] Biology provides sources of models and inspiration for the development of computational systems (e.g., ANN [Artificial Neural Networks] and EC [Evolutionary Computing]). In the second approach, computing provides models and inspiration for biology (e.g., ALife and CA [Cellular Automata]). The last approach involves the use of information processing capabilities of biological systems to replace, or at least supplement, the current silicon-based computers (e.g., Quantum and DNA computing).” [de Castro and Timmis 2002, p. 3].Monod falls in the first approach named above.
In this section, we quickly contrast Monod with three other biologically-motivated computational approaches (genetic programming, artificial neural networks and artificial immune systems) and one approach which involves computing with biological mechanisms (molecular computing). Beyond being biologically-motivated in one sense or another, all of the approaches cited here also share with Monod a basic concern with the the triad of attributes mentioned in Goals: natural parallelizability, tolerance of complexity and adaptability. Some of these approaches have met with great success and are already an important part of the most advanced computational techniques available (artificial neural networks and evolutionary algorithms).