How artificial life relates to theoretical biology

Abstract
There is a long tradition of software simulations in theoretical biology to complement pure analytical mathematics which are often limited to reproduce and understand the self-organisation phenomena resulting from the non-linear and spatially grounded interactions of the huge number of diverse biological objects. Since John Von Neumann and Alan Turing pioneering works on self-replication and morphogenesis, proponents of artificial life have chosen to resolutely neglecting a lot of materialistic and quantitative information deemed not indispensable and have focused on the rule-based mechanisms making life possible, supposedly neutral with respect to their underlying material embodiment. Minimal life begins at the intersection of a series of processes which need to be isolated, differentiated and duplicated as such in computers. Only software developments and running make possible to understand the way these processes are intimately interconnected in order for life to appear at the crossroad. In this paper, I will attempt to set out the history of life as the disciples of artificial life understand it, by placing these different lessons on a temporal and causal axis, showing which one is indispensable to the appearance of the next and how does it connect to the next. These successive stages are the: 1) the appearance of chemical reaction cycles and autocatalytic networks 2) the production by this network of a membrane promoting individuation and catalyzing constitutive reactions 3) the self-replication of this elementary cell 4) the genetic coding and open-evolution by mutation, recombination and selection. I will discuss the task of artificial life as setting up experimental software platforms where these different lessons, whether taken in isolation or together, are tested, simulated, and, more systematically, analysed. I will sketch some of these existing software platforms: chemical reaction networks, Varela's autopoietic cellular automata, Ganti's chemoton model, genetic algorithms, whose running delivers interesting take home messages to open-minded biologists.