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The Mexican iGEM team (http://parts2.mit.edu/wiki/index.php/IPN_UNAM_2006) is a recently established group whose main interest is the implementation of algorithms in biological systems. Our goal is to take advantage of the intrinsic features of these systems in order to explore new approaches to certain computations (unconventional computing). We focus on three different frameworks: cellular automata, reaction-diffusion based computations and approaches from game theory. In the near future we plan to develop real-world applications that not only contribute to the understanding of specific problems in biology, computer science and related disciplines, but that also have a positive social impact. We are optimistic about the great benefits that genetically engineered machines might offer, particularly in a country like Mexico. On the other hand we are also conscious of the risks they involve and would like to stimulate a serious discussion about ethical and legal implications as well as the impact they might have on the community. Aims of the projectThe project focuses on the implementation of algorithms in biological systems. For that we have chosen three specific aspects:(1) Computation based on cellular automata (2) Reaction-diffusion implementations (3) Game theory problems and applicationsOur group has previous experience in cellular automata, in particular through the work of G. Martínez who has extensively studied the diffusion life rule 110 [1] (see Fig. 1). Much progress has been made in exploring the possibilities of these systems in unconventional computations. More specifically, how gliders, blinkers and other structures arising in cellular automata can be used in order to implement logical gates and, eventually, more complex algorithms. One of our first goals is to be able to realise these automata in biological systems. From a more abstract point of view, we are also interested in the connection of these systems with formal languages and graph theory.In recent years the implementation of unconventional computational techniques, specifically those based on reaction-diffusion systems have attracted interest for both theoretical and applied reasons (see [2] for example). Several computations have been successfully carried out in real chemical systems and it seems natural to try to extrapolate them to a biological setting.From a theoretical point of view, it is of great importance to understand the role of the architecture of genetic networks that lead to pattern formation. In particular, simple network architectures might account for the emergence of complex patterns. Currently we are working on the implementation of an activator-inhibitor model in the simple setting of a network consisting of two genes (Fig. 2) that has a counterpart in a real system [3]. We investigated whether such systems could produce so-called Turing patterns.Finally, we also considered the possibility of designing genetic circuits coding different strategies in several classical games, such as the dove -hawk or the pris...